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@article{Ramachandran1991,
abstract = {LETTERS TO NATURE FIG. 1 a, A longitudinal section showing carpels and stamens of the per- mineralized Eocene flower. A, anther in which the smut sorus has replaced the pollen. B, bract. C, carpel. Magnification, x42. b. Scanning electron microscopy of an individual, ...},
author = {Ramachandran, V S},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature 1991 Ramachandran.pdf:pdf},
journal = {Nature},
month = {jan},
title = {{Perceptual filling in of artificially induced scotomas in human vision}},
year = {1991}
}
@article{Priebe2006,
abstract = {We recorded the responses of direction-selective simple and complex cells in the primary visual cortex (V1) of anesthetized, paralyzed macaque monkeys. When studied with sine-wave gratings, almost all simple cells in V1 had responses that were separable for spatial and temporal frequency: the preferred temporal frequency did not change and preferred speed decreased as a function of the spatial frequency of the grating. As in previous recordings from the middle temporal visual area (MT), approximately one-quarter of V1 complex cells had separable responses to spatial and temporal frequency, and one-quarter were "speed tuned" in the sense that preferred speed did not change as a function of spatial frequency. Half fell between these two extremes. Reducing the contrast of the gratings caused the population of V1 complex cells to become more separable in their tuning for spatial and temporal frequency. Contrast dependence is explained by the contrast gain of the neurons, which was relatively higher for gratings that were either both of high or both of low temporal and spatial frequency. For stimuli that comprised two spatially superimposed sine-wave gratings, the preferred speeds and tuning bandwidths of V1 neurons could be predicted from the sum of the responses to the component gratings presented alone, unlike neurons in MT that showed nonlinear interactions. We conclude that spatiotemporal modulation of contrast gain creates speed tuning from separable inputs in V1 complex cells. Speed tuning in MT could be primarily inherited from V1, but processing that occurs after V1 and possibly within MT computes selective combinations of speed-tuned signals of special relevance for downstream perceptual and motor mechanisms.},
address = {Department of Physiology, Howard Hughes Medical Institute, W. M. Keck Foundation Center for Integrative Neuroscience, University of California, San Francisco, California 94143, USA. nico@northwestern.edu},
author = {Priebe, Nicholas J and Lisberger, Stephen G and Movshon, J Anthony},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2006 Priebe.pdf:pdf},
journal = {J. Neurosci.},
month = {mar},
number = {11},
pages = {2941--2950},
title = {{Tuning for spatiotemporal frequency and speed in directionally selective neurons of macaque striate cortex.}},
volume = {26},
year = {2006}
}
@article{Teramae2012,
author = {Teramae, Jun-nosuke and Tsubo, Yasuhiro and Fukai, Tomoki},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Sci. Rep. 2012 Teramae.pdf:pdf},
journal = {Sci. Rep.},
month = {jul},
title = {{Optimal spike-based communication in excitable networks with strong-sparse and weak-dense links}},
volume = {2},
year = {2012}
}
@article{Naselaris2009,
author = {Naselaris, Thomas and Prenger, Ryan J and Kay, Kendrick N and Oliver, Michael and Gallant, Jack L},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neuron 2009 Naselaris.pdf:pdf},
journal = {Neuron},
month = {sep},
number = {6},
pages = {902--915},
title = {{Bayesian Reconstruction of Natural Images from Human Brain Activity}},
volume = {63},
year = {2009}
}
@article{Freiwald2014,
author = {Freiwald, W A and Tsao, D Y},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proceedings of the National Academy of Sciences 2014 Freiwald.pdf:pdf},
journal = {Proc. Natl. Acad. Sci.},
month = {jun},
number = {22},
pages = {7894--7895},
title = {{Neurons that keep a straight face}},
volume = {111},
year = {2014}
}
@article{Nakayama1992,
author = {Nakayama, Ken and Shimojo, Shinsuke},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Science 1992 Nakayama.pdf:pdf},
journal = {Science},
month = {jan},
number = {5075},
pages = {1357--1363},
title = {{Experiencing and perceiving visual surfaces}},
volume = {257},
year = {1992}
}
@article{Havrylov2017,
abstract = {Learning to communicate through interaction, rather than relying on explicit supervision, is often considered a prerequisite for developing a general AI. We study a setting where two agents engage in playing a referential game and, from scratch, develop a communication protocol necessary to succeed in this game. Unlike previous work, we require that messages they exchange, both at train and test time, are in the form of a language (i.e. sequences of discrete symbols). We compare a reinforcement learning approach and one using a differentiable relaxation (straight-through Gumbel-softmax estimator (Jang et al., 2017)) and observe that the latter is much faster to converge and it results in more effective protocols. Interestingly, we also observe that the protocol we induce by optimizing the communication success exhibits a degree of compositionality and variability (i.e. the same information can be phrased in different ways), both properties characteristic of natural languages. As the ultimate goal is to ensure that communication is accomplished in natural language, we also perform experiments where we inject prior information about natural language into our model and study properties of the resulting protocol.},
archivePrefix = {arXiv},
arxivId = {1705.11192},
author = {Havrylov, Serhii and Titov, Ivan},
eprint = {1705.11192},
file = {:Users/hahosoya/Documents/Mendeley Desktop/6709e3b3b860ddda5f79b30a0bb6080c6b747816.pdf:pdf},
journal = {Adv. Neural Inf. Process. Syst.},
pages = {2150--2160},
title = {{Emergence of language with multi-agent games: Learning to communicate with sequences of symbols}},
volume = {2017-Decem},
year = {2017}
}
@article{Haefner2016,
abstract = {Neuron, 90 (2016) 649-660. doi:10.1016/j.neuron.2016.03.020},
author = {Haefner, Ralf M and Berkes, Pietro and Fiser, J{\'{o}}zsef},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neuron 2016 Haefner.pdf:pdf},
journal = {Neuron},
month = {may},
number = {3},
pages = {649--660},
title = {{Perceptual Decision-Making as Probabilistic Inference by Neural Sampling}},
volume = {90},
year = {2016}
}
@article{Lucke2007,
author = {L{\"{u}}cke, J and Sahani, M},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Network Computation in Neural Systems 2007 L{\"{u}}cke.pdf:pdf},
journal = {Artif. Neural Networks–ICANN 2007},
month = {jan},
pages = {657--667},
title = {{Generalized softmax networks for non-linear component extraction}},
year = {2007}
}
@article{VandenOord2014,
abstract = {Abstract Generative models can be seen as the swiss army knives of machine learning, as many problems can be written probabilistically in terms of the distribution of the data, including prediction, reconstruction, imputation and simulation. One of the most promising directions for unsupervised learning may lie in Deep Learning methods, given their success in supervised learning. However, one of the current problems with deep unsupervised ...
},
author = {{Van den Oord}, A and Schrauwen, B},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Advances in neural information processing systems 2014 Van den Oord.pdf:pdf},
journal = {Adv. Neural Inf. Process. Syst.},
month = {jan},
title = {{Factoring variations in natural images with deep gaussian mixture models}},
year = {2014}
}
@article{Olshausen1996,
author = {Olshausen, B A and Field, David J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature 1996 Olshausen.pdf:pdf},
journal = {Nature},
month = {jan},
number = {6583},
pages = {607--609},
title = {{Emergence of simple-cell receptive field properties by learning a sparse code for natural images}},
volume = {381},
year = {1996}
}
@article{Burgess2019,
abstract = {The ability to decompose scenes in terms of abstract building blocks is crucial for general intelligence. Where those basic building blocks share meaningful properties, interactions and other regularities across scenes, such decompositions can simplify reasoning and facilitate imagination of novel scenarios. In particular, representing perceptual observations in terms of entities should improve data efficiency and transfer performance on a wide range of tasks. Thus we need models capable of discovering useful decompositions of scenes by identifying units with such regularities and representing them in a common format. To address this problem, we have developed the Multi-Object Network (MONet). In this model, a VAE is trained end-to-end together with a recurrent attention network -- in a purely unsupervised manner -- to provide attention masks around, and reconstructions of, regions of images. We show that this model is capable of learning to decompose and represent challenging 3D scenes into semantically meaningful components, such as objects and background elements.},
archivePrefix = {arXiv},
arxivId = {1901.11390},
author = {Burgess, Christopher P. and Matthey, Loic and Watters, Nicholas and Kabra, Rishabh and Higgins, Irina and Botvinick, Matt and Lerchner, Alexander},
eprint = {1901.11390},
file = {:Users/hahosoya/Documents/Mendeley Desktop/d25b1edda507cba944938ec8784d8b124c2381a5.pdf:pdf},
pages = {1--22},
title = {{MONet: Unsupervised Scene Decomposition and Representation}},
year = {2019}
}
@article{Tanaka2009,
author = {Tanaka, H and Ohzawa, I},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neurophysiology 2009 Tanaka.pdf:pdf},
journal = {J. Neurophysiol.},
month = {feb},
number = {3},
pages = {1444--1462},
title = {{Surround Suppression of V1 Neurons Mediates Orientation-Based Representation of High-Order Visual Features}},
volume = {101},
year = {2009}
}
@article{Yoshimura2005a,
abstract = {The specificity of cortical neuron connections creates columns of functionally similar neurons spanning from the pia to the white matter1–6. Here we investigate whether there is an additional, finer level of specificity that creates subnetworks of excitatory neurons within functional columns. We tested for fine-scale specificity of connections to cortical layer 2/3 pyramidal neurons in rat visual cortex by using cross-correlation analyses of synaptic currents evoked by photostimulation. Recording simultaneously from adjacent layer 2/3 pyramidal cells, we find that when they are connected to each other (20% of all recorded pairs) they share common input from layer 4 and within layer 2/3. When adjacent layer 2/3 neurons are not connected to each other, they share very little (if any) common excitatory input from layers 4 and 2/3. In contrast, all layer 2/3 neurons share common excitatory input from layer 5 and inhibitory input from layers 2/3 and 4, regard- less of whether they are connected to each other. Thus, excitatory connections from layer 4 to layer 2/3 and within layer 2/3 form fine-scale assemblies of selectively interconnected neurons; inhibitory connections and excitatory connections from layer 5 link neurons across these fine-scale subnetworks. Relatively independent subnetworks of excitatory neurons are therefore embedded within the larger-scale functional architecture; this allows neighbouring neurons to convey information more inde- pendently than suggested by previous descriptions of cortical circuitry.},
author = {Yoshimura, Y and Dantzker, J L M},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature 2005 Yoshimura.pdf:pdf},
journal = {Nature},
month = {jan},
title = {{Excitatory cortical neurons form fine-scale functional networks}},
year = {2005}
}
@article{Olshausen1993,
abstract = {We present a biologically plausible model of an attentional mechanism for forming position- and scale-invariant representations of objects in the visual world. The model relies on a set of control neurons to dynamically modify the synaptic strengths of intracortical connections so that information from a windowed region of primary visual cortex (V1) is selectively routed to higher cortical areas. Local spatial relationships (i.e., topography) within the attentional window are preserved as information is routed through the cortex. This enables attended objects to be represented in higher cortical areas within an object-centered reference frame that is position and scale invariant. We hypothesize that the pulvinar may provide the control signals for routing information through the cortex. The dynamics of the control neurons are governed by simple differential equations that could be realized by neurobiologically plausible circuits. In preattentive mode, the control neurons receive their input from a low-level "saliency map" representing potentially interesting regions of a scene. During the pattern recognition phase, control neurons are driven by the interaction between top-down (memory) and bottom-up (retinal input) sources. The model respects key neurophysiological, neuroanatomical, and psychophysical data relating to attention, and it makes a variety of experimentally testable predictions.},
address = {Computation and Neural Systems Program, California Institute of Technology, Pasadena 91125.},
author = {Olshausen, B A and Anderson, C H and {Van Essen}, D C},
file = {:Users/hahosoya/Documents/Mendeley Desktop/The Journal of neuroscience 1993 Olshausen.pdf:pdf},
journal = {J. Neurosci.},
month = {nov},
number = {11},
pages = {4700--4719},
title = {{A neurobiological model of visual attention and invariant pattern recognition based on dynamic routing of information.}},
volume = {13},
year = {1993}
}
@article{Benna2021,
abstract = {The observation of place cells has suggested that the hippocampus plays a special role in encoding spatial information. However, place cell responses are modulated by several nonspatial variables and reported to be rather unstable. Here, we propose a memory model of the hippocampus that provides an interpretation of place cells consistent with these observations. We hypothesize that the hippocampus is a memory device that takes advantage of the correlations between sensory experiences to generate compressed representations of the episodes that are stored in memory. A simple neural network model that can efficiently compress information naturally produces place cells that are similar to those observed in experiments. It predicts that the activity of these cells is variable and that the fluctuations of the place fields encode information about the recent history of sensory experiences. Place cells may simply be a consequence of a memory compression process implemented in the hippocampus.},
author = {Benna, Marcus K and Fusi, Stefano},
file = {:Users/hahosoya/Documents/Mendeley Desktop/e2018422118.full.pdf:pdf},
journal = {Proc. Natl. Acad. Sci.},
month = {dec},
number = {51},
pages = {e2018422118},
title = {{Place cells may simply be memory cells: Memory compression leads to spatial tuning and history dependence}},
volume = {118},
year = {2021}
}
@article{Read2002a,
author = {Read, J C A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Biological Cybernetics 2002 Read.pdf:pdf},
journal = {Biol. Cybern.},
month = {feb},
number = {2},
pages = {117--136},
title = {{A Bayesian model of stereopsis depth and motion direction discrimination}},
volume = {86},
year = {2002}
}
@article{Szegedy2014,
author = {Szegedy, Christian},
file = {:Users/hahosoya/Documents/Mendeley Desktop/ICLR 2014 Szegedy.pdf:pdf},
journal = {ICLR},
month = {feb},
pages = {1--10},
title = {{Intriguing properties of neural networks}},
year = {2014}
}
@article{Zhou2000,
abstract = {Areas V1 and V2 of the visual cortex have traditionally been conceived as stages of local feature representations. We investigated whether neural responses carry information about how local features belong to objects. Single-cell activity was recorded in areas V1, V2, and V4 of awake behaving monkeys. Displays were used in which the same local feature (contrast edge or line) could be presented as part of different figures. For example, the same light-dark edge could be the left side of a dark square or the right side of a light square. Each display was also presented with reversed contrast. We found significant modulation of responses as a function of the side of the figure in >50% of neurons of V2 and V4 and in 18% of neurons of the top layers of V1. Thus, besides the local contrast border information, neurons were found to encode the side to which the border belongs ("border ownership coding"). A majority of these neurons coded border ownership and the local polarity of luminance-chromaticity contrast. The others were insensitive to contrast polarity. Another 20% of the neurons of V2 and V4, and 48% of top layer V1, coded local contrast polarity, but not border ownership. The border ownership-related response differences emerged soon (<25 msec) after the response onset. In V2 and V4, the differences were found to be nearly independent of figure size up to the limit set by the size of our display (21 degrees ). Displays that differed only far outside the conventional receptive field could produce markedly different responses. When tested with more complex displays in which figure-ground cues were varied, some neurons produced invariant border ownership signals, others failed to signal border ownership for some of the displays, but neurons that reversed signals were rare. The influence of visual stimulation far from the receptive field center indicates mechanisms of global context integration. The short latencies and incomplete cue invariance suggest that the border-ownership effect is generated within the visual cortex rather than projected down from higher levels.},
address = {Krieger Mind/Brain Institute, Johns Hopkins University, Baltimore, Maryland 21218, USA.},
author = {Zhou, H and Friedman, H S and von der Heydt, R},
file = {:Users/hahosoya/Documents/Mendeley Desktop/The Journal of neuroscience 2000 Zhou.pdf:pdf},
journal = {J. Neurosci.},
month = {sep},
number = {17},
pages = {6594--6611},
title = {{Coding of border ownership in monkey visual cortex.}},
volume = {20},
year = {2000}
}
@article{Deng2010,
author = {Deng, Jia and Berg, Alexander C and Li, Kai and Fei-Fei, Li},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Computer Vision–ECCV 2010 2010 Deng.pdf:pdf},
journal = {Comput. Vision–ECCV 2010},
month = {jan},
pages = {71--84},
title = {{What does classifying more than 10,000 image categories tell us?}},
year = {2010}
}
@article{Kurikawa2013,
author = {Kurikawa, Tomoki and Kaneko, Kunihiko},
editor = {Behrens, Tim},
file = {:Users/hahosoya/Documents/Mendeley Desktop/PLoS Comp Biol 2013 Kurikawa.pdf:pdf},
journal = {PLoS Comput. Biol.},
month = {mar},
number = {3},
pages = {e1002943},
title = {{Embedding Responses in Spontaneous Neural Activity Shaped through Sequential Learning}},
volume = {9},
year = {2013}
}
@article{Berkes2010,
abstract = {Human perception has recently been characterized as statistical inference based on noisy and ambiguous sensory inputs. Moreover, suitable neural representations of uncertainty have been identified that could underlie such probabilistic computations. In this review, we argue that ...},
author = {Berkes, P and Orban, G and Lengyel, M},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2010 Berkes.pdf:pdf},
journal = {Trends Cogn. Sci.},
month = {jan},
title = {{Statistically optimal perception and learning: from behavior to neural representations}},
year = {2010}
}
@article{Hinton2006a,
abstract = {High-dimensional data can be converted to low-dimensional codes by training a multilayer neural network with a small central layer to reconstruct high-dimensional input vectors. Gradient descent can be used for fine-tuning the weights in such "autoencoder" networks, but this works well only if the initial weights are close to a good solution. We describe an effective way of initializing the weights that allows deep autoencoder networks to learn low-dimensional codes that work much better than principal components analysis as a tool to reduce the dimensionality of data.},
address = {Department of Computer Science, University of Toronto, 6 King's College Road, Toronto, Ontario M5S 3G4, Canada. hinton@cs.toronto.edu},
author = {Hinton, G E and Salakhutdinov, R R},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Science 2006 Hinton.pdf:pdf},
journal = {Science},
month = {jul},
number = {5786},
pages = {504--507},
title = {{Reducing the dimensionality of data with neural networks.}},
volume = {313},
year = {2006}
}
@article{Xu2019,
abstract = {Neural networks have successfully been applied to solving reasoning tasks, ranging from learning simple concepts like "close to", to intricate questions whose reasoning procedures resemble algorithms. Empirically, not all network structures work equally well for reasoning. For example, Graph Neural Networks have achieved impressive empirical results, while the less structured neural networks may fail to learn to reason. Theoretically, there is currently limited understanding of the interplay between reasoning tasks and network learning. In this paper, we develop a framework to characterize which tasks a neural network can learn well, by studying how well its structure aligns with the algorithmic structure of the relevant reasoning procedure. This suggests that Graph Neural Networks can learn dynamic programming, a powerful algorithmic strategy that solves a broad class of reasoning problems, such as relational question answering, sorting, intuitive physics, and shortest paths. Our perspective also implies strategies to design neural architectures for complex reasoning. On several abstract reasoning tasks, we see empirically that our theory aligns well with practice.},
archivePrefix = {arXiv},
arxivId = {1905.13211},
author = {Xu, Keyulu and Li, Jingling and Zhang, Mozhi and Du, Simon S. and Kawarabayashi, Ken Ichi and Jegelka, Stefanie},
eprint = {1905.13211},
file = {:Users/hahosoya/Documents/Mendeley Desktop/7ba0db53033b73d270242ced3a021ed9491de2d0.pdf:pdf},
journal = {arXiv},
pages = {1--18},
title = {{What can neural networks reason about?}},
year = {2019}
}
@article{Brockwell2006,
abstract = {In recent years, parallel processing has become widely available to researchers. It can be applied in an obvious way in the context of Monte Carlo simulation , but techniques for “parallelizing” Markov chain Monte Carlo (MCMC) algorithms are not so obvious, apart  ...},
author = {Brockwell, A E},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Computational and Graphical Statistics 2006 Brockwell.pdf:pdf},
journal = {J. Comput. Graph. Stat.},
month = {jan},
title = {{Parallel Markov chain Monte Carlo simulation by pre-fetching}},
year = {2006}
}
@article{Salman2018,
abstract = {This review examines the relevance of parameter identifiability for statistical models used in machine learning. In addition to defining main concepts, we address several issues of identifiability closely related to machine learning, showing the advantages and disadvantages of state-of-the-art research and demonstrating recent progress. First, we review criteria for determining the parameter structure of models from the literature. This has three related issues: parameter identifiability, parameter redundancy, and reparameterization. Second, we review the deep influence of identifiability on various aspects of machine learning from theoretical and application viewpoints. In addition to illustrating the utility and influence of identifiability, we emphasize the interplay among identifiability theory, machine learning, mathematical statistics, information theory, optimization theory, information geometry, Riemann geometry, symbolic computation, Bayesian inference, algebraic geometry, and others. Finally, we present a new perspective together with the associated challenges.},
archivePrefix = {arXiv},
arxivId = {1803.01446},
author = {Ran, Zhi-Yong and Hu, Bao-Gang},
eprint = {1803.01446},
file = {:Users/hahosoya/Documents/Mendeley Desktop/1d991c7b5129537a557290613f724748e866a014.pdf:pdf},
journal = {Neural Comput.},
month = {may},
number = {5},
pages = {1151--1203},
title = {{Parameter Identifiability in Statistical Machine Learning: A Review}},
volume = {29},
year = {2017}
}
@article{Kleim1998,
abstract = {Historically, studies of motor learning focused on changes in neuronal activity in association 
with simple reflex adaptation (Lisberger and Pavelko 1988; Thompson 1990). Recent work examining
the development of more complex motor behaviors revealed a robust pattern of  ...},
author = {Kleim, J A and et Al},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neurophysiology 1998 Kleim.pdf:pdf},
journal = {J. Neurophysiol.},
month = {jan},
title = {{Functional reorganization of the rat motor cortex following motor skill learning}},
year = {1998}
}
@article{Bortolon2015,
abstract = {Facial emotion processing has been extensively studied in schizophrenia patients while general face processing has received less attention. The already published reviews do not address the current scientific literature in a complete manner. Therefore, here we tried to answer some questions that remain to be clarified, particularly: are the non-emotional aspects of facial processing in fact impaired in schizophrenia patients? At the behavioral level, our key conclusions are that visual perception deficit in schizophrenia patients: are not specific to faces; are most often present when the cognitive (e.g. attention) and perceptual demands of the tasks are important; and seems to worsen with the illness chronification. Although, currently evidence suggests impaired second order configural processing, more studies are necessary to determine whether or not holistic processing is impaired in schizophrenia patients. Neural and neurophysiological evidence suggests impaired earlier levels of visual processing, which might involve the deficits in interaction of the magnocellular and parvocellular pathways impacting on further processing. These deficits seem to be present even before the disorder out-set. Although evidence suggests that this deficit may be not specific to faces, further evidence on this question is necessary, in particularly more ecological studies including context and body processing.},
author = {Bortolon, Catherine and Capdevielle, Delphine and Raffard, St{\'{e}}phane},
file = {:Users/hahosoya/Documents/Mendeley Desktop/e99c82f3bf02441a6e721326faa991d3aa74f639.pdf:pdf},
journal = {Neurosci. Biobehav. Rev.},
keywords = {Face perception,Face recognition,Holistic processing,Identity perception,Schizophrenia},
pages = {79--107},
title = {{Face recognition in schizophrenia disorder: A comprehensive review of behavioral, neuroimaging and neurophysiological studies}},
volume = {53},
year = {2015}
}
@article{Roweis2000,
abstract = {Many areas of science depend on exploratory data analysis and visualization. The need to analyze large amounts of multivariate data raises the fundamental problem of dimensionality reduction: how to discover compact representations of high-dimensional data. Here, we introduce locally linear embedding (LLE), an unsupervised learning algorithm that computes low-dimensional, neighborhood-preserving embeddings of high-dimensional inputs. Unlike clustering methods for local dimensionality reduction, LLE maps its inputs into a single global coordinate system of lower dimensionality, and its optimizations do not involve local minima. By exploiting the local symmetries of linear reconstructions, LLE is able to learn the global structure of nonlinear manifolds, such as those generated by images of faces or documents of text.},
address = {Gatsby Computational Neuroscience Unit, University College London, 17 Queen Square, London WC1N 3AR, UK. roweis@gatsby.ucl.ac.uk},
author = {Roweis, S T and Saul, L K},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Science 2000 Roweis.pdf:pdf},
journal = {Science},
month = {dec},
number = {5500},
pages = {2323--2326},
title = {{Nonlinear dimensionality reduction by locally linear embedding.}},
volume = {290},
year = {2000}
}
@article{Cox2013,
abstract = {Illusory figures demonstrate the visual system's ability to infer surfaces under conditions of fragmented sensory input. To investigate the role of midlevel visual area V4 in visual surface completion, we used multielectrode arrays to measure spiking responses to two types of visual stimuli: Kanizsa patterns that induce the perception of an illusory surface and physically similar control stimuli that do not. Neurons in V4 exhibited stronger and sometimes rhythmic spiking responses for the illusion-promoting configurations compared with controls. Moreover, this elevated response depended on the precise alignment of the neuron's peak visual field sensitivity (receptive field focus) with the illusory surface itself. Neurons whose receptive field focus was over adjacent inducing elements, less than 1.5° away, did not show response enhancement to the illusion. Neither receptive field sizes nor fixational eye movements could account for this effect, which was present in both single-unit signals and multiunit activity. These results suggest that the active perceptual completion of surfaces and shapes, which is a fundamental problem in natural visual experience, draws upon the selective enhancement of activity within a distinct subpopulation of neurons in cortical area V4.},
address = {Section on Cognitive Neurophysiology and Imaging and Laboratory of Neuropsychology, National Institute of Mental Health, National Institutes of Health, Department of Health and Human Services, Bethesda, MD 20892.},
author = {Cox, Michele A and Schmid, Michael C and Peters, Andrew J and Saunders, Richard C and Leopold, David A and Maier, Alexander},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proceedings of the National Academy of Sciences 2013 Cox.pdf:pdf},
journal = {Proc. Natl. Acad. Sci.},
month = {oct},
number = {42},
pages = {17095--17100},
title = {{Receptive field focus of visual area V4 neurons determines responses to illusory surfaces.}},
volume = {110},
year = {2013}
}
@article{Peyrache2015,
abstract = {Nature Neuroscience 18, 569 (2015). doi:10.1038/nn.3968},
author = {Peyrache, Adrien and Lacroix, Marie M and Petersen, Peter C and Buzs{\'{a}}ki, Gy{\"{o}}rgy},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature Publishing Group 2015 Peyrache.pdf:pdf},
journal = {Nat. Neurosci.},
month = {mar},
number = {4},
pages = {569--575},
title = {{Internally organized mechanisms of the head direction sense}},
volume = {18},
year = {2015}
}
@article{Verhoef2017a,
author = {Verhoef, Bram-Ernst and Maunsell, John H R},
file = {:Users/hahosoya/Documents/Mendeley Desktop/8D76B2C8-C62D-418D-BA9C-2B38A1F01688.pdf:pdf},
journal = {Nat. Neurosci.},
month = {may},
number = {7},
pages = {969--977},
title = {{Attention-related changes in correlated neuronal activity arise from normalization mechanisms}},
volume = {20},
year = {2017}
}
@article{Baker2018a,
abstract = {An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.“Written by three experts in the field, Deep Learning is the only comprehensive {\ldots}},
author = {Baker, Nicholas and Lu, Hongjing and Erlikhman, Gennady and Kellman, Philip J.},
editor = {Einh{\"{a}}user, Wolfgang},
file = {:Users/hahosoya/Documents/Mendeley Desktop/C6E7A70F-65C9-4FBC-A2E1-64C5EF2CDD97.pdf:pdf},
journal = {PLOS Comput. Biol.},
month = {dec},
number = {12},
pages = {e1006613},
title = {{Deep convolutional networks do not classify based on global object shape}},
volume = {14},
year = {2018}
}
@article{Lee2001,
author = {Lee, A B and Mumford, D and Huang, J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/International journal on machine learning 2001 Lee.pdf:pdf},
journal = {Int. J. Comput. Vis.},
month = {jan},
number = {1},
pages = {35--59},
title = {{Occlusion models for natural images: A statistical study of a scale-invariant dead leaves model}},
volume = {41},
year = {2001}
}
@article{Eickenberg2017,
abstract = {Convolutional networks used for computer vision represent candidate models for the computations performed in mammalian visual systems. We use them as a detailed model of human brain activity during the viewing of natural images by constructing predictive models based on their different layers and BOLD fMRI activations. Analyzing the predictive performance across layers yields characteristic fingerprints for each visual brain region: early visual areas are better described by lower level convolutional net layers and later visual areas by higher level net layers, exhibiting a progression across ventral and dorsal streams. Our predictive model generalizes beyond brain responses to natural images. We illustrate this on two experiments, namely retinotopy and face-place oppositions, by synthesizing brain activity and performing classical brain mapping upon it. The synthesis recovers the activations observed in the corresponding fMRI studies, showing that this deep encoding model captures representations of brain function that are universal across experimental paradigms.},
address = {Inria Parietal Team, Inria Saclay, France; Neurospin, I2BM, DSV, CEA Saclay, France; DATA Team, Informatics Department, Ecole normale sup{\'{e}}rieure, Paris, France. Electronic address: michael.eickenberg@nsup.org.},
author = {Eickenberg, Michael and Gramfort, Alexandre and Varoquaux, Ga{\"{e}}l and Thirion, Bertrand},
file = {:Users/hahosoya/Documents/Mendeley Desktop/0E1CE036-3952-4DFA-BFE2-FFE9DD0B7C70.pdf:pdf},
journal = {Neuroimage},
month = {may},
pages = {184--194},
title = {{Seeing it all: Convolutional network layers map the function of the human visual system.}},
volume = {152},
year = {2017}
}
@article{Baker2002,
author = {Baker, Chris I and Behrmann, Marlene and Olson, Carl R},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature neuroscience 2002 Baker.pdf:pdf},
journal = {Nat. Neurosci.},
month = {oct},
number = {11},
pages = {1210--1216},
title = {{Impact of learning on representation of parts and wholes in monkey inferotemporal cortex}},
volume = {5},
year = {2002}
}
@article{Walker1999,
abstract = {Areas beyond the classical receptive field (CRF) can modulate responses of the majority of cells in the primary visual cortex of the cat (). Although general characteristics of this phenomenon have been reported previously, little is known about the detailed spatial organization of the surrounds. Previous work suggests that the surrounds may be uniform regions that encircle the CRF or may be limited to the "ends" of the CRF. We have examined the spatial organization of surrounds of single-cell receptive fields in the primary visual cortex of anesthetized, paralyzed cats. The CRF was stimulated with an optimal drifting grating, whereas the surround was probed with a second small grating patch placed at discrete locations around the CRF. For most cells that exhibit suppression, the surrounds are spatially asymmetric, such that the suppression originates from a localized region. We find a variety of suppressive zone locations, but there is a slight bias for suppression to occur at the end zones of the CRF. The spatial pattern of suppression is independent of the parameters of the suppressive stimulus used, although the effect is clearest with iso-oriented surround stimuli. A subset of cells exhibit axially symmetric or uniform surround fields. These results demonstrate that the surrounds are more specific than previously realized, and this specialization has implications for the processing of visual information in the primary visual cortex. One possibility is that these localized surrounds may provide a substrate for figure-ground segmentation of visual scenes.},
address = {Group in Vision Science, School of Optometry, University of California, Berkeley, California 94720-2020, USA.},
author = {Walker, G A and Ohzawa, I and Freeman, R D},
file = {:Users/hahosoya/Documents/Mendeley Desktop/The Journal of neuroscience 1999 Walker.pdf:pdf},
journal = {J. Neurosci.},
month = {dec},
number = {23},
pages = {10536--10553},
title = {{Asymmetric suppression outside the classical receptive field of the visual cortex.}},
volume = {19},
year = {1999}
}
@article{Sato2001,
author = {Sato, M A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neural Computation 2001 Sato-2.pdf:pdf},
journal = {Neural Comput.},
month = {jan},
number = {7},
pages = {1649--1681},
title = {{Online model selection based on the variational Bayes}},
volume = {13},
year = {2001}
}
@article{Keysers2005,
abstract = {Iconic memory, the short-lasting visual memory of a briefly flashed stimulus, is an important component of most models of visual perception. Here we investigate what physiological mechanisms underlie this capacity by showing rapid serial visual presentation (RSVP) sequences with and without interstimulus gaps to human observers and macaque monkeys. For gaps of up to 93 ms between consecutive images, human observers and neurones in the temporal cortex of macaque monkeys were found to continue processing a stimulus as if it was still present on the screen. The continued firing of neurones in temporal cortex may therefore underlie iconic memory. Based on these findings, a neurophysiological vision of iconic memory is presented. The first two authors contributed equally.},
author = {Keysers, C and Xiao, D K and F{\"{o}}ldi{\'{a}}k, P and Perrett, D I},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Cognitive Neuropsychology 2005 Keysers.pdf:pdf},
journal = {Cogn. Neuropsychol.},
month = {may},
number = {3-4},
pages = {316--332},
title = {{Out of sight but not out of mind: the neurophysiology of iconic memory in the superior temporal sulcus}},
volume = {22},
year = {2005}
}
@article{Eliasmith2012,
author = {Eliasmith, C and Stewart, T C and Choo, X and Bekolay, T and DeWolf, T and Tang, Y and Rasmussen, D},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Science 2012 Eliasmith.pdf:pdf},
journal = {Science},
month = {nov},
number = {6111},
pages = {1202--1205},
title = {{A Large-Scale Model of the Functioning Brain}},
volume = {338},
year = {2012}
}
@article{Turner2016,
abstract = {Neuron, 90 (2016) 1257-1271. doi:10.1016/j.neuron.2016.05.006},
author = {Turner, Maxwell H and Rieke, Fred},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neuron 2016 Turner.pdf:pdf},
journal = {Neuron},
month = {jun},
number = {6},
pages = {1257--1271},
title = {{Synaptic Rectification Controls Nonlinear Spatial Integration of Natural Visual Inputs}},
volume = {90},
year = {2016}
}
@article{Krupic2018,
abstract = {Grid cells are neurons active in multiple fields arranged in a hexagonal lattice and are thought to represent the "universal metric for space." However, they become nonhomogeneously distorted in polarized enclosures, which challenges this view. We found that local changes to the configuration of the enclosure induce individual grid fields to shift in a manner inversely related to their distance from the reconfigured boundary. The grid remained primarily anchored to the unchanged stable walls and showed a nonuniform rescaling. Shifts in simultaneously recorded colocalized grid fields were strongly correlated, which suggests that the readout of the animal's position might still be intact. Similar field shifts were also observed in place and boundary cells-albeit of greater magnitude and more pronounced closer to the reconfigured boundary-which suggests that there is no simple one-to-one relationship between these three different cell types.},
address = {Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, CB2 3EG, UK. jk727@cam.ac.uk.},
author = {Krupic, Julija and Bauza, Marius and Burton, Stephen and O'Keefe, John},
file = {:Users/hahosoya/Documents/Mendeley Desktop/26F38AB6-2E84-4E3A-8D8E-D1E67201642F.pdf:pdf},
journal = {Science},
month = {mar},
number = {6380},
pages = {1143--1146},
title = {{Local transformations of the hippocampal cognitive map.}},
volume = {359},
year = {2018}
}
@article{Kietzmann2012,
abstract = {Although the ability to recognize faces and objects from a variety of viewpoints is crucial to our everyday behavior, the underlying cortical mechanisms are not well understood. Recently, neurons in a face-selective region of the monkey temporal cortex were reported to be selective for mirror-symmetric viewing angles of faces as they were rotated in depth (Freiwald and Tsao, 2010). This property has been suggested to constitute a key computational step in achieving full view-invariance. Here, we measured functional magnetic resonance imaging activity in nine observers as they viewed upright or inverted faces presented at five different angles (-60,-30, 0, 30, and 60°). Using multivariate pattern analysis, we show that sensitivity to viewpoint mirror symmetry is widespread in the human visual system. The effect was observed in a large band of higher order visual areas, including the occipital face area, fusiform face area, lateral occipital cortex, mid fusiform, parahippocampal place area, and extending superiorly to encompass dorsal regions V3A/B and the posterior intraparietal sulcus. In contrast, early retinotopic regions V1-hV4 failed to exhibit sensitivity to viewpoint symmetry, as their responses could be largely explained by a computational model of low-level visual similarity. Our findings suggest that selectivity for mirrorsymmetric viewing angles may constitute an intermediate-level processing step shared across multiple higher order areas of the ventral and dorsal streams, setting the stage for complete viewpoint-invariant representations at subsequent levels of visual processing. {\textcopyright} 2012 the authors.},
author = {Kietzmann, Tim C. and Swisher, Jascha D. and K{\"{o}}nig, Peter and Tong, Frank},
file = {:Users/hahosoya/Documents/Mendeley Desktop/11763.full.pdf:pdf},
journal = {J. Neurosci.},
number = {34},
pages = {11763--11772},
title = {{Prevalence of selectivity for mirror-symmetric views of faces in the ventral and dorsal visual pathways}},
volume = {32},
year = {2012}
}
@article{Lample2017,
abstract = {This paper introduces a new encoder-decoder architecture that is trained to reconstruct images by disentangling the salient information of the image and the values of attributes directly in the latent space. As a result, after training, our model can generate different realistic versions of an input image by varying the attribute values. By using continuous attribute values, we can choose how much a specific attribute is perceivable in the generated image. This property could allow for applications where users can modify an {\ldots}
},
author = {Lample, Guillaume and Zeghidour, Neil and Usunier, Nicolas and Bordes, Antoine and Denoyer, Ludovic and Ranzato, Marc'Aurelio},
file = {:Users/hahosoya/Documents/Mendeley Desktop/9493E704-53BE-4246-BBC6-E35CC95A9C06.pdf:pdf},
journal = {Adv. Neural Inf. Process. Syst.},
month = {jan},
pages = {5967--5976},
title = {{Fader Networks:Manipulating Images by Sliding Attributes}},
year = {2017}
}
@article{Mynarski2021,
abstract = {The ability to adapt to changes in stimulus statistics is a hallmark of sensory systems. Here, we developed a theoretical framework that can account for the dynamics of adaptation from an information processing perspective. We use this framework to optimize and analyze adaptive sensory codes, and we show that codes optimized for stationary environments can suffer from prolonged periods of poor performance when the environment changes. To mitigate the adversarial effects of these environmental changes, sensory systems must navigate tradeoffs between the ability to accurately encode incoming stimuli and the ability to rapidly detect and adapt to changes in the distribution of these stimuli. We derive families of codes that balance these objectives, and we demonstrate their close match to experimentally observed neural dynamics during mean and variance adaptation. Our results provide a unifying perspective on adaptation across a range of sensory systems, environments, and sensory tasks.},
author = {M{\l}ynarski, Wiktor F. and Hermundstad, Ann M.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/s41593-021-00846-0.pdf:pdf},
journal = {Nat. Neurosci.},
number = {7},
pages = {998--1009},
pmid = {34017131},
title = {{Efficient and adaptive sensory codes}},
volume = {24},
year = {2021}
}
@article{Sugita2008,
abstract = {Infant monkeys were reared with no exposure to any faces for 6-24 months. Before being allowed to see a face, the monkeys showed a preference for human and monkey faces in photographs, and they discriminated human faces as well as monkey faces. After the deprivation period, the monkeys were exposed first to either human or monkey faces for a month. Soon after, the monkeys selectively discriminated the exposed species of face and showed a marked difficulty in regaining the ability to discriminate the other nonexposed species of face. These results indicate the existence of an experience-independent ability for face processing as well as an apparent sensitive period during which a broad but flexible face prototype develops into a concrete one for efficient processing of familiar faces.},
address = {Core Research for Evolutional Science and Technology (CREST), Japan Science and Technology Agency, Sanban-cho 5, Chiyoda-ku, Tokyo 102-0075, Japan. y.sugita@aist.go.jp},
author = {Sugita, Yoichi},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proceedings of the National Academy of Sciences 2008 Sugita.pdf:pdf},
journal = {Proc. Natl. Acad. Sci.},
month = {jan},
number = {1},
pages = {394--398},
title = {{Face perception in monkeys reared with no exposure to faces.}},
volume = {105},
year = {2008}
}
@article{Markov2013,
abstract = {Current Opinion in Neurobiology. 10.1016/j.conb.2012.12.008},
author = {Markov, Nikola T and Kennedy, Henry},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Curr. Opin. Neurobiol. 2013 Markov.pdf:pdf},
journal = {Curr. Opin. Neurobiol.},
month = {jan},
number = {2},
pages = {1--8},
title = {{The importance of being hierarchical}},
volume = {23},
year = {2013}
}
@inproceedings{Blanz1999a,
abstract = {Abstract In this paper, a new technique for modeling textured 3D faces is introduced. 3D faces can either be generated automatically from one or more photographs, or modeled directly through an intuitive user interface. Users are assisted in two key problems of ...
},
author = {Blanz, V and Vetter, T},
booktitle = {Proc. 26th Annu. Conf. {\ldots}},
file = {:Users/hahosoya/Documents/Mendeley Desktop/1999 Blanz.pdf:pdf},
month = {jan},
title = {{A morphable model for the synthesis of 3D faces}},
year = {1999}
}
@article{Verhoef2016,
author = {Verhoef, Bram-Ernst and Vogels, Rufin and Janssen, Peter},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Philosophical Transactions of the Royal Society B Biological Sciences 2016 Verhoef.pdf:pdf},
journal = {Philos. Trans. R. Soc. B Biol. Sci.},
month = {jun},
number = {1697},
pages = {20150259},
title = {{Binocular depth processing in the ventral visual pathway}},
volume = {371},
year = {2016}
}
@article{Gavornik2014,
abstract = {Nature Neuroscience 17, 732 (2014). doi:10.1038/nn.3683},
author = {Gavornik, Jeffrey P and Bear, Mark F},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature Publishing Group 2014 Gavornik.pdf:pdf},
journal = {Nat. Neurosci.},
month = {mar},
number = {5},
pages = {732--737},
title = {{Learned spatiotemporal sequence recognition and prediction in primary visual cortex}},
volume = {17},
year = {2014}
}
@article{Schwartz2002,
abstract = {10 Natural Image Statistics and Divisive Normalization Martin J. Wainwright, Odelia Schwartz , and Eero P. Simoncelli Introduction Understanding the functional role of neurons and neural systems is a primary goal of systems neuroscience. A longstanding hypothesis ...},
author = {Schwartz, O and Simoncelli, E P},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2002 Schwartz.pdf:pdf},
journal = {Probabilistic Model. {\ldots}},
month = {jan},
title = {{Natural Image Statistics and Divisive Normalization: Modeling Nonlinearities and Adaptation in Cortical Neurons}},
year = {2002}
}
@article{Santoro2016,
abstract = {Despite recent breakthroughs in the applications of deep neural networks, one setting that presents a persistent challenge is that of "one-shot learning." Traditional gradient-based networks require a lot of data to learn, often through extensive iterative training. When new data is encountered, the models must inefficiently releam their parameters to adequately incorporate the new information without catastrophic interference. Architectures with augmented memory capacities, such as Neural Turing Machines (NTMs), offer the ability to quickly encode and retrieve new information, and hence can potentially obviate the downsides of conventional models. Here, we demonstrate the ability of a memory-augmented neural network to rapidly assimilate new data, and leverage this data to make accurate predictions after only a few samples. We also introduce a new method for accessing an external memory that focuses on memory content, unlike previous methods that additionally use memory location-based focusing mechanisms.},
author = {Santoro, Adam and Bartunov, Sergey and Botvinick, Matthew and Wierstra, Daan and Lillicrap, Timothy},
file = {:Users/hahosoya/Documents/Mendeley Desktop/af001301ef840e8a0932484ed130cb4c86cc76c1.pdf:pdf},
isbn = {9781510829008},
journal = {33rd Int. Conf. Mach. Learn. ICML 2016},
pages = {2740--2751},
title = {{Meta-Learning with Memory-Augmented Neural Networks}},
volume = {4},
year = {2016}
}
@article{Welling2005,
abstract = {Directed graphical models with one layer of observed random variables and one or more layers 
of hidden random variables have been the dom- inant modelling paradigm in many research
fields. Although this ap- proach has met with considerable success, the causal semantics  ...},
author = {Welling, M and et Al},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Advances in neural information {\ldots} 2005 Welling.pdf:pdf},
journal = {Adv. Neural Inf. {\ldots}},
month = {jan},
title = {{Exponential family harmoniums with an application to information retrieval}},
year = {2005}
}
@inproceedings{Salakhutdinov2009,
abstract = {...  Deep  Boltzmann Machines h3 h2 h1 v W3 W2 W1 Deep Belief Network Deep  Boltzmann  Machine Pretraining W W W h h h h W h ... v v Compose W W v RBM RBM 1 1 2 1 1 2 2 2 1 2 2 1 Figure 2: Left: A three-layer Deep Belief Network and a three-layer Deep  Boltzmann  Machine . ... 
},
author = {Salakhutdinov, R and Hinton, G E},
booktitle = {Proc. Int. {\ldots}},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2009 Salakhutdinov.pdf:pdf},
month = {jan},
title = {{Deep boltzmann machines}},
year = {2009}
}
@article{Rad2010,
abstract = {Estimating two-dimensional firing rate maps is a common problem, arising in a number of contexts: the estimation of place fields in hippocampus, the analysis of temporally nonstationary tuning curves in sensory and motor areas, the estimation of firing rates following spike-triggered covariance analyses, etc. Here we introduce methods based on Gaussian process nonparametric Bayesian techniques for estimating these two-dimensional rate maps. These techniques offer a number of advantages: the estimates may be computed efficiently, come equipped with natural errorbars, adapt their smoothness automatically to the local density and informativeness of the observed data, and permit direct fitting of the model hyperparameters (e.g., the prior smoothness of the rate map) via maximum marginal likelihood. We illustrate the method's flexibility and performance on a variety of simulated and real data.},
address = {Department of Statistics and Center for Theoretical Neuroscience, Columbia University, New York, USA. kamiar@stat.columbia.edu},
author = {Rad, Kamiar Rahnama and Paninski, Liam},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Network Computation in Neural Systems 2010 Rad.pdf:pdf},
journal = {Netw. Comput. Neural Syst.},
month = {jan},
number = {3-4},
pages = {142--168},
title = {{Efficient, adaptive estimation of two-dimensional firing rate surfaces via Gaussian process methods.}},
volume = {21},
year = {2010}
}
@article{Betancourt2017,
abstract = {Abstract: Hamiltonian Monte Carlo has proven a remarkable empirical success, but only recently have we begun to develop a rigorous under-standing of why it performs so well on difficult problems and how it is best applied in practice. Unfortunately, that understanding is con-fined within the mathematics of differential geometry which has limited its dissemination, especially to the applied communities for which it is particularly important. In this review I provide a comprehensive conceptual account of these theoretical foundations, focusing on {\ldots}
},
author = {Betancourt, Michael},
file = {:Users/hahosoya/Documents/Mendeley Desktop/C3C62219-2AAC-4CED-9F41-9C011FB2649A.pdf:pdf},
month = {jan},
title = {{A Conceptual Introduction to Hamiltonian Monte Carlo}},
year = {2017}
}
@article{Lindeberg2013,
author = {Lindeberg, Tony},
editor = {Martinez, Luis M},
file = {:Users/hahosoya/Documents/Mendeley Desktop/PLoS ONE 2013 Lindeberg.pdf:pdf},
journal = {PLoS One},
month = {jul},
number = {7},
pages = {e66990},
title = {{Invariance of visual operations at the level of receptive fields}},
volume = {8},
year = {2013}
}
@article{Dean2012,
abstract = {Abstract Recent work in unsupervised feature learning and deep learning has shown that being able to train large models can dramatically improve performance. In this paper, we consider the problem of training a deep network with billions of parameters using tens of ... 
},
author = {Dean, J and Corrado, G and Monga, R and Chen, K},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Advances in neural {\ldots} 2012 Dean.pdf:pdf},
journal = {Adv. neural  {\ldots}},
month = {jan},
title = {{Large scale distributed deep networks}},
year = {2012}
}
@article{MANGINI2004,
author = {MANGINI, M and Biederman, I},
file = {:Users/hahosoya/Documents/Mendeley Desktop/9C51DCAE-8D9E-4260-B88B-5804335B9AB6.pdf:pdf},
journal = {Cogn. Sci.},
month = {mar},
number = {2},
pages = {209--226},
title = {{Making the ineffable explicit: estimating the information employed for face classifications}},
volume = {28},
year = {2004}
}
@article{Schwartz2006a,
abstract = {Response properties of sensory neurons are commonly described using receptive fields. This description may be formalized in a model that operates with a small set of linear filters whose outputs are nonlinearly combined to determine the instantaneous firing rate. Spike-triggered average and covariance analyses can be used to estimate the filters and nonlinear combination rule from extracellular experimental data. We describe this methodology, demonstrating it with simulated model neuron examples that emphasize practical issues that arise in experimental situations.},
address = {Howard Hughes Medical Institute and The Salk Intitute, La Jolla, CA 92037, USA. odelia@salk.edu},
author = {Schwartz, Odelia and Pillow, Jonathan W and Rust, Nicole C and Simoncelli, Eero P},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Vision 2006 Schwartz.pdf:pdf},
journal = {J. Vis.},
month = {jan},
number = {4},
pages = {484--507},
title = {{Spike-triggered neural characterization.}},
volume = {6},
year = {2006}
}
@article{Taubert2020,
abstract = {When we move the features of our face, or turn our head, we communicate changes in our internal state to the people around us. How this information is encoded and utilized by an observer's brain is poorly understood. We investigated this issue using a functional MRI-adaptation paradigm in awake male macaques. Among face-selective patches of the superior temporal sulcus (STS), we found a double dissociation of areas processing facial expression and those processing head orientation. The face-selective patches in the STS fundus were most sensitive to facial expression, as was the amygdala, whereas those on the lower, lateral edge of the sulcus were most sensitive to head orientation. The results of this study reveal a new dimension of functional organization, with face-selective patches segregating within the STS. The findings thus force a rethinking of the role of the face processing system in representing subject-directed actions and supporting social cognition.SIGNIFICANCE STATEMENT.When we are interacting with another person, we make inferences about their emotional state based on visual signals. For example, when a person's facial expression changes we are given information about their feelings. While primates are thought to have specialized cortical mechanisms for analyzing the identity of faces, less is known about how these mechanisms unpack transient signals, like expression, that can change from one moment to the next. Here, using an fMRI-adaptation paradigm, we demonstrate that while the identity of a face is held constant, there are separate mechanisms in the macaque brain for processing transient changes in the face's expression and orientation. These findings shed new light on the function of the face processing system during social exchanges.},
author = {Taubert, Jessica and Japee, Shruti and Murphy, Aidan P. and Tardiff, Clarissa T. and Koele, Elissa A. and Kumar, Susheel and Leopold, David A. and Ungerleider, Leslie G.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/8119.full.pdf:pdf},
journal = {J. Neurosci.},
keywords = {about their feelings,amygdala,cognitive neuroscience,example,face perception,facial expressions,for,nhp fmri,on visual signals,s facial expression changes,significance statement,social cognition,their emotional state based,we are given information,we make inferences about,when a person,when we are interacting,while primates are thought,with another person},
number = {42},
pages = {8119--8131},
pmid = {32928886},
title = {{Parallel Processing of Facial Expression and Head Orientation in the Macaque Brain}},
volume = {40},
year = {2020}
}
@article{Perrett1985,
abstract = {Abstract The direction of eye gaze and orientation of the face towards or away from another are important social signals for man and for macaque monkey. We have studied the effects of these signals in a region of the macaque temporal cortex where cells have been found ... 
},
author = {Perrett, D I and Smith, P A J and Potter, D D},
file = {:Users/hahosoya/Documents/Mendeley Desktop/1985 Perrett.pdf:pdf},
journal = {{\ldots} R. {\ldots}},
month = {jan},
title = {{Visual cells in the temporal cortex sensitive to face view and gaze direction}},
year = {1985}
}
@article{Allen2012,
author = {Allen, E A and Damaraju, E and Plis, S M and Erhardt, E B and Eichele, T and Calhoun, V D},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Cerebral Cortex 2012 Allen.pdf:pdf},
journal = {Cereb. Cortex},
month = {nov},
title = {{Tracking Whole-Brain Connectivity Dynamics in the Resting State}},
year = {2012}
}
@article{He2001,
abstract = {Exposure to visual patterns of high contrast (for example, gratings formed by alternating white and black bars) creates after-effects in perception. We become temporarily insensitive to faint test patterns that resemble the pre-exposed pattern (such as gratings of the same orientation), and we require more contrast to detect them. Moreover, if the test pattern is slightly tilted relative to the pre-exposed one, this tilt may be perceptually exaggerated: we experience a tilt after-effect. Here we show that these visual after-effects occur even if the pre-exposed grating is too fine to be perceptually resolved. After looking at a very fine grating, so high in spatial frequency that it was perceptually indistinguishable from a uniform field, observers required more contrast to detect a test grating presented at the same orientation than one presented at the orthogonal orientation. They also experienced a tilt after-effect that depended on the relation of the test pattern's tilt to the unseen orientation of the pre-exposed pattern. Because these after-effects are due to changes in orientation-sensitive mechanisms in visual cortex, our observations imply that extremely fine details, even those too fine to be seen, can penetrate the visual system as far as the cortex, where they are represented neurally without conscious awareness.},
address = {Department of Psychology, University of Minnesota, 75 East River Road, Minneapolis, Minnesota 55455, USA. sheng@tc.umn.edu},
author = {He, S and MacLeod, D I},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature 2001 He.pdf:pdf},
journal = {Nature},
month = {may},
number = {6836},
pages = {473--476},
title = {{Orientation-selective adaptation and tilt after-effect from invisible patterns.}},
volume = {411},
year = {2001}
}
@article{Kanai2012,
abstract = {CURBIO, 22 (2012) R392-R396. doi:10.1016/j.cub.2012.03.033},
author = {Kanai, Ryota and Tsuchiya, Naotsugu},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Curr. Biol. 2012 Kanai.pdf:pdf},
journal = {Curr. Biol.},
month = {may},
number = {10},
pages = {R392--R396},
title = {{Qualia}},
volume = {22},
year = {2012}
}
@article{Zylberberg2013,
author = {Zylberberg, Joel and DeWeese, Michael Robert},
editor = {Sporns, Olaf},
file = {:Users/hahosoya/Documents/Mendeley Desktop/PLoS Comp Biol 2013 Zylberberg.pdf:pdf},
journal = {PLoS Comput. Biol.},
month = {aug},
number = {8},
pages = {e1003182},
title = {{Sparse Coding Models Can Exhibit Decreasing Sparseness while Learning Sparse Codes for Natural Images}},
volume = {9},
year = {2013}
}
@article{Rosenberg2015,
abstract = {Autism is a neurodevelopmental disorder that manifests as a heterogeneous set of social, cognitive, motor, and perceptual symptoms. This system-wide pervasiveness suggests that, rather than narrowly impacting individual systems such as affection or vision, autism may broadly alter neural computation. Here, we propose that alterations in nonlinear, canonical computations occurring throughout the brain may underlie the behavioral characteristics of autism. One such computation, called divisive normalization, balances a neuron's net excitation with inhibition reflecting the overall activity of the neuronal population. Through neural network simulations, we investigate how alterations in divisive normalization may give rise to autism symptomatology. Our findings show that a reduction in the amount of inhibition that occurs through divisive normalization can account for perceptual consequences of autism, consistent with the hypothesis of an increased ratio of neural excitation to inhibition (E/I) in the disorder. These results thus establish a bridge between an E/I imbalance and behavioral data on autism that is currently absent. Interestingly, our findings implicate the context-dependent, neuronal milieu as a key factor in autism symptomatology, with autism reflecting a less "social" neuronal population. Through a broader discussion of perceptual data, we further examine how altered divisive normalization may contribute to a wide array of the disorder's behavioral consequences. These analyses show how a computational framework can provide insights into the neural basis of autism and facilitate the generation of falsifiable hypotheses. A computational perspective on autism may help resolve debates within the field and aid in identifying physiological pathways to target in the treatment of the disorder.},
author = {Rosenberg, Ari and Patterson, Jaclyn Sky and Angelaki, Dora E.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/68346a971f01e2a7cae81af9a5787e2e0f8ceb78.pdf:pdf},
journal = {Proc. Natl. Acad. Sci. U. S. A.},
keywords = {Autism,Bayesian inference,Divisive normalization,E/I imbalance,Neural computation},
number = {30},
pages = {9158--9165},
title = {{A computational perspective on autism}},
volume = {112},
year = {2015}
}
@article{Kayaert2005,
author = {Kayaert, Greet and Biederman, Irving and {Op de Beeck}, Hans P and Vogels, Rufin},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Eur J Neurosci 2005 Kayaert.pdf:pdf},
journal = {Eur. J. Neurosci.},
month = {jul},
number = {1},
pages = {212--224},
title = {{Tuning for shape dimensions in macaque inferior temporal cortex}},
volume = {22},
year = {2005}
}
@article{Limbacher2020,
abstract = {The ability to base current computations on memories from the past is critical for many cognitive tasks such as story understanding. Hebbian-type synaptic plasticity is believed to underlie the retention of memories over medium and long time scales in the brain. However, it is unclear how such plasticity processes are integrated with computations in cortical networks. Here, we propose Hebbian Memory Networks (H-Mems), a simple neural network model that is built around a core hetero-associative network subject to Hebbian plasticity. We show that the network can be optimized to utilize the Hebbian plasticity processes for its computations. H-Mems can one-shot memorize associations between stimulus pairs and use these associations for decisions later on. Furthermore, they can solve demanding question-answering tasks on synthetic stories. Our study shows that neural network models are able to enrich their computations with memories through simple Hebbian plasticity processes.},
author = {Limbacher, Thomas and Legenstein, Robert},
file = {:Users/hahosoya/Documents/Mendeley Desktop/dfaa87db18e82948ce961810b6ca1cda1abbd53a.pdf:pdf},
journal = {NeurIPS},
number = {NeurIPS},
title = {{H-Mem: Harnessing synaptic plasticity with Hebbian Memory Networks}},
year = {2020}
}
@article{Fournier2018,
abstract = {Neuron, 97 (2018) 164-187. doi:10.1016/j.neuron.2017.11.017},
author = {Fournier, Julien and M{\"{u}}ller, Christian M and Schneider, Ingmar and Laurent, Gilles},
file = {:Users/hahosoya/Documents/Mendeley Desktop/92166ED3-8561-44F5-B930-A2F441BF74D6.pdf:pdf},
journal = {Neuron},
month = {jan},
number = {1},
pages = {164--180.e7},
title = {{Spatial Information in a Non-retinotopic Visual Cortex}},
volume = {97},
year = {2018}
}
@article{Abeles1995,
abstract = {Parallel recordings of spike trains of several single cortical neurons in behaving monkeys were analyzed as a hidden Markov process. The parallel spike trains were considered as a multivariate Poisson process whose vector firing rates change with time. As a consequence of this approach, the complete recording can be segmented into a sequence of a few statistically discriminated hidden states, whose dynamics are modeled as a first-order Markov chain. The biological validity and benefits of this approach were examined in several independent ways: (i) the statistical consistency of the segmentation and its correspondence to the behavior of the animals; (ii) direct measurement of the collective flips of activity, obtained by the model; and (iii) the relation between the segmentation and the pair-wise short-term cross-correlations between the recorded spike trains. Comparison with surrogate data was also carried out for each of the above examinations to assure their significance. Our results indicated the existence of well-separated states of activity, within which the firing rates were approximately stationary. With our present data we could reliably discriminate six to eight such states. The transitions between states were fast and were associated with concomitant changes of firing rates of several neurons. Different behavioral modes and stimuli were consistently reflected by different states of neural activity. Moreover, the pair-wise correlations between neurons varied considerably between the different states, supporting the hypothesis that these distinct states were brought about by the cooperative action of many neurons.},
address = {School of Medicine, Hebrew University, Jerusalem, Israel.},
author = {Abeles, M and Bergman, H and Gat, I and Meilijson, I and Seidemann, E and Tishby, N and Vaadia, E},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proc. Natl. Acad. Sci. U.S.A. 1995 Abeles.pdf:pdf},
journal = {Proc. Natl. Acad. Sci. U. S. A.},
month = {sep},
number = {19},
pages = {8616--8620},
title = {{Cortical activity flips among quasi-stationary states.}},
volume = {92},
year = {1995}
}
@article{Stevens2001,
abstract = {A hallmark of mammalian brain evolution is the disproportionate increase in neocortical size as compared with subcortical structures 1 . Because primary visual cortex (V1) is the most thoroughly understood cortical region, the visual system provides an excellent model in which to ...},
author = {Stevens, Charles F},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2001 Stevens.pdf:pdf},
month = {may},
number = {6834},
pages = {193--195},
title = {{An evolutionary scaling law for the primate visual system and its basis in cortical function}},
volume = {411},
year = {2001}
}
@article{Layton2014,
abstract = {... Human subjects that view displays with simulated rotation report traveling along a circular path. (c) First-order optic flow experienced by an observer traveling on a circular path whose gaze is along heading or tangent to the circular path. doi: 10.1371 / journal . pcbi . 1003476 . g001 . ... 
},
author = {Layton, O W and Browning, N A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/PLoS Comp Biol 2014 Layton.pdf:pdf},
journal = {PLoS Comput. Biol.},
month = {jan},
title = {{A Unified Model of Heading and Path Perception in Primate MSTd}},
year = {2014}
}
@article{Archambeau2009,
author = {Archambeau, C and Bach, F},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2009 Archambeau.pdf:pdf},
month = {jan},
title = {{Sparse probabilistic projections}},
year = {2009}
}
@article{Engelcke2019,
abstract = {Generative latent-variable models are emerging as promising tools in robotics and reinforcement learning. Yet, even though tasks in these domains typically involve distinct objects, most state-of-the-art generative models do not explicitly capture the compositional nature of visual scenes. Two recent exceptions, MONet and IODINE, decompose scenes into objects in an unsupervised fashion. Their underlying generative processes, however, do not account for component interactions. Hence, neither of them allows for principled sampling of novel scenes. Here we present GENESIS, the first object-centric generative model of 3D visual scenes capable of both decomposing and generating scenes by capturing relationships between scene components. GENESIS parameterises a spatial GMM over images which is decoded from a set of object-centric latent variables that are either inferred sequentially in an amortised fashion or sampled from an autoregressive prior. We train GENESIS on several publicly available datasets and evaluate its performance on scene generation, decomposition, and semi-supervised learning.},
archivePrefix = {arXiv},
arxivId = {1907.13052},
author = {Engelcke, Martin and Kosiorek, Adam R. and Jones, Oiwi Parker and Posner, Ingmar},
eprint = {1907.13052},
file = {:Users/hahosoya/Documents/Mendeley Desktop/02f1c7ab504ed09c84bd3914fb624da703a4341d.pdf:pdf},
pages = {1--17},
title = {{GENESIS: Generative Scene Inference and Sampling with Object-Centric Latent Representations}},
year = {2019}
}
@article{Anzai2007,
abstract = {Contours and textures are important attributes of object surfaces, and are often described by combinations of local orientations in visual images. To elucidate the neural mechanisms underlying contour and texture processing, we examined receptive field (RF) structures of neurons in visual area V2 of the macaque monkey for encoding combinations of orientations. By measuring orientation tuning at several locations within the classical RF, we found that a majority (70%) of V2 neurons have similar orientation tuning throughout the RF. However, many others have RFs containing subregions tuned to different orientations, most commonly about 90 degrees apart. By measuring interactions between two positions within the RF, we found that approximately one-third of neurons show inhibitory interactions that make them selective for combinations of orientations. These results indicate that V2 neurons could play an important role in analyzing contours and textures and could provide useful cues for surface segmentation.},
address = {Department of Anatomy and Neurobiology, Washington University School of Medicine, 660 South Euclid Avenue, St. Louis, Missouri 63110, USA. aanzai@cvs.rochester.edu},
author = {Anzai, Akiyuki and Peng, Xinmiao and {Van Essen}, David C},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature neuroscience 2007 Anzai-2.pdf:pdf},
journal = {Nat. Neurosci.},
month = {oct},
number = {10},
pages = {1313--1321},
title = {{Neurons in monkey visual area V2 encode combinations of orientations.}},
volume = {10},
year = {2007}
}
@article{Treue2001,
abstract = {The processing of visual information combines bottom-up sensory aspects with top-down influences, most notably attentional processes. Attentional influences have now been demonstrated throughout visual cortex , and their influence on the processing of visual  ...},
author = {Treue, S},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Trends in Neurosciences 2001 Treue.pdf:pdf},
journal = {Trends Neurosci.},
month = {jan},
title = {{Neural correlates of attention in primate visual cortex}},
year = {2001}
}
@article{Wang2018,
abstract = {Episodic memory, the conscious recollection of past events, is typically experienced from a first-person (egocentric) perspective. The hippocampus plays an essential role in episodic memory and spatial cognition. Although the allocentric nature of hippocampal spatial coding is well understood, little is known about whether the hippocampus receives egocentric information about external items. We recorded in rats the activity of single neurons from the lateral entorhinal cortex (LEC) and medial entorhinal cortex (MEC), the two major inputs to the hippocampus. Many LEC neurons showed tuning for egocentric bearing of external items, whereas MEC cells tended to represent allocentric bearing. These results demonstrate a fundamental dissociation between the reference frames of LEC and MEC neural representations.},
address = {Zanvyl Krieger Mind/Brain Institute, Johns Hopkins University, Baltimore, MD, USA.},
author = {Wang, Cheng and Chen, Xiaojing and Lee, Heekyung and Deshmukh, Sachin S and Yoganarasimha, D and Savelli, Francesco and Knierim, James J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/D29CDA9A-CF6E-44EA-AC3D-9EF60E10C764.pdf:pdf},
journal = {Science},
month = {nov},
number = {6417},
pages = {945--949},
title = {{Egocentric coding of external items in the lateral entorhinal cortex.}},
volume = {362},
year = {2018}
}
@article{Rajalingham2015,
author = {Rajalingham, R and Schmidt, K and DiCarlo, J J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2015 Rajalingham.pdf:pdf},
journal = {J. Neurosci.},
month = {sep},
number = {35},
pages = {12127--12136},
title = {{Comparison of Object Recognition Behavior in Human and Monkey}},
volume = {35},
year = {2015}
}
@article{Klink2017,
abstract = {Neuron, 95 (2017) 209-223. doi:10.1016/j.neuron.2017.05.033},
author = {Klink, P Christiaan and Dagnino, Bruno and Gariel-Mathis, Marie-Alice and Roelfsema, Pieter R},
file = {:Users/hahosoya/Documents/Mendeley Desktop/52D349CC-5616-403B-94B0-D982002BA26B.pdf:pdf},
journal = {Neuron},
month = {jul},
number = {1},
pages = {209--220.e3},
title = {{Distinct Feedforward and Feedback Effects of Microstimulation in Visual Cortex Reveal Neural Mechanisms of Texture Segregation}},
volume = {95},
year = {2017}
}
@article{Wilson2002,
abstract = {... 2909-2923. doi:10.1016/S0042-6989(02)00362-0 | How to Cite or Link Using DOI 
Copyright {\textcopyright} 2002 Elsevier Science Ltd. All rights reserved. Permissions & Reprints. Synthetic  faces , face  cubes , and the geometry of face  space . ...
},
author = {Wilson, H R and Loffler, Gunter and et Al},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Vision Research 2002 Wilson.pdf:pdf},
journal = {Vision Res.},
month = {jan},
title = {{Synthetic faces, face cubes, and the geometry of face space}},
year = {2002}
}
@article{Cukur2013,
abstract = {Nature Neuroscience 16, 763 (2013). doi:10.1038/nn.3381},
author = {{\c{C}}ukur, Tolga and Nishimoto, Shinji and Huth, Alexander G and Gallant, Jack L},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature Publishing Group 2013 {\c{C}}ukur.pdf:pdf},
journal = {Nat. Neurosci.},
month = {apr},
number = {6},
pages = {763--770},
title = {{Attention during natural vision warps semantic representation across the human brain}},
volume = {16},
year = {2013}
}
@article{King2013,
abstract = {Abstract Sparse coding models of natural scenes can account for several physiological properties of primary visual cortex (V1), including the shapes of simple cell receptive fields (RFs) and the highly kurtotic firing rates of V1 neurons. Current spiking network models of ... 
},
author = {King, P D and Zylberberg, J and DeWeese, M R},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2013 King.pdf:pdf},
journal = {J. Neurosci.},
month = {mar},
number = {13},
pages = {5475--5485},
title = {{Inhibitory Interneurons Decorrelate Excitatory Cells to Drive Sparse Code Formation in a Spiking Model of V1}},
volume = {33},
year = {2013}
}
@article{Battaglia2018,
abstract = {Artificial intelligence (AI) has undergone a renaissance recently, making major progress in key domains such as vision, language, control, and decision-making. This has been due, in part, to cheap data and cheap compute resources, which have fit the natural strengths of deep learning. However, many defining characteristics of human intelligence, which developed under much different pressures, remain out of reach for current approaches. In particular, generalizing beyond one's experiences—a hallmark of human intelligence from infancy—remains a formidable challenge for modern AI. The following is part position paper, part review, and part unification. We argue that combinatorial generalization must be a top priority for AI to achieve human-like abilities, and that structured representations and computations are key to realizing this objective. Just as biology uses nature and nurture cooperatively, we reject the false choice between “hand-engineering” and “end-to-end” learning, and instead advocate for an approach which benefits from their complementary strengths. We explore how using relational inductive biases within deep learning architectures can facilitate learning about entities, relations, and rules for composing them. We present a new building block for the AI toolkit with a strong relational inductive bias—the graph network—which generalizes and extends various approaches for neural networks that operate on graphs, and provides a straightforward interface for manipulating structured knowledge and producing structured behaviors. We discuss how graph networks can support relational reasoning and combinatorial generalization, laying the foundation for more sophisticated, interpretable, and flexible patterns of reasoning. As a companion to this paper, we have also released an open-source software library for building graph networks, with demonstrations of how to use them in practice.},
archivePrefix = {arXiv},
arxivId = {1806.01261},
author = {Battaglia, Peter W. and Hamrick, Jessica B. and Bapst, Victor and Sanchez-Gonzalez, Alvaro and Zambaldi, Vinicius and Malinowski, Mateusz and Tacchetti, Andrea and Raposo, David and Santoro, Adam and Faulkner, Ryan and Gulcehre, Caglar and Song, Francis and Ballard, Andrew and Gilmer, Justin and Dahl, George and Vaswani, Ashish and Allen, Kelsey and Nash, Charles and Langston, Victoria and Dyer, Chris and Heess, Nicolas and Wierstra, Daan and Kohli, Pushmeet and Botvinick, Matt and Vinyals, Oriol and Li, Yujia and Pascanu, Razvan},
eprint = {1806.01261},
file = {:Users/hahosoya/Documents/Mendeley Desktop/ae86e2b5fce620d50b11dfc7302c8528ff7e9da3.pdf:pdf},
journal = {arXiv},
pages = {1--40},
title = {{Relational inductive biases, deep learning, and graph networks}},
year = {2018}
}
@article{Kawabe2015,
author = {Kawabe, Takahiro and Maruya, Kazushi and Nishida, Shin'ya},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proceedings of the National Academy of Sciences 2015 Kawabe.pdf:pdf},
journal = {Proc. Natl. Acad. Sci.},
month = {aug},
number = {33},
pages = {E4620--E4627},
title = {{Perceptual transparency from image deformation}},
volume = {112},
year = {2015}
}
@article{Banyai2019,
abstract = {The presence of stimulus-unrelated covariability in neural responses poses a challenge to models of perception and behavior. Here we review advances in theory and experiments that highlight how noise correlations are implicated in perceptual processes. We focus on the structure of correlations and variations in it to reveal the computations performed by the cortex. We argue that to discover the functional role of correlations in cortical computations, and make predictions about them, we need to harness hierarchical encoding models. We propose that in hierarchical models the source of noise correlations is dual: inference of both task-related and higher-level perceptual variables contribute to their emergence, which is reflected in the task- and stimulus-dependence of the correlation structure.},
author = {B{\'{a}}nyai, Mih{\'{a}}ly and Orb{\'{a}}n, Gergő},
file = {:Users/hahosoya/Documents/Mendeley Desktop/1-s2.0-S0959438818302058-main.pdf:pdf},
journal = {Curr. Opin. Neurobiol.},
pages = {209--217},
title = {{Noise correlations and perceptual inference}},
volume = {58},
year = {2019}
}
@article{Tang2012,
abstract = {Abstract: An efficient way to learn deep density models that have many layers of latent variables is to learn one layer at a time using a model that has only one layer of latent variables. After learning each layer, samples from the posterior distributions for that layer are used as training data for learning the next layer. This approach is commonly used with Restricted Boltzmann Machines, which are undirected graphical models with a single ...
},
author = {Tang, Y and Salakhutdinov, R and Hinton, G},
file = {:Users/hahosoya/Documents/Mendeley Desktop/arXiv 2012 Tang.pdf:pdf},
journal = {arXiv.org},
month = {jan},
title = {{Deep mixtures of factor analysers}},
year = {2012}
}
@article{Rothenberg1971,
abstract = {A theory of identification is developed for a general stochastic model whose probability law is determined by a finite number of parameters. It is shown under weak regularity conditions that local identifiability of the unknown parameter vector is equivalent to nonsingularity of the information matrix. The use of "reduced-form" parameters to establish identifiability is also analyzed. The general results are applied to the familiar problem of determining whether the coefficients of a system of linear simultaneous equations are identifiable. CR - Copyright &#169; 1971 The Econometric Society},
author = {Rothenberg, Thomas J.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/43588ff71d9a7646c114d6d63eb8fe235287651b.pdf:pdf},
journal = {Econometrica},
month = {may},
number = {3},
pages = {577},
title = {{Identification in Parametric Models}},
volume = {39},
year = {1971}
}
@article{Bolinger2012,
abstract = {Neuron, 73 (2012) 333-346. doi:10.1016/j.neuron.2011.10.039},
author = {B{\"{o}}linger, Daniel and Gollisch, Tim},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neuron 2012 B{\"{o}}linger.pdf:pdf},
journal = {Neuron},
month = {jan},
number = {2},
pages = {333--346},
title = {{Closed-Loop Measurements of Iso-Response Stimuli Reveal Dynamic Nonlinear Stimulus Integration in the Retina}},
volume = {73},
year = {2012}
}
@article{Moeller2017,
author = {Moeller, Sebastian and Crapse, Trinity and Chang, Le and Tsao, Doris Y},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature Publishing Group 2017 Moeller.pdf:pdf},
journal = {Nat. Neurosci.},
month = {mar},
number = {5},
pages = {743--752},
title = {{The effect of face patch microstimulation on perception of faces and objects}},
volume = {20},
year = {2017}
}
@article{Hosoya2019,
author = {Hosoya, Haruo},
file = {:Users/hahosoya/Documents/Mendeley Desktop/E23BB2AC-4C41-4F08-8627-911151F25D9E.pdf:pdf},
journal = {Int. Jt. Conf. Artif. Intell.},
month = {jan},
title = {{Group-based learning of disentangled representations with generalizability for novel contents}},
year = {2019}
}
@article{Heeger1995,
abstract = {Abstract This paper describes a method for synthesizing images that match the texture appearance of a given digitized sample. This synthesis is completely automatic and requires only the “target” texture as input. It allows generation of as much texture as desired so that  ...},
author = {Heeger, D J and Bergen, J R},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proceedings of the 22nd annual conference on {\ldots} 1995 Heeger.pdf:pdf},
journal = {Proc. 22nd Annu. Conf.  {\ldots}},
month = {jan},
title = {{Pyramid-based texture analysis/synthesis}},
year = {1995}
}
@article{VanderLoos1973,
abstract = {References and Notes 1. DW Allen and P. Cole, N. Engl. J. Med. 286, 70 (1972); SE Stewart, 
G. Kasnic, Jr., C. Draycott, W. Feller, A. Golden, E. Mitchell, T. Ben, J. Nat. Cancer Inst. 48, 273
(1972); RM McAllister, WA Nelson- Rees, EY Johnson, RW Rongey, MB Gardner, ibid. 47,  ...},
author = {{Van der Loos}, H and Woolsey, T A},
journal = {Science},
month = {jan},
title = {{Somatosensory cortex: structural alterations following early injury to sense organs}},
year = {1973}
}
@article{Simoncelli1998,
abstract = {Electrophysiological studies indicate that neurons in the middle temporal ( MT ) area of the primate brain are selective for the velocity of visual stimuli. This paper describes a computational model of MT physiology, in which local image velocities are represented ... 
},
author = {Simoncelli, E P and Heeger, D J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Vision Research 1998 Simoncelli.pdf:pdf},
journal = {Vision Res.},
month = {jan},
title = {{A model of neuronal responses in visual area MT}},
year = {1998}
}
@article{Gold1999,
abstract = {Signal but not noise changes with perceptual learning},
author = {Gold, J and Bennett, P J and Sekuler, A B},
file = {:Users/hahosoya/Documents/Mendeley Desktop/1FA0D98B-AE0E-4901-AE1E-0B1801C8E081.pdf:pdf},
journal = {Nature},
month = {nov},
number = {6758},
pages = {176--178},
title = {{Signal but not noise changes with perceptual learning}},
volume = {402},
year = {1999}
}
@article{Bruce1981,
author = {Bruce, C and Desimone, R and Gross, C G},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neurophysiology 1981 Bruce.pdf:pdf},
journal = {J. Neurophysiol.},
month = {aug},
number = {2},
pages = {369--384},
title = {{Visual properties of neurons in a polysensory area in superior temporal sulcus of the macaque.}},
volume = {46},
year = {1981}
}
@article{Taylor2001,
abstract = {Abstract Faces and eyes are critical social stimuli which adults process with ease, but how this expertise develops is not yet understood. Neural changes associated with face and eye processing were investigated developmentally using ERPs (N170), in 128 subjects (4–15 ...
},
author = {Taylor, M J and Edmonds, G E and McCarthy, G and Allison, T},
file = {:Users/hahosoya/Documents/Mendeley Desktop/NeuroReport 2001 Taylor.pdf:pdf},
journal = {Neuroreport},
month = {jan},
title = {{Eyes first! Eye processing develops before face processing in children}},
year = {2001}
}
@article{Lee2007,
author = {Lee, J and Williford, T and Maunsell, J H R},
file = {:Users/hahosoya/Documents/Mendeley Desktop/The Journal of neuroscience 1997 Connor.pdf:pdf},
journal = {J. Neurosci.},
month = {jan},
number = {36},
pages = {9632--9637},
title = {{Spatial attention and the latency of neuronal responses in macaque area V4}},
volume = {27},
year = {2007}
}
@article{Rock1992,
abstract = {Abstract Having found by the use of a new method for examining perception without attention that grouping and texture segregation do not seem to occur (see Mack, Tang, Tuma, Kahn, & Rock (1992) Cognitive Psychology, 24, we go on to ask what is perceived ... 
},
author = {Rock, I and Linnett, C M and Grant, P and Mack, A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/1992 Rock.pdf:pdf},
journal = {Cogn. Psychol.},
month = {jan},
title = {{Perception without attention: Results of a new method}},
year = {1992}
}
@article{Murty2020,
abstract = {The fusiform face area responds selectively to faces and is causally involved in face perception. How does face-selectivity in the fusiform arise in development, and why does it develop so systematically in the same location across individuals? Preferential cortical responses to faces develop early in infancy, yet evidence is conflicting on the central question of whether visual experience with faces is necessary. Here, we revisit this question by scanning congenitally blind individuals with fMRI while they haptically explored 3D-printed faces and other stimuli. We found robust face-selective responses in the lateral fusiform gyrus of individual blind participants during haptic exploration of stimuli, indicating that neither visual experience with faces nor fovea-biased inputs is necessary for face-selectivity to arise in the lateral fusiform gyrus. Our results instead suggest a role for long-range connectivity in specifying the location of face-selectivity in the human brain.},
author = {Murty, N. Apurva Ratan and Teng, Santani and Beeler, David and Mynick, Anna and Oliva, Aude and Kanwisher, Nancy},
file = {:Users/hahosoya/Documents/Mendeley Desktop/23011.full.pdf:pdf},
journal = {Proc. Natl. Acad. Sci. U. S. A.},
keywords = {Congenital blindness,Development,Face selectivity,Fusiform gyrus,Haptics},
number = {37},
pages = {23011--23020},
pmid = {32839334},
title = {{Visual experience is not necessary for the development of face-selectivity in the lateral fusiform gyrus}},
volume = {117},
year = {2020}
}
@article{Moore2001,
abstract = {Several studies have shown that targets defined on the basis of the spatial relations between objects yield highly inefficient visual search performance (e.g., Logan, 1994; Palmer, 1994), suggesting that the apprehension of spatial relations may require the selective allocation of attention within the scene. In the present study, we tested the hypothesis that depth relations might be different in this regard and might support efficient visual search. This hypothesis was based, in part, on the fact that many perceptual organization processes that are believed to occur early and in parallel, such as figure-ground segregation and perceptual completion, seem to depend on the assignment of depth relations. Despite this, however, using increasingly salient cues to depth (Experiments 2-4) and including a separate test of the sufficiency of the most salient depth cue used (Experiment 5), no evidence was found to indicate that search for a target defined by depth relations is any different than search for a target defined by other types of spatial relations, with regard to efficiency of search. These findings are discussed within the context of the larger literature on early processing of three-dimensional characteristics of visual scenes.},
address = {Department of Psychology, Penn State University, University Park, PA 16802, USA. cmm15@psu.edu},
author = {Moore, C M and Elsinger, C L and Lleras, A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Percept Psychophys 2001 Moore.pdf:pdf},
journal = {Percept. Psychophys.},
month = {may},
number = {4},
pages = {595--606},
title = {{Visual attention and the apprehension of spatial relations: the case of depth.}},
volume = {63},
year = {2001}
}
@article{Panichello2021,
abstract = {Cognitive control guides behaviour by controlling what, when, and how information is represented in the brain1. For example, attention controls sensory processing; top-down signals from prefrontal and parietal cortex strengthen the representation of task-relevant stimuli2–4. A similar ‘selection' mechanism is thought to control the representations held ‘in mind'—in working memory5–10. Here we show that shared neural mechanisms underlie the selection of items from working memory and attention to sensory stimuli. We trained rhesus monkeys to switch between two tasks, either selecting one item from a set of items held in working memory or attending to one stimulus from a set of visual stimuli. Neural recordings showed that similar representations in prefrontal cortex encoded the control of both selection and attention, suggesting that prefrontal cortex acts as a domain-general controller. By contrast, both attention and selection were represented independently in parietal and visual cortex. Both selection and attention facilitated behaviour by enhancing and transforming the representation of the selected memory or attended stimulus. Specifically, during the selection task, memory items were initially represented in independent subspaces of neural activity in prefrontal cortex. Selecting an item caused its representation to transform from its own subspace to a new subspace used to guide behaviour. A similar transformation occurred for attention. Our results suggest that prefrontal cortex controls cognition by dynamically transforming representations to control what and when cognitive computations are engaged.},
author = {Panichello, Matthew F. and Buschman, Timothy J.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/s41586-021-03390-w.pdf:pdf},
journal = {Nature},
number = {April},
pmid = {33790467},
title = {{Shared mechanisms underlie the control of working memory and attention}},
volume = {592},
year = {2021}
}
@article{Lau2002,
abstract = {A crucial step toward understanding visual processing is to obtain a comprehensive description of the relationship between visual stimuli and neuronal responses. Many neurons in the visual cortex exhibit nonlinear responses, making it difficult to characterize their stimulus-response relationships. Here, we recorded the responses of primary visual cortical neurons of the cat to spatiotemporal random-bar stimuli and trained artificial neural networks to predict the response of each neuron. The random initial connections in the networks consistently converged to regular patterns. Analyses of these connection patterns showed that the response of each complex cell to the random-bar stimuli could be well approximated by the sum of a small number of subunits resembling simple cells. The direction selectivity of each complex cell measured with drifting gratings was also well predicted by the combination of these subunits, indicating the generality of the model. These results are consistent with a simple functional model for complex cells and demonstrate the usefulness of the neural network method for revealing the stimulus-response transformations of nonlinear neurons.},
address = {Division of Neurobiology, Department of Molecular and Cell Biology, University of California, Berkeley, CA 94720, USA.},
author = {Lau, Brian and Stanley, Garrett B and Dan, Yang},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proc. Natl. Acad. Sci. U.S.A. 2002 Lau.pdf:pdf},
journal = {Proc. Natl. Acad. Sci. U. S. A.},
month = {jun},
number = {13},
pages = {8974--8979},
title = {{Computational subunits of visual cortical neurons revealed by artificial neural networks.}},
volume = {99},
year = {2002}
}
@book{Hyvarinen2001,
abstract = {A comprehensive introduction to ICA for students and practitioners< br> Independent Component Analysis (ICA) is one of the most exciting new topics in fields such as neural networks, advanced statistics, and signal processing. This is the first book to provide a ... 
},
author = {Hyv{\"{a}}rinen, A and Karhunen, J and Oja, E},
month = {jan},
title = {{No Title}},
year = {2001}
}
@article{Kaminiarz2014,
author = {Kaminiarz, A and Schlack, A and Hoffmann, K P and Lappe, M and Bremmer, F},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neurophysiology 2014 Kaminiarz.pdf:pdf},
journal = {J. Neurophysiol.},
month = {nov},
number = {10},
pages = {2470--2480},
title = {{Visual selectivity for heading in the macaque ventral intraparietal area}},
volume = {112},
year = {2014}
}
@article{Silver2016,
abstract = {Nature 529, 484 (2016). doi:10.1038/nature16961},
author = {Silver, David and Huang, Aja and Maddison, Chris J and Guez, Arthur and Sifre, Laurent and van den Driessche, George and Schrittwieser, Julian and Antonoglou, Ioannis and Panneershelvam, Veda and Lanctot, Marc and Dieleman, Sander and Grewe, Dominik and Nham, John and Kalchbrenner, Nal and Sutskever, Ilya and Lillicrap, Timothy and Leach, Madeleine and Kavukcuoglu, Koray and Graepel, Thore and Hassabis, Demis},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature 2016 Silver.pdf:pdf},
journal = {Nature},
month = {jan},
number = {7585},
pages = {484--489},
title = {{Mastering the game of Go with deep neural networks and tree search}},
volume = {529},
year = {2016}
}
@article{Lee2008,
author = {Lee, Honglak and Ekanadham, Chaitanya and Ng, Andrew Y},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Advances in neural information processing systems 2008 Lee.pdf:pdf},
journal = {Adv. Neural Inf. Process. Syst.},
month = {jan},
pages = {873--880},
title = {{Sparse deep belief net model for visual area V2}},
year = {2008}
}
@article{Kim2017a,
abstract = {While humans easily recognize relations between data from different domains without any supervision, learning to automatically discover them is in general very challenging and needs many ground-truth pairs that illustrate the relations. To avoid costly pairing, we address the task of discovering cross-domain relations given unpaired data. We propose a method based on generative adversarial networks that learns to discover relations between different domains (DiscoGAN). Using the discovered relations, our proposed network successfully transfers style from one domain to another while preserving key attributes such as orientation and face identity. Source code for official implementation is publicly available https://github.com/SKTBrain/DiscoGAN},
author = {Kim, Taeksoo and Cha, Moonsu and Kim, Hyunsoo and Lee, Jung Kwon and Kim, Jiwon},
file = {:Users/hahosoya/Documents/Mendeley Desktop/DA2BFDEA-874A-4536-9FB1-C3E196DFFE15.pdf:pdf},
journal = {arXiv.org},
month = {mar},
title = {{Learning to Discover Cross-Domain Relations with Generative Adversarial Networks}},
volume = {cs.CV},
year = {2017}
}
@article{Mnih2014,
abstract = {Abstract Applying convolutional neural networks to large images is computationally expensive because the amount of computation scales linearly with the number of image pixels. We present a novel recurrent neural network model that is capable of extracting ...
},
author = {Mnih, V and Heess, N and Graves, A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Advances in neural information {\ldots} 2014 Mnih.pdf:pdf},
journal = {Adv. Neural Inf. {\ldots}},
month = {jan},
title = {{Recurrent models of visual attention}},
year = {2014}
}
@article{McMains2004,
abstract = {Spatially directed attention strongly enhances visual perceptual processing. The metaphor of the “spotlight” has long been used to describe spatial attention; however, there has been considerable debate as to whether spatial attention must be unitary or may be split between discrete regions of space. This question was addressed here through functional MR imaging of human subjects as they performed a task that required simultaneous attention to two ...
},
author = {McMains, S A and Somers, D C},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neuron 2004 McMains.pdf:pdf},
journal = {Neuron},
month = {jan},
title = {{Multiple spotlights of attentional selection in human visual cortex}},
year = {2004}
}
@article{Rawat2017,
author = {Rawat, Waseem and Wang, Zenghui},
file = {:Users/hahosoya/Documents/Mendeley Desktop/AAD4F8E6-D418-4B2A-85C3-EB622B47EEEE.pdf:pdf},
journal = {Neural Comput.},
month = {sep},
number = {9},
pages = {2352--2449},
title = {{Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review}},
volume = {29},
year = {2017}
}
@article{Hinton,
abstract = {... Page 3. Some things you will learn in this tutorial • How to learn multi-layer generative models of unlabelled ... It can get stuck in poor local optima. – These are often quite good, but for deep nets they are far from optimal. Page 9. ... Page 10. Belief Nets • A belief  net is a directed ... 
},
author = {Hinton, G},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Hinton.pdf:pdf},
journal = {cs.toronto.edu },
title = {{Deep Belief Nets}},
}
@article{Hirose2013,
author = {Hirose, Tatsuya and Taniguchi, Tadahiro},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2013 Hirose.pdf:pdf},
month = {jan},
title = {{Abstraction Multimodal Low-Dimensional Representation from High-Dimensional Posture Information and Visual Images}},
year = {2013}
}
@inproceedings{Chen2018,
abstract = {The development of high-dimensional generative models has recently gained a great surge of interest with the introduction of variational auto-encoders and generative adversarial neural networks. Different variants have been proposed where the underlying latent space is structured, for example, based on attributes describing the data to generate. We focus on a particular problem where one aims at generating samples corresponding to a number of objects under various views. We assume that the distribution of the data is driven by two independent latent factors: the content, which represents the intrinsic features of an object, and the view, which stands for the settings of a particular observation of that object. Therefore, we propose a generative model and a conditional variant built on such a disentangled latent space. This approach allows us to generate realistic samples corresponding to various objects in a high variety of views. Unlike many multi-view approaches, our model doesn't need any supervision on the views but only on the content. Compared to other conditional generation approaches that are mostly based on binary or categorical attributes, we make no such assumption about the factors of variations. Our model can be used on problems with a huge, potentially infinite, number of categories. We experiment it on four image datasets on which we demonstrate the effectiveness of the model and its ability to generalize.},
author = {Chen, Micka{\"{e}}l and Denoyer, Ludovic and Arti{\`{e}}res, Thierry},
booktitle = {Int. Conf. Learn. Represent.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/09394770-67CC-4DBE-A41B-F09F4D8BF99D.pdf:pdf},
month = {nov},
title = {{Multi-View Data Generation Without View Supervision}},
year = {2018}
}
@article{Schwartz1983,
abstract = {Inferior temporal cortex plays an important role in shape recognition. To study the shape selectivity of single inferior temporal neurons, we recorded their responses to a set of shapes systematically varying in boundary curvature. Many inferior temporal neurons were selective for stimuli of specific boundary curvature and maintained this selectivity over changes in stimulus size or position. The method of describing boundary curvature was that of Fourier descriptors.},
author = {Schwartz, E L and Desimone, R and Albright, T D and Gross, C G},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proc. Natl. Acad. Sci. U.S.A. 1983 Schwartz.pdf:pdf},
journal = {Proc. Natl. Acad. Sci. U. S. A.},
month = {sep},
number = {18},
pages = {5776--5778},
title = {{Shape recognition and inferior temporal neurons.}},
volume = {80},
year = {1983}
}
@article{Coltheart1983,
abstract = {Abstract Investigations of the processing of brief visual displays, and the explanation of such processing in terms of iconic memory , are reviewed. It is concluded that the concept of a pre- categorical sensory memory for visual material remains tenable. The ability to report ... 
},
author = {Coltheart, M and Laming, D R J and Routh, D A and Broadbent, D E},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Philosophical Transactions of the Royal Society B Biological Sciences 1983 Coltheart.pdf:pdf},
journal = {Philos. Trans. R. Soc. B Biol. Sci.},
month = {aug},
number = {1110},
pages = {283--294},
title = {{Iconic Memory [and Discussion]}},
volume = {302},
year = {1983}
}
@article{DePasquale2012,
abstract = {Neuron, 74 (2012) 753-764. doi:10.1016/j.neuron.2012.03.031},
author = {de Pasquale, Francesco and {Della Penna}, Stefania and Snyder, Abraham Z and Marzetti, Laura and Pizzella, Vittorio and Romani, Gian Luca and Corbetta, Maurizio},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neuron 2012 de Pasquale.pdf:pdf},
journal = {Neuron},
month = {may},
number = {4},
pages = {753--764},
title = {{A Cortical Core for Dynamic Integration of Functional Networks in the Resting Human Brain}},
volume = {74},
year = {2012}
}
@article{Hunt2013,
author = {Hunt, Jonathan J and Dayan, Peter and Goodhill, Geoffrey J},
editor = {Bethge, Matthias},
file = {:Users/hahosoya/Documents/Mendeley Desktop/PLoS Comp Biol 2013 Hunt.pdf:pdf},
journal = {PLoS Comput. Biol.},
month = {may},
number = {5},
pages = {e1003005},
title = {{Sparse Coding Can Predict Primary Visual Cortex Receptive Field Changes Induced by Abnormal Visual Input}},
volume = {9},
year = {2013}
}
@article{Czuba2014,
author = {Czuba, T B and Huk, A C and Cormack, L K and Kohn, A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2014 Czuba.pdf:pdf},
journal = {J. Neurosci.},
month = {nov},
number = {47},
pages = {15522--15533},
title = {{Area MT Encodes Three-Dimensional Motion}},
volume = {34},
year = {2014}
}
@article{Park2021,
abstract = {The ability to rapidly adapt to novel situations is essential for survival, and this flexibility is impaired in many neuropsychiatric disorders1. Thus, understanding whether and how novelty prepares, or primes, brain circuitry to facilitate cognitive flexibility has important translational relevance. Exposure to novelty recruits the hippocampus and medial prefrontal cortex (mPFC)2 and may prime hippocampal–prefrontal circuitry for subsequent learning-associated plasticity. Here we show that novelty resets the neural circuits that link the ventral hippocampus (vHPC) and the mPFC, facilitating the ability to overcome an established strategy. Exposing mice to novelty disrupted a previously encoded strategy by reorganizing vHPC activity to local theta (4–12 Hz) oscillations and weakening existing vHPC–mPFC connectivity. As mice subsequently adapted to a new task, vHPC neurons developed new task-associated activity, vHPC–mPFC connectivity was strengthened, and mPFC neurons updated to encode the new rules. Without novelty, however, mice adhered to their established strategy. Blocking dopamine D1 receptors (D1Rs) or inhibiting novelty-tagged cells that express D1Rs in the vHPC prevented these behavioural and physiological effects of novelty. Furthermore, activation of D1Rs mimicked the effects of novelty. These results suggest that novelty promotes adaptive learning by D1R-mediated resetting of vHPC–mPFC circuitry, thereby enabling subsequent learning-associated circuit plasticity.},
author = {Park, Alan J. and Harris, Alexander Z. and Martyniuk, Kelly M. and Chang, Chia Yuan and Abbas, Atheir I. and Lowes, Daniel C. and Kellendonk, Christoph and Gogos, Joseph A. and Gordon, Joshua A.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/s41586-021-03272-1.pdf:pdf},
journal = {Nature},
number = {7851},
pages = {615--619},
pmid = {33627872},
title = {{Reset of hippocampal–prefrontal circuitry facilitates learning}},
volume = {591},
year = {2021}
}
@article{Konkle2012,
abstract = {Neuron, 74 (2012) 1114-1124. doi:10.1016/j.neuron.2012.04.036},
author = {Konkle, Talia and Oliva, Aude},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neuron 2012 Konkle.pdf:pdf},
journal = {Neuron},
month = {jun},
number = {6},
pages = {1114--1124},
title = {{A Real-World Size Organization of Object Responses in Occipitotemporal Cortex}},
volume = {74},
year = {2012}
}
@article{Iwasato2000,
abstract = {The goal of this Challenge is to create a comprehensive list of equipment and processes used 
for con{\ldots}  ... Material supply of non-commercial compounds matching the shown core structure
is desired. The See{\ldots}  ... Correspondence to: Shigeyoshi Itohara 1 Correspondence and  ...},
author = {Iwasato, T and Datwani, A and Wolf, A M and et Al},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature 2000 Iwasato.pdf:pdf},
journal = {Nature},
month = {jan},
title = {{Cortex-restricted disruption of NMDAR1 impairs neuronal patterns in the barrel cortex}},
year = {2000}
}
@article{Hancock1996,
abstract = {Abstract Principal components analysis (PCA) of face images is here related to subjects' performance on the same images. In two experiments subjects were shown a set of faces and asked to rate them for distinctiveness. They were subsequently shown a superset of ... 
},
author = {Hancock, Peter J B and Burton, A Mike and Bruce, Vicki},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Memory & Cognition 1996 Hancock.pdf:pdf},
journal = {Mem. Cognit.},
month = {jan},
number = {1},
pages = {26--40},
title = {{Face processing: Human perception and principal components analysis}},
volume = {24},
year = {1996}
}
@article{Valpola2015,
abstract = {Ever since Hubel and Wiesel [1] published their findings about a hierarchy of increasingly abstract invariant visual features in the cat neocortex, researchers have been trying to mimic the same hierarchy of feature extraction stages with artificial neural networks. An early example is the neocognitron by Fukushima [2]. Nowadays the art of estimating deep feature extraction hierarchies is called deep learning (for a thorough overview of the history of the ...
},
author = {Valpola, H},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2015 Valpola.pdf:pdf},
journal = {Adv. Indep. Compon. Anal. {\ldots}},
month = {jan},
title = {{From neural PCA to deep unsupervised learning}},
year = {2015}
}
@article{Quaia2013,
author = {Quaia, C and Optican, L M and Cumming, B G},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2013 Quaia.pdf:pdf},
journal = {J. Neurosci.},
month = {nov},
number = {48},
pages = {18867--18879},
title = {{Terminator Disparity Contributes to Stereo Matching for Eye Movements and Perception}},
volume = {33},
year = {2013}
}
@article{Ghahramani2001,
author = {Ghahramani, Z and Beal, M J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Advances in neural information processing systems 2001 Ghahramani.pdf:pdf},
journal = {Adv. Neural Inf. Process. Syst.},
month = {jan},
pages = {507--513},
title = {{Propagation algorithms for variational Bayesian learning}},
year = {2001}
}
@article{Moser2008,
abstract = {More than three decades of research have demonstrated a role for hippocampal place cells in representation of the spatial environment in the brain. New studies have shown that place cells are part of a broader circuit for dynamic representation of self-location. A key component of this network is the entorhinal grid cells, which, by virtue of their tessellating firing fields, may provide the elements of a path integration-based neural map. Here we review how place cells and grid cells may form the basis for quantitative spatiotemporal representation of places, routes, and associated experiences during behavior and in memory. Because these cell types have some of the most conspicuous behavioral correlates among neurons in nonsensory cortical systems, and because their spatial firing structure reflects computations internally in the system, studies of entorhinal-hippocampal representations may offer considerable insight into general principles of cortical network dynamics. Copyright {\textcopyright} 2008 by Annual Reviews. All rights reserved.},
author = {Moser, Edvard I. and Kropff, Emilio and Moser, May Britt},
file = {:Users/hahosoya/Documents/Mendeley Desktop/ad6fb35857ea67e61d24b7dbcf07305113aaf229.pdf:pdf},
journal = {Annu. Rev. Neurosci.},
keywords = {Attractor,Entorhinal cortex,Hippocampus,Memory,Path integration,Phase precession},
pages = {69--89},
pmid = {18284371},
title = {{Place cells, grid cells, and the brain's spatial representation system}},
volume = {31},
year = {2008}
}
@article{Hinton2006,
abstract = {We show how to use “complementary priors” to eliminate the explaining- away effects that make 
inference difficult in densely connected belief nets that have many hidden layers. Using complementary
priors, we derive a fast , greedy algorithm that can learn deep , directed belief networks  ...},
author = {Hinton, Geoffrey E and et Al},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neural Computation 2006 Hinton.pdf:pdf},
journal = {Neural Comput.},
month = {jan},
title = {{A fast learning algorithm for deep belief nets}},
year = {2006}
}
@article{Ullman2002,
abstract = {A fundamental question in the study of visual processing is the problem of 'feature selection': which features of an image are extracted and represented by the visual cortex? Several brain areas are involved in visual object processing, and different features are represented at ...},
author = {Ullman, S and Vidal-Naquet, M},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature neuroscience 2002 Ullman-1.pdf:pdf},
journal = {Nat. Neurosci.},
month = {jan},
title = {{Visual features of intermediate complexity and their use in classification}},
year = {2002}
}
@article{Crespi2011,
abstract = {The neural substrate of the phenomenological experience of a stable visual world remains obscure. One possible mechanism would be to construct spatiotopic neural maps where the response is selective to the position of the stimulus in external space, rather than to retinal eccentricities, but evidence for these maps has been inconsistent. Here we show, with fMRI, that when human subjects perform concomitantly a demanding attentive task on stimuli displayed at the fovea, BOLD responses evoked by moving stimuli irrelevant to the task were mostly tuned in retinotopic coordinates. However, under more unconstrained conditions, where subjects could attend easily to the motion stimuli, BOLD responses were tuned not in retinal but in external coordinates (spatiotopic selectivity) in many visual areas, including MT, MST, LO and V6, agreeing with our previous fMRI study. These results indicate that spatial attention may play an important role in mediating spatiotopic selectivity.},
address = {Department of Psychology, Universit{\`{a}} Degli Studi di Firenze, Florence, Italy.},
author = {Crespi, Sofia and Biagi, Laura and D'Avossa, Giovanni and Burr, David C and Tosetti, Michela and Morrone, Maria Concetta},
file = {:Users/hahosoya/Documents/Mendeley Desktop/PLoS ONE 2011 Crespi.pdf:pdf},
journal = {PLoS One},
month = {jan},
number = {7},
pages = {e21661},
title = {{Spatiotopic coding of BOLD signal in human visual cortex depends on spatial attention.}},
volume = {6},
year = {2011}
}
@article{Olshausen2000,
abstract = {We describe a method for learning an overcomplete set of basis functions for the purpose of 
modeling sparse structure in images. The sparsity of the basis function coe cients is modeled
with a mixture-of-Gaussians distribution. One Gaussian captures non- active coe cients  ...},
author = {Olshausen, Bruno A and Millman, K Jarrod},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Advances in neural information processing {\ldots} 2000 Olshausen.pdf:pdf},
journal = {Adv. Neural Inf. Process. Syst.},
month = {jan},
title = {{Learning sparse codes with a mixture-of-Gaussians prior}},
year = {2000}
}
@article{Barlow1989,
abstract = {What use can the brain make of the massive flow of sensory information that occurs without any associated rewards or punishments? This question is reviewed in the light of connectionist models of unsupervised learning and some older ideas, namely the ... 
},
author = {Barlow, H B},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neural Computation 1989 Barlow.pdf:pdf},
journal = {Neural Comput.},
month = {sep},
number = {3},
pages = {295--311},
title = {{Unsupervised Learning}},
volume = {1},
year = {1989}
}
@article{Gilbert2013,
abstract = {Nature Reviews Neuroscience 14, 350 (2013). doi:10.1038/nrn3476},
author = {Gilbert, Charles D and Li, Wu},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2013 Gilbert.pdf:pdf},
month = {apr},
pages = {1--14},
title = {{Top-down influences on visual processing}},
year = {2013}
}
@article{Hyvarinen2001a,
author = {Hyv{\"{a}}rinen, Aapo and Hoyer, Patrik O},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Vision Research 2001 Hyv{\"{a}}rinen.pdf:pdf},
journal = {Vision Res.},
month = {jan},
number = {18},
pages = {2413--2423},
title = {{A two-layer sparse coding model learns simple and complex cell receptive fields and topography from natural images}},
volume = {41},
year = {2001}
}
@article{Raman2020,
abstract = {Recent computational studies have emphasized layer-wise quantitative similarity between convolutional neural networks (CNNs) and the primate visual ventral stream. However, whether such similarity holds for the face-selective areas, a subsystem of the higher visual cortex, is not clear. Here, we extensively investigate whether CNNs exhibit tuning properties as previously observed in different macaque face areas. While simulating four past experiments on a variety of CNN models, we sought for the model layer that quantitatively matches the multiple tuning properties of each face area. Our results show that higher model layers explain reasonably well the properties of anterior areas, while no layer simultaneously explains the properties of middle areas, consistently across the model variation. Thus, some similarity may exist between CNNs and the primate face-processing system in the near-goal representation, but much less clearly in the intermediate stages, thus requiring alternative modeling such as non-layer-wise correspondence or different computational principles.},
author = {Raman, Rajani and Hosoya, Haruo},
file = {:Users/hahosoya/Documents/Mendeley Desktop/s42003-020-0945-x.pdf:pdf},
journal = {Commun. Biol.},
number = {1},
pages = {1--14},
pmid = {32385392},
title = {{Convolutional neural networks explain tuning properties of anterior, but not middle, face-processing areas in macaque inferotemporal cortex}},
volume = {3},
year = {2020}
}
@article{DeValois1982,
abstract = {Quantitative data are presented on the orientation and direction specificity of the responses of cells in macaque monkey striate cortex. There is a bimodal distribution of direction-specific and nondirection-specific cells, with similar orientation tuning in each class. Cells range in orientation bandwidth at half amplitude from 6 degrees to 360 degrees (i.e. no orientation tuning), with a median near 40 degrees. Foveal-parafoveal and simple-complex subsamples show similar ranges of orientation bandwidths as well as similar medians (the bandwidths being somewhat broader than those found in cat cortex). The foveal subsample and a high-spatial-frequency subsample have more horizontal and vertical optimal orientations than oblique ones. Most cells show inhibition to some orientations, as well as excitation to others. Minimum-response orientations are generally less than 90 degrees from the optimal orientation--indicating maximum inhibition adjacent to the excitatory orientations. Three simple receptive field models are shown to differ in their abilities to account for these results.},
author = {{De Valois}, R L and Yund, E W and Hepler, N},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Vision Research 1982 De Valois-2.pdf:pdf},
journal = {Vision Res.},
month = {jan},
number = {5},
pages = {531--544},
title = {{The orientation and direction selectivity of cells in macaque visual cortex.}},
volume = {22},
year = {1982}
}
@article{Stahl2015,
abstract = {Given the overwhelming quantity of information available from the environment, how do young learners know what to learn about and what to ignore? We found that 11-month-old infants (N = 110) used violations of prior expectations as special opportunities for learning. The infants were shown events that violated expectations about object behavior or events that were nearly identical but did not violate expectations. The sight of an object that violated expectations enhanced learning and promoted information-seeking behaviors; specifically, infants learned more effectively about objects that committed violations, explored those objects more, and engaged in hypothesis-testing behaviors that reflected the particular kind of violation seen. Thus, early in life, expectancy violations offer a wedge into the problem of what to learn.},
address = {Department of Psychological & Brain Sciences, Johns Hopkins University, Baltimore, MD 21218, USA. astahl4@jhu.edu.},
author = {Stahl, Aimee E and Feigenson, Lisa},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Science 2015 Stahl.pdf:pdf},
journal = {Science},
month = {apr},
number = {6230},
pages = {91--94},
title = {{Cognitive development. Observing the unexpected enhances infants' learning and exploration.}},
volume = {348},
year = {2015}
}
@article{Schmid2014,
author = {Schmid, A M and Purpura, K P and Victor, J D},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2014 Schmid.pdf:pdf},
journal = {J. Neurosci.},
month = {mar},
number = {10},
pages = {3559--3578},
title = {{Responses to Orientation Discontinuities in V1 and V2: Physiological Dissociations and Functional Implications}},
volume = {34},
year = {2014}
}
@inproceedings{Vida2017,
author = {Vida, M D and Nestor, A and Plaut, D C},
booktitle = {Proc. {\ldots}},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2017 Vida.pdf:pdf},
month = {jan},
title = {{Spatiotemporal dynamics of similarity-based neural representations of facial identity}},
year = {2017}
}
@article{Bardy2006,
abstract = {In mammalian striate cortex (V1), two distinct functional classes of neurones, the so-called simple and complex cells, are routinely distinguished. They can be quantitatively differentiated from each other on the basis of the ratio between the phase-variant (F1) component and the ...},
author = {Bardy, C},
file = {:Users/hahosoya/Documents/Mendeley Desktop/The Journal of Physiology 2006 Bardy.pdf:pdf},
journal = {J. Physiol.},
month = {may},
number = {3},
pages = {731--750},
title = {{'Simplification' of responses of complex cells in cat striate cortex: suppressive surrounds and 'feedback' inactivation}},
volume = {574},
year = {2006}
}
@article{Hoyer2000,
abstract = {Previous work has shown that independent component analysis (ICA) applied to feature extraction from natural image data yields features resembling Gabor functions and simple-cell receptive fields. This article considers the effects of including chromatic and stereo information. The inclusion of colour leads to features divided into separate red/green, blue/yellow, and bright/dark channels. Stereo image data, on the other hand, leads to binocular receptive fields which are tuned to various disparities. The similarities between these results and the observed properties of simple cells in the primary visual cortex are further evidence for the hypothesis that visual cortical neurons perform some type of redundancy reduction, which was one of the original motivations for ICA in the first place. In addition, ICA provides a principled method for feature extraction from colour and stereo images; such features could be used in image processing operations such as denoising and compression, as well as in pattern recognition.},
address = {Neural Networks Research Centre, Helsinki University of Technology, Finland. patrik.hoyer@hut.fi},
author = {Hoyer, P O and Hyv{\"{a}}rinen, A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Network Computation in Neural Systems 2000 Hoyer.pdf:pdf},
journal = {Netw. Comput. Neural Syst.},
month = {aug},
number = {3},
pages = {191--210},
title = {{Independent component analysis applied to feature extraction from colour and stereo images.}},
volume = {11},
year = {2000}
}
@article{Kottur2017,
abstract = {A number of recent works have proposed techniques for end-to-end learning of communication protocols among cooperative multi-agent populations, and have simultaneously found the emergence of grounded human-interpretable language in the protocols developed by the agents, learned without any human supervision! In this paper, using a Task & Talk reference game between two agents as a testbed, we present a sequence of ‘negative' results culminating in a ‘positive' one – showing that while most agent-invented languages are effective (i.e. achieve near-perfect task rewards), they are decidedly not interpretable or compositional. In essence, we find that natural language does not emerge ‘naturally', despite the semblance of ease of natural-language-emergence that one may gather from recent literature. We discuss how it is possible to coax the invented languages to become more and more human-like and compositional by increasing restrictions on how two agents may communicate.},
archivePrefix = {arXiv},
arxivId = {1706.08502},
author = {Kottur, Satwik and Moura, Jos{\'{e}} M.F. and Lee, Stefan and Batra, Dhruv},
eprint = {1706.08502},
file = {:Users/hahosoya/Documents/Mendeley Desktop/dacab9f346413eea39e6e3381f4e26d2fa753a33.pdf:pdf},
isbn = {9781945626838},
journal = {EMNLP 2017 - Conf. Empir. Methods Nat. Lang. Process. Proc.},
pages = {2962--2967},
title = {{Natural language does not emerge ‘naturally' in multi-agent dialog}},
year = {2017}
}
@article{Salinas1995,
abstract = {During sensory-guided motor tasks, information must be transferred from arrays of neurons coding target location to motor networks that generate and control movement. We address two basic questions about this information transfer. First, what mechanisms assure that the different neural representations align properly so that activity in the sensory network representing target location evokes a motor response generating accurate movement toward the target? Coordinate transformations may be needed to put the sensory data into a form appropriate for use by the motor system. For example, in visually guided reaching the location of a target relative to the body is determined by a combination of the position of its image on the retina and the direction of gaze. What assures that the motor network responds to the appropriate combination of sensory inputs corresponding to target position in body- or arm-centered coordinates? To answer these questions, we model a sensory network coding target position and use it to drive a similarly modeled motor network. To determine the actual motor response we use decoding methods that have been developed and verified in experimental work. We derive a general set of conditions on the sensory-to-motor synaptic connections that assure a properly aligned and transformed response. The accuracy of the response for different numbers of coding cells is computed. We show that development of the synaptic weights needed to generate the correct motor response can occur spontaneously through the observation of random movements and correlation-based synaptic modification. No error signal or external teaching is needed during this process. We also discuss nonlinear coordinate transformations and the presence of both shifting and nonshifting receptive fields in sensory/motor systems.},
address = {Center for Complex Systems, Brandeis University, Waltham, Massachusetts 02254, USA.},
author = {Salinas, E and Abbott, L F},
file = {:Users/hahosoya/Documents/Mendeley Desktop/The Journal of neuroscience 1995 Salinas.pdf:pdf},
journal = {J. Neurosci.},
month = {oct},
number = {10},
pages = {6461--6474},
title = {{Transfer of coded information from sensory to motor networks.}},
volume = {15},
year = {1995}
}
@article{Pillow2006,
abstract = {Abstract We describe an information - theoretic framework for fitting neural spike responses with a Linear–Nonlinear–Poisson cascade model. This framework unifies the spike - triggered average (STA) and spike - triggered covariance (STC) approaches to neural  ...},
author = {Pillow, J W and et Al},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Vision 2006 Pillow.pdf:pdf},
journal = {J. Vis.},
month = {jan},
title = {{Dimensionality reduction in neural models: an information-theoretic generalization of spike-triggered average and covariance analysis}},
year = {2006}
}
@article{Betsch2004,
abstract = {Abstract. The mammalian visual system is one of the most intensively investigated sensory 
systems. However, our knowledge of the typical input it is operating on is surprisingly limited.
To address this issue, we seek to learn about the natural visual environment and the  ...},
author = {Betsch, B Y and Einh{\"{a}}user, W and et Al},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Biological Cybernetics 2004 Betsch.pdf:pdf},
journal = {Biol. Cybern.},
month = {jan},
title = {{The world from a cat's perspective–statistics of natural videos}},
year = {2004}
}
@article{Sinha,
author = {Sinha, P and Balas, B and Ostrovsky, Y and Russell, R},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proc. IEEE Sinha.pdf:pdf},
journal = {Proc. IEEE},
number = {11},
pages = {1948--1962},
title = {{Face Recognition by Humans: Nineteen Results All Computer Vision Researchers Should Know About}},
volume = {94}
}
@article{Huang2012,
abstract = {Abstract Unsupervised joint alignment of images has been demonstrated to improve performance on recognition tasks such as face verification. Such alignment reduces undesired variability due to factors such as pose, while only requiring weak supervision in ...
},
author = {Huang, G and Mattar, M and Lee, H},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Advances in neural information processing systems 2012 Huang.pdf:pdf},
journal = {Adv. Neural Inf. Process. Syst.},
month = {jan},
pages = {764--772},
title = {{Learning to align from scratch}},
year = {2012}
}
@article{Murphy1999,
abstract = {... of California, Berkeley, CA 94720 murphyk@cs.berkeley.edu Abstract We show how to use 
a variational approximation to the logistic function to perform approximate inference in
Bayesian networks containing dis- crete nodes with continuous parents. ...
},
author = {Murphy, K P},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proc. UAI 1999 Murphy.pdf:pdf},
journal = {Proc. UAI},
month = {jan},
title = {{A variational approximation for Bayesian networks with discrete and continuous latent variables}},
year = {1999}
}
@article{Bonini2014,
abstract = {Abstract The capacity to evaluate the outcomes of our actions is fundamental for adapting and optimizing behavior and depends on an action - monitoring system that assesses ongoing actions and detects errors. The neuronal network underlying this executive ... 
},
author = {Bonini, F and Burle, B and Li{\'{e}}geois-Chauvel, C and R{\'{e}}gis, J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Science 2014 Bonini.pdf:pdf},
journal = {Science},
month = {jan},
title = {{Action Monitoring and Medial Frontal Cortex: Leading Role of Supplementary Motor Area}},
year = {2014}
}
@article{Tajima1,
abstract = {Nature Communications, (2016). doi:10.1038/ncomms12400},
author = {Tajima, Satohiro and Drugowitsch, Jan and Pouget, Alexandre},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature Communications 1 Tajima.pdf:pdf},
journal = {Nat. Commun.},
month = {jan},
pages = {1--12},
title = {{Optimal policy for value-based decision-making}},
volume = {7},
year = {1}
}
@article{Whittington2020,
abstract = {The hippocampal-entorhinal system is important for spatial and relational memory tasks. We formally link these domains, provide a mechanistic understanding of the hippocampal role in generalization, and offer unifying principles underlying many entorhinal and hippocampal cell types. We propose medial entorhinal cells form a basis describing structural knowledge, and hippocampal cells link this basis with sensory representations. Adopting these principles, we introduce the Tolman-Eichenbaum machine (TEM). After learning, TEM entorhinal cells display diverse properties resembling apparently bespoke spatial responses, such as grid, band, border, and object-vector cells. TEM hippocampal cells include place and landmark cells that remap between environments. Crucially, TEM also aligns with empirically recorded representations in complex non-spatial tasks. TEM also generates predictions that hippocampal remapping is not random as previously believed; rather, structural knowledge is preserved across environments. We confirm this structural transfer over remapping in simultaneously recorded place and grid cells.},
author = {Whittington, James C.R. and Muller, Timothy H. and Mark, Shirley and Chen, Guifen and Barry, Caswell and Burgess, Neil and Behrens, Timothy E.J.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/1-s2.0-S009286742031388X-main-2.pdf:pdf},
journal = {Cell},
keywords = {entorhinal cortex,generalization,grid cells,hippocampus,neural networks,non-spatial reasoning,place cells,representation learning},
number = {5},
pages = {1249--1263.e23},
pmid = {33181068},
title = {{The Tolman-Eichenbaum Machine: Unifying Space and Relational Memory through Generalization in the Hippocampal Formation}},
volume = {183},
year = {2020}
}
@article{Sønderby2016,
abstract = {Abstract Variational autoencoders are a powerful framework for unsupervised learning. However, previous work has been restricted to shallow models with one or two layers of fully factorized stochastic latent variables, limiting the flexibility of the latent representation. We propose three advances in training algorithms of variational autoencoders, for the first time allowing to train deep models of up to five stochastic layers,(1) using a structure similar to ...
},
author = {S{\o}nderby, C Kaae and Raiko, T and Maal{\o}e, L},
file = {:Users/hahosoya/Documents/Mendeley Desktop/arXiv 2016 S{\o}nderby.pdf:pdf},
journal = {arXiv.org},
month = {jan},
title = {{How to Train Deep Variational Autoencoders and Probabilistic Ladder Networks}},
year = {2016}
}
@article{Destexhe2006,
author = {Destexhe, A and Contreras, D},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Science 2006 Destexhe.pdf:pdf},
journal = {Science},
month = {oct},
number = {5796},
pages = {85--90},
title = {{Neuronal Computations with Stochastic Network States}},
volume = {314},
year = {2006}
}
@article{Li2017,
abstract = {When the corresponding retinal locations in the two eyes are presented with incompatible images, a stable percept gives way to perceptual alternations in which the two images compete for perceptual dominance. As perceptual experience evolves dynamically under constant external inputs, binocular rivalry has been used for studying intrinsic cortical computations and for understanding how the brain regulates competing inputs. Converging behavioral and EEG results have shown that binocular rivalry and attention are intertwined: binocular rivalry ceases when attention is diverted away from the rivalry stimuli. In addition, the competing image in one eye suppresses the target in the other eye through a pattern of gain changes similar to those induced by attention. These results require a revision of the current computational theories of binocular rivalry, in which the role of attention is ignored. Here, we provide a computational model of binocular rivalry. In the model, competition between two images in rivalry is driven by both attentional modulation and mutual inhibition, which have distinct selectivity (feature vs. eye of origin) and dynamics (relatively slow vs. relatively fast). The proposed model explains a wide range of phenomena reported in rivalry, including the three hallmarks: (i) binocular rivalry requires attention; (ii) various perceptual states emerge when the two images are swapped between the eyes multiple times per second; (iii) the dominance duration as a function of input strength follows Levelt's propositions. With a bifurcation analysis, we identified the parameter space in which the model's behavior was consistent with experimental results.},
address = {Department of Psychology, New York University, New York, NY 10003; hsin.hung.li@nyu.edu david.heeger@nyu.edu.},
author = {Li, Hsin-Hung and Rankin, James and Rinzel, John and Carrasco, Marisa and Heeger, David J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/D263F854-8727-4D40-9FF3-49FD2424E977.pdf:pdf},
journal = {Proc. Natl. Acad. Sci.},
month = {jul},
number = {30},
pages = {E6192--E6201},
title = {{Attention model of binocular rivalry.}},
volume = {114},
year = {2017}
}
@article{Inamura2004,
author = {Inamura, Tetsunari and Toshima, Iwaki and Tanie, Hiroaki and Nakamura, Yoshihiko},
file = {:Users/hahosoya/Documents/Mendeley Desktop/int j robot res 2004 Inamura.pdf:pdf},
journal = {Int. J. Rob. Res.},
month = {apr},
number = {4},
pages = {363--377},
title = {{Embodied Symbol Emergence Based on Mimesis Theory}},
volume = {23},
year = {2004}
}
@article{Madipakkam2015,
author = {Madipakkam, A R and Rothkirch, M and Guggenmos, M and Heinz, A and Sterzer, P},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2015 Madipakkam.pdf:pdf},
journal = {J. Neurosci.},
month = {sep},
number = {39},
pages = {13287--13299},
title = {{Gaze Direction Modulates the Relation between Neural Responses to Faces and Visual Awareness}},
volume = {35},
year = {2015}
}
@article{Hong2016,
abstract = {Extensive research has revealed that the ventral visual stream hierarchically builds a robust representation for supporting visual object categorization tasks. We systematically explored the ability of multiple ventral visual areas to support a variety of 'category-orthogonal' object properties such as position, size and pose. For complex naturalistic stimuli, we found that the inferior temporal (IT) population encodes all measured category-orthogonal object properties, including those properties often considered to be low-level features (for example, position), more explicitly than earlier ventral stream areas. We also found that the IT population better predicts human performance patterns across properties. A hierarchical neural network model based on simple computational principles generates these same cross-area patterns of information. Taken together, our empirical results support the hypothesis that all behaviorally relevant object properties are extracted in concert up the ventral visual hierarchy, and our computational model explains how that hierarchy might be built.},
address = {Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.},
author = {Hong, Ha and Yamins, Daniel L K and Majaj, Najib J and DiCarlo, James J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature neuroscience 2016 Hong-1.pdf:pdf;:Users/hahosoya/Documents/Mendeley Desktop/41593_2016_BFnn4247_MOESM13_ESM.pdf:pdf},
journal = {Nat. Neurosci.},
month = {apr},
number = {4},
pages = {613--622},
title = {{Explicit information for category-orthogonal object properties increases along the ventral stream.}},
volume = {19},
year = {2016}
}
@article{VandeVen2020,
abstract = {Artificial neural networks suffer from catastrophic forgetting. Unlike humans, when these networks are trained on something new, they rapidly forget what was learned before. In the brain, a mechanism thought to be important for protecting memories is the reactivation of neuronal activity patterns representing those memories. In artificial neural networks, such memory replay can be implemented as ‘generative replay', which can successfully – and surprisingly efficiently – prevent catastrophic forgetting on toy examples even in a class-incremental learning scenario. However, scaling up generative replay to complicated problems with many tasks or complex inputs is challenging. We propose a new, brain-inspired variant of replay in which internal or hidden representations are replayed that are generated by the network's own, context-modulated feedback connections. Our method achieves state-of-the-art performance on challenging continual learning benchmarks (e.g., class-incremental learning on CIFAR-100) without storing data, and it provides a novel model for replay in the brain.},
author = {van de Ven, Gido M. and Siegelmann, Hava T. and Tolias, Andreas S.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/s41467-020-17866-2.pdf:pdf},
journal = {Nat. Commun.},
number = {1},
pmid = {32792531},
title = {{Brain-inspired replay for continual learning with artificial neural networks}},
volume = {11},
year = {2020}
}
@article{Aitchison2021,
abstract = {Learning, especially rapid learning, is critical for survival. However, learning is hard; a large number of synaptic weights must be set based on noisy, often ambiguous, sensory information. In such a high-noise regime, keeping track of probability distributions over weights is the optimal strategy. Here we hypothesize that synapses take that strategy; in essence, when they estimate weights, they include error bars. They then use that uncertainty to adjust their learning rates, with more uncertain weights having higher learning rates. We also make a second, independent, hypothesis: synapses communicate their uncertainty by linking it to variability in postsynaptic potential size, with more uncertainty leading to more variability. These two hypotheses cast synaptic plasticity as a problem of Bayesian inference, and thus provide a normative view of learning. They generalize known learning rules, offer an explanation for the large variability in the size of postsynaptic potentials and make falsifiable experimental predictions.},
archivePrefix = {arXiv},
arxivId = {1410.1029},
author = {Aitchison, Laurence and Jegminat, Jannes and Menendez, Jorge Aurelio and Pfister, Jean Pascal and Pouget, Alexandre and Latham, Peter E.},
eprint = {1410.1029},
file = {:Users/hahosoya/Documents/Mendeley Desktop/s41593-021-00809-5.pdf:pdf},
journal = {Nat. Neurosci.},
number = {4},
pages = {565--571},
pmid = {33707754},
title = {{Synaptic plasticity as Bayesian inference}},
volume = {24},
year = {2021}
}
@article{Hirabayashi2014,
abstract = {Trends in Neurosciences, 37 (2014) 178-187. doi:10.1016/j.tins.2014.01.002},
author = {Hirabayashi, Toshiyuki and Miyashita, Yasushi},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Trends in Neurosciences 2014 Hirabayashi.pdf:pdf},
journal = {Trends Neurosci.},
month = {mar},
number = {3},
pages = {178--187},
title = {{Computational principles of microcircuits for visual object processing in the macaque temporal cortex}},
volume = {37},
year = {2014}
}
@article{Swersky2010,
abstract = {Abstract—In this study, we provide a direct comparison of the Stochastic Maximum Likelihood 
algorithm and Contrastive Divergence for training Restricted Boltzmann Machines using the
MNIST data set. We demonstrate that Stochastic Maximum Likelihood is superior when  ...},
author = {Swersky, K and Chen, B and et Al},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Information Theory and {\ldots} 2010 Swersky.pdf:pdf},
journal = {Inf. Theory  {\ldots}},
month = {jan},
title = {{A tutorial on stochastic approximation algorithms for training restricted Boltzmann machines and deep belief nets}},
year = {2010}
}
@article{Logothetis1995,
abstract = {BACKGROUND:The inferior temporal cortex (IT) of the monkey has long been known to play an essential role in visual object recognition. Damage to this area results in severe deficits in perceptual learning and object recognition, without significantly affecting basic visual capacities. Consistent with these ablation studies is the discovery of IT neurons that respond to complex two-dimensional visual patterns, or objects such as faces or body parts. What is the role of these neurons in object recognition? Is such a complex configurational selectivity specific to biologically meaningful objects, or does it develop as a result of extensive exposure to any objects whose identification relies on subtle shape differences? If so, would IT neurons respond selectively to recently learned views of features of novel objects? The present study addresses this question by using combined psychophysical and electrophysiological experiments, in which monkeys learned to classify and recognize computer-generated three-dimensional objects.

RESULTS:A population of IT neurons was found that responded selectively to views of previously unfamiliar objects. The cells discharged maximally to one view of an object, and their response declined gradually as the object was rotated away from this preferred view. No selective responses were ever encountered for views that the animal systematically failed to recognize. Most neurons also exhibited orientation-dependent responses during view-plane rotations. Some neurons were found to be tuned around two views of the same object, and a very small number of cells responded in a view-invariant manner. For the five different objects that were used extensively during the training of the animals, and for which behavioral performance became view-independent, multiple cells were found that were tuned around different views of the same object. A number of view-selective units showed response invariance for changes in the size of the object or the position of its image within the parafovea.

CONCLUSION:Our results suggest that IT neurons can develop a complex receptive field organization as a consequence of extensive training in the discrimination and recognition of objects. None of these objects had any prior meaning for the animal, nor did they resemble anything familiar in the monkey's environment. Simple geometric features did not appear to account for the neurons' selective responses. These findings support the idea that a population of neurons--each tuned to a different object aspect, and each showing a certain degree of invariance to image transformations--may, as an ensemble, encode at least some types of complex three-dimensional objects. In such a system, several neurons may be active for any given vantage point, with a single unit acting like a blurred template for a limited neighborhood of a single view.},
address = {Division of Neuroscience, Baylor College of Medicine, Houston, Texas 77030, USA.},
author = {Logothetis, N K and Pauls, J and Poggio, T},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Curr. Biol. 1995 Logothetis.pdf:pdf},
journal = {Curr. Biol.},
month = {may},
number = {5},
pages = {552--563},
title = {{Shape representation in the inferior temporal cortex of monkeys.}},
volume = {5},
year = {1995}
}
@article{Hoyer2002,
author = {Hoyer, Patrik O and Hyv{\"{a}}rinen, Aapo},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Vision Research 2002 Hoyer.pdf:pdf},
journal = {Vision Res.},
month = {jan},
number = {12},
pages = {1593--1605},
title = {{A multi-layer sparse coding network learns contour coding from natural images}},
volume = {42},
year = {2002}
}
@inproceedings{Cheung2014,
abstract = {Deep learning has enjoyed a great deal of success because of its ability to learn useful features for tasks such as classification. But there has been less exploration in learning the factors of variation apart from the classification signal. By augmenting autoencoders with simple regularization terms during training, we demonstrate that standard deep architectures can discover and explicitly represent factors of variation beyond those relevant for categorization. We introduce a cross-covariance penalty (XCov) as a method to disentangle factors like handwriting style for digits and subject identity in faces. We demonstrate this on the MNIST handwritten digit database, the Toronto Faces Database (TFD) and the Multi-PIE dataset by generating manipulated instances of the data. Furthermore, we demonstrate these deep networks can extrapolate `hidden' variation in the supervised signal.},
author = {Cheung, Brian and Livezey, Jesse A and Bansal, Arjun K and Olshausen, Bruno A},
booktitle = {Int. Conf. Learn. Represent. Work.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2014 Cheung.pdf:pdf},
month = {dec},
title = {{Discovering Hidden Factors of Variation in Deep Networks}},
volume = {cs.LG},
year = {2014}
}
@article{Epshtein2008,
abstract = {The human visual system recognizes objects and their constituent parts rapidly and with high accuracy. Standard models of recognition by the visual cortex use feed-forward processing, in which an object's parts are detected before the complete object. However, parts are often ...},
author = {Epshtein, B and Lifshitz, I and Ullman, S},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proceedings of the National Academy of Sciences 2008 Epshtein.pdf:pdf},
journal = {Proc. Natl. Acad. Sci.},
month = {sep},
number = {38},
pages = {14298--14303},
title = {{Image interpretation by a single bottom-up top-down cycle}},
volume = {105},
year = {2008}
}
@article{2014,
author = {裕貴, 寺島},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2014 裕貴.pdf:pdf},
month = {dec},
pages = {1--5},
title = {脳の画像・音声処理戦略を解き明かすスパースモデリング},
year = {2014}
}
@article{Nandy2013,
abstract = {Neuron, 78 (2013) 1102-1115. doi:10.1016/j.neuron.2013.04.016},
author = {Nandy, Anirvan S and Sharpee, Tatyana O and Reynolds, John H and Mitchell, Jude F},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neuron 2013 Nandy.pdf:pdf},
journal = {Neuron},
month = {jun},
number = {6},
pages = {1102--1115},
title = {{The Fine Structure of Shape Tuning in Area V4}},
volume = {78},
year = {2013}
}
@article{Coates2012,
abstract = {Abstract Recent work in unsupervised feature learning has focused on the goal of discovering high-level features from unlabeled images. Much progress has been made in this direction, but in most cases it is still standard to use a large amount of labeled data in ... 
},
author = {Coates, A and Karpathy, A and Ng, A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Advances in neural information {\ldots} 2012 Coates.pdf:pdf},
journal = {Adv. Neural Inf. {\ldots}},
month = {jan},
title = {{Emergence of Object-Selective Features in Unsupervised Feature Learning}},
year = {2012}
}
@article{Palva2013,
author = {Palva, J Matias and Zhigalov, Alexander and Hirvonen, Jonni and Korhonen, Onerva and Linkenkaer-Hansen, Klaus and Palva, Satu},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proc. Natl. Acad. Sci. U.S.A. 2013 Palva.pdf:pdf},
journal = {Proc. Natl. Acad. Sci. U. S. A.},
month = {jan},
number = {9},
pages = {3585--3590},
title = {{Neuronal long-range temporal correlations and avalanche dynamics are correlated with behavioral scaling laws}},
volume = {110},
year = {2013}
}
@article{Steimer2013,
author = {Steimer, Andreas and Douglas, Rodney},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neural Computation 2013 Steimer.pdf:pdf},
journal = {Neural Comput.},
month = {sep},
number = {9},
pages = {2303--2354},
title = {{Spike-Based Probabilistic Inference in Analog Graphical Models Using Interspike-Interval Coding}},
volume = {25},
year = {2013}
}
@book{Hyvarinen2009,
author = {Hyv{\"{a}}rinen, Aapo and Hurri, Jarmo and Hoyer, Patrik O},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2009 Hyv{\"{a}}rinen.pdf:pdf},
isbn = {1848824912},
month = {jan},
title = {{Natural Image Statistics: A probabilistic approach to early computational vision}},
volume = {39},
year = {2009}
}
@article{Ba2016,
abstract = {Until recently, research on artificial neural networks was largely restricted to systems with only two types of variable: Neural activities that represent the current or recent input and weights that learn to capture regularities among inputs, outputs and payoffs. There is no good reason for this restriction. Synapses have dynamics at many different time-scales and this suggests that artificial neural networks might benefit from variables that change slower than activities but much faster than the standard weights. These "fast weights" can be used to store temporary memories of the recent past and they provide a neurally plausible way of implementing the type of attention to the past that has recently proved very helpful in sequence-to-sequence models. By using fast weights we can avoid the need to store copies of neural activity patterns.},
archivePrefix = {arXiv},
arxivId = {1610.06258},
author = {Ba, Jimmy and Hinton, Geoffrey and Mnih, Volodymyr and Leibo, Joel Z. and Ionescu, Catalin},
eprint = {1610.06258},
file = {:Users/hahosoya/Documents/Mendeley Desktop/623b3a128eef6a0e23977b52c8381755edb91098.pdf:pdf},
journal = {Adv. Neural Inf. Process. Syst.},
pages = {4338--4346},
title = {{Using fast weights to attend to the recent past}},
year = {2016}
}
@article{Vinje2000,
abstract = {Theoretical studies suggest that primary visual cortex (area V1) uses a sparse code to efficiently represent natural scenes. This issue was investigated by recording from V1 neurons in awake behaving macaques during both free viewing of natural scenes and conditions simulating natural vision. Stimulation of the nonclassical receptive field increases the selectivity and sparseness of individual V1 neurons, increases the sparseness of the population response distribution, and strongly decorrelates the responses of neuron pairs. These effects are due to both excitatory and suppressive modulation of the classical receptive field by the nonclassical receptive field and do not depend critically on the spatiotemporal structure of the stimuli. During natural vision, the classical and nonclassical receptive fields function together to form a sparse representation of the visual world. This sparse code may be computationally efficient for both early vision and higher visual processing.},
address = {Program in Neuroscience, Department of Molecular and Cellular Biology, and Department of Psychology, University of California at Berkeley, Berkeley, CA 94720-1650, USA.},
author = {Vinje, W E and Gallant, J L},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Science 2000 Vinje.pdf:pdf},
journal = {Science},
month = {feb},
number = {5456},
pages = {1273--1276},
title = {{Sparse coding and decorrelation in primary visual cortex during natural vision.}},
volume = {287},
year = {2000}
}
@article{Palmer1987,
abstract = {JOURNALOFNEUROPHYSIOLOGY Vol. 58, No. 6, December 1987. Printed in USA ... An Evaluation of the Two-Dimensional Gabor Filter ... Model of Simple Receptive Fields in Cat Striate Cortex ... JUDSON P. JONES AND LARRY A. PALMER Department of Anatomy, and David ...},
author = {Palmer, L A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neurophysiology 1987 Palmer-2.pdf:pdf},
journal = {J. Neurophysiol.},
month = {jan},
title = {{An evaluation of the two-dimensional Gabor filter model of simple receptive fields in cat striate cortex}},
year = {1987}
}
@article{Rajaei2019,
abstract = {Core object recognition, the ability to rapidly recognize objects despite variations in their appearance, is largely solved through the feedforward processing of visual information. Deep neural networks are shown to achieve human-level performance in these tasks, and explain the primate brain representation. On the other hand, object recognition under more challenging conditions (i.e. beyond the core recognition problem) is less characterized. One such example is object recognition under occlusion. It is unclear to what extent feedforward and recurrent processes contribute in object recognition under occlusion. Furthermore, we do not know whether the conventional deep neural networks, such as AlexNet, which were shown to be successful in solving core object recognition, can perform similarly well in problems that go beyond the core recognition. Here, we characterize neural dynamics of object recognition under occlusion, using magnetoencephalography (MEG), while participants were presented with images of objects with various levels of occlusion. We provide evidence from multivariate analysis of MEG data, behavioral data, and computational modelling, demonstrating an essential role for recurrent processes in object recognition under occlusion. Furthermore, the computational model with local recurrent connections, used here, suggests a mechanistic explanation of how the human brain might be solving this problem.},
author = {Rajaei, Karim and Mohsenzadeh, Yalda and Ebrahimpour, Reza and Khaligh-Razavi, Seyed Mahdi},
file = {:Users/hahosoya/Documents/Mendeley Desktop/journal.pcbi.1007001.pdf:pdf},
isbn = {1111111111},
journal = {PLoS Comput. Biol.},
number = {5},
pages = {1--30},
title = {{Beyond core object recognition: Recurrent processes account for object recognition under occlusion}},
volume = {15},
year = {2019}
}
@article{Askenfelt1991,
author = {Askenfelt, Anders and Jansson, Erik V},
file = {:Users/hahosoya/Documents/Mendeley Desktop/J. Acoust. Soc. Am. 1991 Askenfelt.pdf:pdf},
journal = {J. Acoust. Soc. Am.},
month = {jan},
number = {5},
pages = {2383--2393},
title = {{From touch to string vibrations. II: The motion of the key and hammer}},
volume = {90},
year = {1991}
}
@article{Diaz2016,
abstract = {Nature 529, 167 (2016). doi:10.1038/nature16489},
author = {D{\'{i}}az, Sandra and Kattge, Jens and Cornelissen, Johannes H C and Wright, Ian J and Lavorel, Sandra and Dray, St{\'{e}}phane and Reu, Bj{\"{o}}rn and Kleyer, Michael and Wirth, Christian and Prentice, I Colin and Garnier, Eric and B{\"{o}}nisch, Gerhard and Westoby, Mark and Poorter, Hendrik and Reich, Peter B and Moles, Angela T and Dickie, John and Gillison, Andrew N and Zanne, Amy E and Chave, J{\'{e}}r{\^{o}}me and Wright, S Joseph and Sheremet'ev, Serge N and Jactel, Herv{\'{e}} and Baraloto, Christopher and Cerabolini, Bruno and Pierce, Simon and Shipley, Bill and Kirkup, Donald and Casanoves, Fernando and Joswig, Julia S and G{\"{u}}nther, Angela and Falczuk, Valeria and R{\"{u}}ger, Nadja and Mahecha, Miguel D and Gorn{\'{e}}, Lucas D},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature 2016 D{\'{i}}az.pdf:pdf},
journal = {Nature},
month = {jan},
number = {7585},
pages = {167--171},
title = {{The global spectrum of plant form and function}},
volume = {529},
year = {2016}
}
@article{Danjo2018a,
abstract = {An animal's awareness of its location in space depends on the activity of place cells in the hippocampus. How the brain encodes the spatial position of others has not yet been identified. We investigated neuronal representations of other animals' locations in the dorsal CA1 region of the hippocampus with an observational T-maze task in which one rat was required to observe another rat's trajectory to successfully retrieve a reward. Information reflecting the spatial location of both the self and the other was jointly and discretely encoded by CA1 pyramidal cells in the observer rat. A subset of CA1 pyramidal cells exhibited spatial receptive fields that were identical for the self and the other. These findings demonstrate that hippocampal spatial representations include dimensions for both self and nonself.},
address = {Laboratory for Systems Neurophysiology, RIKEN Brain Science Institute, 2-1 Hirosawa, Wako, Saitama, 351-0198, Japan.},
author = {Danjo, Teruko and Toyoizumi, Taro and Fujisawa, Shigeyoshi},
file = {:Users/hahosoya/Documents/Mendeley Desktop/06B6ECC3-42DA-444C-9326-94CC53CF18E2.pdf:pdf},
journal = {Science},
month = {jan},
number = {6372},
pages = {213--218},
title = {{Spatial representations of self and other in the hippocampus.}},
volume = {359},
year = {2018}
}
@article{Dosovitskiy2016,
abstract = {We propose a class of loss functions, which we call deep perceptual similarity metrics (DeePSiM), allowing to generate sharp high resolution images from compressed abstract representations. Instead of computing distances in the image space, we compute distances between image features extracted by deep neural networks. This metric reflects perceptual similarity of images much better and, thus, leads to better results. We demonstrate two examples of use cases of the proposed loss:(1) networks that invert the AlexNet {\ldots}
},
author = {Dosovitskiy, Alexey and Brox, Thomas},
file = {:Users/hahosoya/Documents/Mendeley Desktop/1C0D7726-2226-4C94-8D09-F721411BA933.pdf:pdf},
journal = {NIPS},
month = {jan},
pages = {658--666},
title = {{Generating Images with Perceptual Similarity Metrics based on Deep Networks}},
year = {2016}
}
@article{Stensola2012,
abstract = {Nature 492, 72 (2012). doi:10.1038/nature11649},
author = {Stensola, Hanne and Stensola, Tor and Solstad, Trygve and Fr{\o}land, Kristian and Moser, May-Britt and Moser, Edvard I},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature 2012 Stensola.pdf:pdf},
journal = {Nature},
month = {nov},
number = {7427},
pages = {72--78},
title = {{The entorhinal grid map is discretized}},
volume = {492},
year = {2012}
}
@article{Mordatch2018,
abstract = {By capturing statistical patterns in large corpora, machine learning has enabled significant advances in natural language processing, including in machine translation, question answering, and sentiment analysis. However, for agents to intelligently interact with humans, simply capturing the statistical patterns is insufficient. In this paper we investigate if, and how, grounded compositional language can emerge as a means to achieve goals in multi-agent populations. Towards this end, we propose a multi-agent learning environment and learning methods that bring about emergence of a basic compositional language. This language is represented as streams of abstract discrete symbols uttered by agents over time, but nonetheless has a coherent structure that possesses a defined vocabulary and syntax. We also observe emergence of nonverbal communication such as pointing and guiding when language communication is unavailable.},
archivePrefix = {arXiv},
arxivId = {1703.04908},
author = {Mordatch, Igor and Abbeel, Pieter},
eprint = {1703.04908},
file = {:Users/hahosoya/Documents/Mendeley Desktop/78997e1c5c25382c0c66ca78cf4173c224d52a51.pdf:pdf},
isbn = {9781577358008},
journal = {32nd AAAI Conf. Artif. Intell. AAAI 2018},
pages = {1495--1502},
title = {{Emergence of grounded compositional language in multi-agent populations}},
year = {2018}
}
@article{Habenschuss2013,
abstract = {The brain faces the problem of inferring reliable hidden causes from large populations of noisy neurons, for example, the direction of a moving object from spikes in area MT. It is known that a theoretically optimal likelihood decoding could be carried out by simple linear readout neurons if weights of synaptic connections were set to certain values that depend on the tuning functions of sensory neurons. We show here that such theoretically optimal readout weights emerge autonomously through STDP in conjunction with lateral inhibition between readout neurons. In particular, we identify a class of optimal STDP learning rules with homeostatic plasticity, for which the autonomous emergence of optimal readouts can be explained on the basis of a rigorous learning theory. This theory shows that the network motif we consider approximates expectation-maximization for creating internal generative models for hidden causes of high-dimensional spike inputs. Notably, we find that this optimal functionality can be well approximated by a variety of STDP rules beyond those predicted by theory. Furthermore, we show that this learning process is very stable and automatically adjusts weights to changes in the number of readout neurons, the tuning functions of sensory neurons, and the statistics of external stimuli.},
address = {Institute for Theoretical Computer Science, Graz University of Technology, A-8010 Graz, Austria. habenschuss@igi.tugraz.at.},
author = {Habenschuss, Stefan and Puhr, Helmut and Maass, Wolfgang},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neural Computation 2013 Habenschuss.pdf:pdf},
journal = {Neural Comput.},
month = {mar},
title = {{Emergence of Optimal Decoding of Population Codes Through STDP.}},
year = {2013}
}
@article{Buiatti2019,
author = {Buiatti, Marco and {Di Giorgio}, Elisa and Piazza, Manuela and Polloni, Carlo and Menna, Giuseppe and Taddei, Fabrizio and Baldo, Ermanno and Vallortigara, Giorgio},
file = {:Users/hahosoya/Documents/Mendeley Desktop/4035012E-9839-44E8-B29F-3F10003C9609.pdf:pdf},
journal = {Proc. Natl. Acad. Sci. U. S. A.},
month = {mar},
number = {10},
pages = {4625--4630},
title = {{Cortical route for facelike pattern processing in human newborns}},
volume = {116},
year = {2019}
}
@article{Khaligh-Razavi2017,
abstract = {Studies of the primate visual system have begun to test a wide range of complex computational object-vision models. Realistic models have many parameters, which in practice cannot be fitted using the limited amounts of brain-activity data typically available. Task performance optimization (e.g. using backpropagation to train neural networks) provides major constraints for fitting parameters and discovering nonlinear representational features appropriate for the task (e.g. object classification). Model representations can be compared to brain representations in terms of the representational dissimilarities they predict for an image set. This method, called representational similarity analysis (RSA), enables us to test the representational feature space as is (fixed RSA) or to fit a linear transformation that mixes the nonlinear model features so as to best explain a cortical area's representational space (mixed RSA). Like voxel/population-receptive-field modelling, mixed RSA uses a training set (different stimuli) to fit one weight per model feature and response channel (voxels here), so as to best predict the response profile across images for each response channel. We analysed response patterns elicited by natural images, which were measured with functional magnetic resonance imaging (fMRI). We found that early visual areas were best accounted for by shallow models, such as a Gabor wavelet pyramid (GWP). The GWP model performed similarly with and without mixing, suggesting that the original features already approximated the representational space, obviating the need for mixing. However, a higher ventral-stream visual representation (lateral occipital region) was best explained by the higher layers of a deep convolutional network and mixing of its feature set was essential for this model to explain the representation. We suspect that mixing was essential because the convolutional network had been trained to discriminate a set of 1000 categories, whose frequencies in the training set did not match their frequencies in natural experience or their behavioural importance. The latter factors might determine the representational prominence of semantic dimensions in higher-level ventral-stream areas. Our results demonstrate the benefits of testing both the specific representational hypothesis expressed by a model's original feature space and the hypothesis space generated by linear transformations of that feature space.},
author = {Khaligh-Razavi, Seyed Mahdi and Henriksson, Linda and Kay, Kendrick and Kriegeskorte, Nikolaus},
file = {:Users/hahosoya/Documents/Mendeley Desktop/1-s2.0-S0022249616301134-main.pdf:pdf},
journal = {J. Math. Psychol.},
keywords = {Deep convolutional networks,Mixed RSA,Object-vision models,Representational similarity analysis,Voxel-receptive-field modelling},
pages = {184--197},
title = {{Fixed versus mixed RSA: Explaining visual representations by fixed and mixed feature sets from shallow and deep computational models}},
volume = {76},
year = {2017}
}
@article{Henaff2015,
abstract = {We develop a new method for visualizing and refining the invariances of learned representations. Specifically, we test for a general form of invariance, linearization, in which the action of a transformation is confined to a low-dimensional subspace. Given two reference images (typically, differing by some transformation), we synthesize a sequence of images lying on a path between them that is of minimal length in the space of the representation (a "representational geodesic"). If the transformation relating the two reference images is linearized by the representation, this sequence should follow the gradual evolution of this transformation. We use this method to assess the invariance properties of a state-of-the-art image classification network and find that geodesics generated for image pairs differing by translation, rotation, and dilation do not evolve according to their associated transformations. Our method also suggests a remedy for these failures, and following this prescription, we show that the modified representation is able to linearize a variety of geometric image transformations.},
author = {H{\'{e}}naff, Olivier J and Simoncelli, Eero P},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2016-3.pdf:pdf},
journal = {arXiv.org},
month = {nov},
title = {{Geodesics of learned representations}},
volume = {cs.CV},
year = {2015}
}
@article{Boureau2010,
abstract = {Abstract Many successful models for scene or object recognition transform low- level descriptors (such as Gabor filter responses, or SIFT descriptors) into richer representations of intermediate complexity. This process can often be broken down into two steps:(1) a ... 
},
author = {Boureau, Y L and Bach, F and LeCun, Y},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2010 Boureau.pdf:pdf},
journal = {{\ldots} Pattern Recognit. {\ldots}},
month = {jan},
title = {{ieee xplore learning mid level features for recognition - Google 検索}},
year = {2010}
}
@article{Friston2003,
abstract = {This article is about how the brain data mines its sensory inputs. There are several architectural principles of functional brain anatomy that have emerged from careful anatomic and physiologic studies over the past century. These principles are considered in the light of representational learning to see if they could have been predicted a priori on the basis of purely theoretical considerations. We first review the organisation of hierarchical sensory cortices, paying special attention to the distinction between forward and backward connections. We then review various approaches to representational learning as special cases of generative models, starting with supervised learning and ending with learning based upon empirical Bayes. The latter predicts many features, such as a hierarchical cortical system, prevalent top-down backward influences and functional asymmetries between forward and backward connections that are seen in the real brain. The key points made in article are: (i). hierarchical generative models enable the learning of empirical priors and eschew prior assumptions about the causes of sensory input that are inherent in non-hierarchical models. These assumptions are necessary for learning schemes based on information theory and efficient or sparse coding, but are not necessary in a hierarchical context. Critically, the anatomical infrastructure that may implement generative models in the brain is hierarchical. Furthermore, learning based on empirical Bayes can proceed in a biologically plausible way. (ii). The second point is that backward connections are essential if the processes generating inputs cannot be inverted, or the inversion cannot be parameterised. Because these processes involve many-to-one mappings, are non-linear and dynamic in nature, they are generally non-invertible. This enforces an explicit parameterisation of generative models (i.e. backward connections) to afford recognition and suggests that forward architectures, on their own, are not sufficient for perception. (iii). Finally, non-linearities in generative models, mediated by backward connections, require these connections to be modulatory, so that representations in higher cortical levels can interact to predict responses in lower levels. This is important in relation to functional asymmetries in forward and backward connections that have been demonstrated empirically.},
address = {The Wellcome Department of Imaging Neuroscience, Institute of Neurology, University College London, 12 Queen Square, London WC1N 3BG, UK. k.friston@fl.ion.ucl.ac.uk},
author = {Friston, Karl},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neural Netw 2003 Friston.pdf:pdf},
journal = {Neural Netw.},
month = {nov},
number = {9},
pages = {1325--1352},
title = {{Learning and inference in the brain.}},
volume = {16},
year = {2003}
}
@article{Rust2012,
abstract = {Although popular accounts suggest that neurons along the ventral visual processing stream become increasingly selective for particular objects, this appears at odds with the fact that inferior temporal cortical (IT) neurons are broadly tuned. To explore this apparent contradiction, we compared processing in two ventral stream stages (visual cortical areas V4 and IT) in the rhesus macaque monkey. We confirmed that IT neurons are indeed more selective for conjunctions of visual features than V4 neurons and that this increase in feature conjunction selectivity is accompanied by an increase in tolerance ("invariance") to identity-preserving transformations (e.g., shifting, scaling) of those features. We report here that V4 and IT neurons are, on average, tightly matched in their tuning breadth for natural images ("sparseness") and that the average V4 or IT neuron will produce a robust firing rate response (>50% of its peak observed firing rate) to ∼10% of all natural images. We also observed that sparseness was positively correlated with conjunction selectivity and negatively correlated with tolerance within both V4 and IT, consistent with selectivity-building and invariance-building computations that offset one another to produce sparseness. Our results imply that the conjunction-selectivity-building and invariance-building computations necessary to support object recognition are implemented in a balanced manner to maintain sparseness at each stage of processing.},
address = {McGovern Institute for Brain Research, Cambridge, Massachusetts 02139, USA. nrust@sas.upenn.edu},
author = {Rust, Nicole C and DiCarlo, James J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2012 Rust.pdf:pdf},
journal = {J. Neurosci.},
month = {jul},
number = {30},
pages = {10170--10182},
title = {{Balanced increases in selectivity and tolerance produce constant sparseness along the ventral visual stream.}},
volume = {32},
year = {2012}
}
@article{Popivanov2016,
author = {Popivanov, Ivo D and Schyns, Philippe G and Vogels, Rufin},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proceedings of the National Academy of Sciences 2016 Popivanov.pdf:pdf},
journal = {Proc. Natl. Acad. Sci.},
month = {apr},
number = {17},
pages = {E2450--E2459},
title = {{Stimulus features coded by single neurons of a macaque body category selective patch}},
volume = {113},
year = {2016}
}
@article{Bellmund2018,
abstract = {The hippocampal formation has long been suggested to underlie both memory formation and spatial navigation. We discuss how neural mechanisms identified in spatial navigation research operate across information domains to support a wide spectrum of cognitive functions. In our framework, place and grid cell population codes provide a representational format to map variable dimensions of cognitive spaces. This highly dynamic mapping system enables rapid reorganization of codes through remapping between orthogonal representations across behavioral contexts, yielding a multitude of stable cognitive spaces at different resolutions and hierarchical levels. Action sequences result in trajectories through cognitive space, which can be simulated via sequential coding in the hippocampus. In this way, the spatial representational format of the hippocampal formation has the capacity to support flexible cognition and behavior.},
author = {Bellmund, Jacob L.S. and G{\"{a}}rdenfors, Peter and Moser, Edvard I. and Doeller, Christian F.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/eaat6766.full.pdf:pdf},
journal = {Science},
number = {6415},
pmid = {30409861},
title = {{Navigating cognition: Spatial codes for human thinking}},
volume = {362},
year = {2018}
}
@article{Klink2017a,
abstract = {Neuron, 95 (2017) 209-223. doi:10.1016/j.neuron.2017.05.033},
author = {Klink, P Christiaan and Dagnino, Bruno and Gariel-Mathis, Marie-Alice and Roelfsema, Pieter R},
file = {:Users/hahosoya/Documents/Mendeley Desktop/52D349CC-5616-403B-94B0-D982002BA26B.pdf:pdf},
journal = {Neuron},
month = {jul},
number = {1},
pages = {209--220.e3},
title = {{Distinct Feedforward and Feedback Effects of Microstimulation in Visual Cortex Reveal Neural Mechanisms of Texture Segregation}},
volume = {95},
year = {2017}
}
@article{Tavares2015,
abstract = {Deciphering the neural mechanisms of social behavior has propelled the growth of social neuroscience. The exact computations of the social brain, however, remain elusive. Here we investigated how the human brain tracks ongoing changes in social relationships using functional neuroimaging. Participants were lead characters in a role-playing game in which they were to find a new home and a job through interactions with virtual cartoon characters. We found that a two-dimensional geometric model of social relationships, a "social space" framed by power and affiliation, predicted hippocampal activity. Moreover, participants who reported better social skills showed stronger covariance between hippocampal activity and "movement" through "social space." The results suggest that the hippocampus is crucial for social cognition, and imply that beyond framing physical locations, the hippocampus computes a more general, inclusive, abstract, and multidimensional cognitive map consistent with its role in episodic memory.},
author = {Tavares, Rita Morais and Mendelsohn, Avi and Grossman, Yael and Williams, Christian Hamilton and Shapiro, Matthew and Trope, Yaacov and Schiller, Daniela},
file = {:Users/hahosoya/Documents/Mendeley Desktop/097516de2fce99ea7531098956affd4cd84af27e.pdf:pdf},
journal = {Neuron},
number = {1},
pages = {231--243},
pmid = {26139376},
title = {{A Map for Social Navigation in the Human Brain}},
volume = {87},
year = {2015}
}
@article{Balduzzi2008,
author = {Balduzzi, David and Tononi, Giulio},
editor = {Sporns, Olaf},
file = {:Users/hahosoya/Documents/Mendeley Desktop/PLoS Comp Biol 2008 Balduzzi.pdf:pdf},
journal = {PLoS Comput. Biol.},
month = {jun},
number = {6},
pages = {e1000091},
title = {{Integrated Information in Discrete Dynamical Systems: Motivation and Theoretical Framework}},
volume = {4},
year = {2008}
}
@article{Tsao2015,
author = {Tsao, Doris},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Cold Spring Harb Symp Quant Biol 2015 Tsao.pdf:pdf},
journal = {Cold Spring Harb. Symp. Quant. Biol.},
month = {jun},
pages = {109--114},
title = {{The Macaque Face Patch System: A Window into Object Representation}},
volume = {79},
year = {2015}
}
@misc{Bunsey1996,
abstract = {THE hippocampus is critical to declarative memory in humans. This kind of memory involves associations among items or events that can be accessed flexibly to guide memory expression in various and even new situation. In animals, there has been controversy about whether the hippocampus is specialized for spatial memory or whether it mediates a general memory function, as it does in humans. To address this issue we trained normal rats and rats with hippocampal damage on non-spatial stimulus-stimulus associations, then probed the nature of their memory representations. We report here that normal rats demonstrated two forms of flexible memory expression, transitivity, the ability to judge inferentially across stimulus pairs that share a common element, and symmetry, the ability to associate paired elements presented in the reverse of training order. Rats with neurotoxic damage limited to the hippocampus demonstrated neither form of flexible expression, indicating that non-spatial declarative processing depends specifically on the hippocampus in animals as it does in humans.},
author = {Bunsey, M. and Elchenbaum, H.},
booktitle = {Nature},
file = {:Users/hahosoya/Documents/Mendeley Desktop/7f5dc64f212c91440df849e273fa4c4d8518e82d.pdf:pdf},
number = {6562},
pages = {255--257},
pmid = {8538790},
title = {{Conservation of hippocampal memory function in rats and humans}},
volume = {379},
year = {1996}
}
@article{Freiwald2010,
abstract = {Primates can recognize faces across a range of viewing conditions. Representations of individual identity should thus exist that are invariant to accidental image transformations like view direction. We targeted the recently discovered face-processing network of the macaque monkey that consists of six interconnected face-selective regions and recorded from the two middle patches (ML, middle lateral, and MF, middle fundus) and two anterior patches (AL, anterior lateral, and AM, anterior medial). We found that the anatomical position of a face patch was associated with a unique functional identity: Face patches differed qualitatively in how they represented identity across head orientations. Neurons in ML and MF were view-specific; neurons in AL were tuned to identity mirror-symmetrically across views, thus achieving partial view invariance; and neurons in AM, the most anterior face patch, achieved almost full view invariance.},
address = {The Rockefeller University, 1230 York Avenue, New York, NY 10065, USA. wfreiwald@rockefeller.edu},
author = {Freiwald, Winrich A and Tsao, Doris Y},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Science 2010 Freiwald(2).pdf:pdf;:Users/hahosoya/Documents/Mendeley Desktop/Science 2010 Freiwald.pdf:pdf},
journal = {Science},
month = {nov},
number = {6005},
pages = {845--851},
title = {{Functional Compartmentalization and Viewpoint Generalization Within the Macaque Face-Processing System}},
volume = {330},
year = {2010}
}
@article{Ringach2009,
abstract = {Abstract The traditional view of spontaneous neural activity as “noise” has been challenged by recent findings suggesting that:(a) spontaneous activity in cortical populations is highly structured in both space and time,(b) the spatio-temporal structure of spontaneous activity  ...},
address = {Department of Neurobiology and Psychology, Jules Stein Eye Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095-1563, USA. dario@ucla.edu},
author = {Ringach, Dario L},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2AEB7FFE-5AF4-4A74-B4D3-2DC6F8B3057B.pdf:pdf},
journal = {Curr. Opin. Neurobiol.},
month = {aug},
number = {4},
pages = {439--444},
title = {{Spontaneous and driven cortical activity: implications for computation}},
volume = {19},
year = {2009}
}
@article{Carlson2011,
abstract = {Sparse coding has long been recognized as a primary goal of image transformation in the visual system [1–4]. Sparse coding in early visual cortex is achieved by abstracting local oriented spatial frequencies [5] and by excitatory/inhibitory surround modulation [6]. Object  ... 
},
author = {Carlson, Eric T and Rasquinha, Russell J and Zhang, Kechen and Connor, Charles E},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Current Biology 2011 Carlson.pdf:pdf},
journal = {Curr. Biol.},
month = {feb},
number = {4},
pages = {288--293},
title = {{A Sparse Object Coding Scheme in Area V4}},
volume = {21},
year = {2011}
}
@article{Freeman2011,
author = {Freeman, Jeremy and Simoncelli, Eero P},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature neuroscience 2011 Freeman-1.pdf:pdf},
journal = {Nat. Neurosci.},
month = {aug},
number = {9},
pages = {1195--1201},
title = {{Metamers of the ventral stream}},
volume = {14},
year = {2011}
}
@article{Ohayon2012,
abstract = {Neuron, 74 (2012) 567-581. doi:10.1016/j.neuron.2012.03.024},
author = {Ohayon, Shay and Freiwald, Winrich A and Tsao, Doris Y},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neuron 2012 Ohayon.pdf:pdf;:Users/hahosoya/Documents/Mendeley Desktop/mmc1.pdf:pdf},
journal = {Neuron},
month = {may},
number = {3},
pages = {567--581},
title = {{What Makes a Cell Face Selective? The Importance of Contrast}},
volume = {74},
year = {2012}
}
@article{Lazaridou2017,
abstract = {The current mainstream approach to train natural language systems is to expose them to large amounts of text. This passive learning is problematic if we are interested in developing interactive machines, such as conversational agents. We propose a framework for language learning that relies on multi-agent communication. We study this learning in the context of referential games. In these games, a sender and a receiver see a pair of images. The sender is told one of them is the target and is allowed to send a message from a fixed, arbitary vocabulary to the receiver. The receiver must rely on this message to identify the target. Thus, the agents develop their own language interactively out of the need to communicate. We show that two networks with simple configurations are able to learn to coordinate in the referential game. We further explore how to make changes to the game environment to cause the “word meanings” induced in the game to better reflect intuitive semantic properties of the images. In addition, we present a simple strategy for grounding the agents' code into natural language. Both of these are necessary steps towards developing machines that are able to communicate with humans productively.},
archivePrefix = {arXiv},
arxivId = {1612.07182},
author = {Lazaridou, Angeliki and Peysakhovich, Alexander and Baroni, Marco},
eprint = {1612.07182},
file = {:Users/hahosoya/Documents/Mendeley Desktop/82831bc9aef2fc111f10cb5bd1ba23a74ee47421.pdf:pdf},
journal = {5th Int. Conf. Learn. Represent. ICLR 2017 - Conf. Track Proc.},
pages = {1--11},
title = {{Multi-agent cooperation and the emergence of (natural) language}},
year = {2017}
}
@article{Anderson2017,
abstract = {People with schizophrenia (SZ) experience abnormal visual perception on a range of visual tasks, which have been linked to abnormal synaptic transmission and an imbalance between cortical excitation and inhibition. However, differences in the underlying architecture of visual cortex neurons, which might explain these visual anomalies, have yet to be reported in vivo. Here, we probed the neural basis of these deficits using fMRI and population receptive field (pRF) mapping to infer properties of visually responsive neurons in people with SZ. We employed a difference-of-Gaussian model to capture the center-surround configuration of the pRF, providing critical information about the spatial scale of the pRFs inhibitory surround. Our analysis reveals that SZ is associated with reduced pRF size in early retinotopic visual cortex, as well as a reduction in size and depth of the inhibitory surround in V1, V2, and V4. We consider how reduced inhibition might explain the diverse range of visual deficits reported in SZ.},
author = {Anderson, Elaine J. and Tibber, Marc S. and Schwarzkopf, D. Sam and Shergill, Sukhwinder S. and Fernandez-Egea, Emilio and Rees, Geraint and Dakin, Steven C.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/16723d5ca8b899b768339331898190591b73a988.pdf:pdf},
journal = {J. Neurosci.},
keywords = {FMRI,PRF,Perception,Schizophrenia,Surround suppression,Visual cortex},
number = {6},
pages = {1546--1556},
title = {{Visual population receptive fields in people with schizophrenia have reduced inhibitory surrounds}},
volume = {37},
year = {2017}
}
@article{Chen2017,
abstract = {Neuron, 96 (2017) 1388-1406. doi:10.1016/j.neuron.2017.11.004},
author = {Chen, Rujia and Wang, Feng and Liang, Hualou and Li, Wu},
file = {:Users/hahosoya/Documents/Mendeley Desktop/D9F7EDEE-DC65-4B42-983F-B857DBD22A16.pdf:pdf},
journal = {Neuron},
month = {dec},
number = {6},
pages = {1388--1402.e4},
title = {{Synergistic Processing of Visual Contours across Cortical Layers in V1 and V2}},
volume = {96},
year = {2017}
}
@article{Churchland2012,
abstract = {Nature (2012). doi:10.1038/nature11129},
author = {Churchland, Mark M and Cunningham, John P and Kaufman, Matthew T and Foster, Justin D and Nuyujukian, Paul and Ryu, Stephen I and Shenoy, Krishna V},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature 2012 Churchland.pdf:pdf},
journal = {Nature},
month = {jun},
pages = {1--8},
title = {{Neural population dynamics during reaching}},
year = {2012}
}
@article{Sanders2015,
abstract = {Trends in Neurosciences, 38 (2015) 763-775. doi:10.1016/j.tins.2015.10.004},
author = {Sanders, Honi and Renn{\'{o}}-Costa, C{\'{e}}sar and Idiart, Marco and Lisman, John},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Trends in Neurosciences 2015 Sanders.pdf:pdf},
journal = {Trends Neurosci.},
month = {dec},
number = {12},
pages = {763--775},
title = {{Grid Cells and Place Cells: An Integrated View of their Navigational and Memory Function}},
volume = {38},
year = {2015}
}
@article{Gross2010,
abstract = {A close relationship exists between the advancement of face recognition algorithms and the availability of face databases varying factors that affect facial appearance in a controlled manner. The CMU PIE database has been very influential in advancing research in face recognition across pose and illumination. Despite its success the PIE database has several shortcomings: a limited number of subjects, single recording session and only few expressions captured. To address these issues we collected the CMU Multi-PIE database. It contains 337 subjects, imaged under 15 view points and 19 illumination conditions in up to four recording sessions. In this paper we introduce the database and describe the recording procedure. We furthermore present results from baseline experiments using PCA and LDA classifiers to highlight similarities and differences between PIE and Multi-PIE.},
address = {Robotics Institute, Carnegie Mellon University.},
author = {Gross, Ralph and Matthews, Iain and Cohn, Jeff and Kanade, Takeo and Baker, Simon},
file = {:Users/hahosoya/Documents/Mendeley Desktop/F19001BE-E81D-460D-A0C9-8B6A69727FAC.pdf:pdf},
journal = {Proc. Int. Conf. Autom. Face Gesture Recognition. IEEE Int. Conf. Autom. Face Gesture Recognit.},
month = {may},
number = {5},
pages = {807--813},
title = {{Multi-PIE.}},
volume = {28},
year = {2010}
}
@article{Tsao2003,
author = {Tsao, Doris Y and Freiwald, Winrich A and Knutsen, Tamara A and Mandeville, Joseph B and Tootell, Roger B H},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature neuroscience 2003 Tsao-1.pdf:pdf},
journal = {Nat. Neurosci.},
month = {aug},
number = {9},
pages = {989--995},
title = {{Faces and objects in macaque cerebral cortex}},
volume = {6},
year = {2003}
}
@article{Dayan2007,
abstract = {The selectivities of neurons in primary visual cortex are often considered to be adapted to the statistics of natural images. Accordingly, simple cell-like tuning emerges when unsupervised learning models that seek sparse representations of input probabilities are  ...},
author = {Dayan, P},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Vision Research 2007 Dayan.pdf:pdf},
journal = {Vision Res.},
month = {jan},
title = {{An unsupervised learning model of neural plasticity: Orientation selectivity in goggle-reared kittens}},
year = {2007}
}
@article{Marino2018,
abstract = {Inference models are a key component in scaling variational inference to deep latent variable models, most notably as encoder networks in variational auto-encoders (VAEs). By replacing conventional optimization-based inference with a learned model, inference is amortized over data examples and therefore more computationally efficient. However, standard inference models are restricted to direct mappings from data to approximate posterior estimates. The failure of these models to reach fully optimized approximate posterior estimates results in an amortization gap. We aim toward closing this gap by proposing iterative inference models, which learn to perform inference optimization through repeatedly encoding gradients. Our approach generalizes standard inference models in VAEs and provides insight into several empirical findings, including top-down inference techniques. We demonstrate the inference optimization capabilities of iterative inference models and show that they outperform standard inference models on several benchmark data sets of images and text.},
archivePrefix = {arXiv},
arxivId = {1807.09356},
author = {Marino, Joseph and Yue, Yisong and Mandt, Stephan},
eprint = {1807.09356},
file = {:Users/hahosoya/Documents/Mendeley Desktop/46ccbf6fef38747833006a613b1c6582a15d4699.pdf:pdf},
isbn = {9781510867963},
journal = {35th Int. Conf. Mach. Learn. ICML 2018},
pages = {5444--5462},
title = {{Iterative amortized inference}},
volume = {8},
year = {2018}
}
@article{Spanne2015,
abstract = {Trends in Neurosciences, 38 (2015) 417-427. doi:10.1016/j.tins.2015.05.005},
author = {Spanne, Anton and J{\"{o}}rntell, Henrik},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Trends in Neurosciences 2015 Spanne.pdf:pdf},
journal = {Trends Neurosci.},
month = {jul},
number = {7},
pages = {417--427},
title = {{Questioning the role of sparse coding in the brain}},
volume = {38},
year = {2015}
}
@article{Whittington2018,
abstract = {A central problem to understanding intelligence is the concept of generalisation. This allows previously learnt structure to be exploited to solve tasks in novel situations differing in their particularities. We take inspiration from neuroscience, specifically the hippocampal-entorhinal system known to be important for generalisation. We propose that to generalise structural knowledge, the representations of the structure of the world, i.e. how entities in the world relate to each other, need to be separated from representations of the entities themselves. We show, under these principles, artificial neural networks embedded with hierarchy and fast Hebbian memory, can learn the statistics of memories and generalise structural knowledge. Spatial neuronal representations mirroring those found in the brain emerge, suggesting spatial cognition is an instance of more general organising principles. We further unify many entorhinal cell types as basis functions for constructing transition graphs, and show these representations effectively utilise memories. We experimentally support model assumptions, showing a preserved relationship between entorhinal grid and hippocampal place cells across environments.},
archivePrefix = {arXiv},
arxivId = {1805.09042},
author = {Whittington, James C.R. and Muller, Timothy H. and Barry, Caswell and Mark, Shirley and Behrens, Timothy E.J.},
eprint = {1805.09042},
file = {:Users/hahosoya/Documents/Mendeley Desktop/8ba48a9b1ea9b68b8e3fa6bba51bcdb6b7291d89.pdf:pdf},
journal = {Adv. Neural Inf. Process. Syst.},
number = {Nips},
pages = {8484--8495},
title = {{Generalisation of structural knowledge in the hippocampal-entorhinal system}},
volume = {2018-Decem},
year = {2018}
}
@article{Saleem2018,
abstract = {Nature, doi:10.1038/s41586-018-0516-1},
author = {Saleem, Aman B and Diamanti, E Mika and Fournier, Julien and Harris, Kenneth D and Carandini, Matteo},
file = {:Users/hahosoya/Documents/Mendeley Desktop/483305F6-3EE7-489B-8903-3A6BE04D2FAF.pdf:pdf},
journal = {Nature},
month = {sep},
number = {7725},
pages = {1--18},
title = {{Coherent encoding of subjective spatial position in visual cortex and hippocampus}},
volume = {562},
year = {2018}
}
@article{Sadagopan2017,
abstract = {... processing operations can build. DOI: 10.7554 / eLife . 18558.001 Introduction For primates, faces are among the most behaviorally salient and socially relevant visual stimuli (Leopold and Rhodes, 2010). Reflecting this importance ... 
},
author = {Sadagopan, S and Zarco, W and Freiwald, W A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2017 Sadagopan.pdf:pdf},
journal = {Elife},
month = {jan},
title = {{A causal relationship between face-patch activity and face-detection behavior}},
year = {2017}
}
@article{Tang2013,
abstract = {Abstract Multilayer perceptrons (MLPs) or neural networks are popular models used for nonlinear regression and classification tasks. As regressors, MLPs model the conditional distribution of the predictor variables Y given the input variables X. However, this predictive distribution is assumed to be unimodal (eg Gaussian). For tasks such as structured prediction problems, the conditional distribution should be multimodal, forming one-to- ...
},
author = {Tang, Y and Salakhutdinov, R R},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Advances in neural information {\ldots} 2013 Tang.pdf:pdf},
journal = {Adv. Neural Inf. {\ldots}},
month = {jan},
title = {{Learning stochastic feedforward neural networks}},
year = {2013}
}
@article{Parker1988,
abstract = {Measurements of the spatial contrast sensitivity function and orientation selectivity of visual neurons in the foveal striate cortex (V1) of primates were interpreted within the context of a model of the two-dimensional spatial structure of their receptive fields. Estimates of the spatial dimensions of the receptive fields along the axis of preferred orientation were derived from the application of the model and were compared with estimates of the smallest spatial subunit in the dimension orthogonal to the preferred orientation. Some measure of agreement was found with corresponding estimates of parameters for psychophysical channels in human foveal vision.},
author = {Parker, A J and Hawken, M J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/J. Opt. Soc. Am. A 1988 Parker.pdf:pdf},
journal = {J. Opt. Soc. Am. A},
month = {jan},
number = {4},
pages = {598--605},
title = {{Two-dimensional spatial structure of receptive fields in monkey striate cortex}},
volume = {5},
year = {1988}
}
@article{Mathieu2016,
author = {Mathieu, Micha{\"{e}}l and Zhao, Junbo Jake and Sprechmann, Pablo and Ramesh, Aditya and LeCun, Yann},
file = {:Users/hahosoya/Documents/Mendeley Desktop/45272B90-F896-4276-B833-B40E93DD8E64.pdf:pdf},
journal = {Adv. Neural Inf. Process. Syst.},
month = {jan},
title = {{Disentangling factors of variation in deep representation using adversarial training.}},
year = {2016}
}
@article{Consortium2013,
abstract = {The Lancet, 381 (2013) 1371-1379. doi:10.1016/S0140-6736(12)62129-1},
author = {Consortium, Cross-Disorder Group of the Psychiatric Genomics},
file = {:Users/hahosoya/Documents/Mendeley Desktop/The Lancet 2013 Consortium.pdf:pdf},
journal = {Lancet},
month = {apr},
number = {9875},
pages = {1371--1379},
title = {{Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis}},
volume = {381},
year = {2013}
}
@article{Capparelli2019,
abstract = {A central goal in visual neuroscience is to understand computational mechanisms and to identify neural structures responsible for integrating local visual features into global representations. When probed with complex stimuli that extend beyond their classical receptive field, neurons display non-linear behaviours indicative of such integration processes already in early stages of visual processing. Recently some progress has been made in explaining these effects from first principles by sparse coding models with a neurophysiologically realistic inference dynamics. They reproduce some of the complex response characteristics observed in primary visual cortex, but only when the context is located near the classical receptive field, since the connection scheme they propose include interactions only among neurons with overlapping input fields. Longer-range interactions required for addressing the plethora of contextual effects reaching beyond this range do not exist. Hence, a satisfactory explanation of contextual phenomena in terms of realistic interactions and dynamics in visual cortex is still missing. Here we propose an extended generative model for visual scenes that includes spatial dependencies among different features. We derive a neurophysiologically realistic inference scheme under the constraint that neurons have direct access to only local image information. The scheme can be interpreted as a network in primary visual cortex where two neural populations are organized in different layers within orientation hypercolumns that are connected by local, short-range and long-range recurrent interactions. When trained with natural images, the model predicts a connectivity structure linking neurons with similar orientation preferences matching the typical patterns found for long-ranging horizontal axons and feedback projections in visual cortex. Subjected to contextual stimuli typically used in empirical studies our model replicates several hallmark effects of contextual processing and predicts characteristic differences for surround modulation between the two model populations. In summary, our model provides a novel framework for contextual processing in the visual system proposing a well-defined functional role for horizontal axons and feedback projections.},
author = {Capparelli, Federica and Pawelzik, Klaus and Ernst, Udo},
file = {:Users/hahosoya/Documents/Mendeley Desktop/journal.pcbi.1007370.pdf:pdf},
isbn = {1111111111},
journal = {PLOS Comput. Biol.},
number = {10},
pages = {e1007370},
title = {{Constrained inference in sparse coding reproduces contextual effects and predicts laminar neural dynamics}},
volume = {15},
year = {2019}
}
@article{Kell2018,
abstract = {Neuron, Corrected proof. doi:10.1016/j.neuron.2018.03.044},
author = {Kell, Alexander J E and Yamins, Daniel L K and Shook, Erica N and Norman-Haignere, Sam V and McDermott, Josh H},
file = {:Users/hahosoya/Documents/Mendeley Desktop/9E9FEB0A-FC76-42C5-8DF7-57AC811976D2.pdf:pdf},
journal = {Neuron},
month = {apr},
number = {3},
pages = {1--32},
title = {{A Task-Optimized Neural Network Replicates Human Auditory Behavior, Predicts Brain Responses, and Reveals a Cortical Processing Hierarchy}},
volume = {98},
year = {2018}
}
@article{Graves2016,
abstract = {Artificial neural networks are remarkably adept at sensory processing, sequence learning and reinforcement learning, but are limited in their ability to represent variables and data structures and to store data over long timescales, owing to the lack of an external memory. Here we introduce a machine learning model called a differentiable neural computer (DNC), which consists of a neural network that can read from and write to an external memory matrix, analogous to the random-access memory in a conventional computer. Like a conventional computer, it can use its memory to represent and manipulate complex data structures, but, like a neural network, it can learn to do so from data. When trained with supervised learning, we demonstrate that a DNC can successfully answer synthetic questions designed to emulate reasoning and inference problems in natural language. We show that it can learn tasks such as finding the shortest path between specified points and inferring the missing links in randomly generated graphs, and then generalize these tasks to specific graphs such as transport networks and family trees. When trained with reinforcement learning, a DNC can complete a moving blocks puzzle in which changing goals are specified by sequences of symbols. Taken together, our results demonstrate that DNCs have the capacity to solve complex, structured tasks that are inaccessible to neural networks without external read-write memory.},
author = {Graves, Alex and Wayne, Greg and Reynolds, Malcolm and Harley, Tim and Danihelka, Ivo and Grabska-Barwi{\'{n}}ska, Agnieszka and Colmenarejo, Sergio G{\'{o}}mez and Grefenstette, Edward and Ramalho, Tiago and Agapiou, John and Badia, Adri{\`{a}} Puigdom{\`{e}}nech and Hermann, Karl Moritz and Zwols, Yori and Ostrovski, Georg and Cain, Adam and King, Helen and Summerfield, Christopher and Blunsom, Phil and Kavukcuoglu, Koray and Hassabis, Demis},
file = {:Users/hahosoya/Documents/Mendeley Desktop/ba00f2d6eff68d7c1ac97240372438e75efc5c8f.pdf:pdf},
journal = {Nature},
number = {7626},
pages = {471--476},
title = {{Hybrid computing using a neural network with dynamic external memory}},
volume = {538},
year = {2016}
}
@article{Dadarlat2014,
abstract = {Nature Neuroscience 18, 138 (2014). doi:10.1038/nn.3883},
author = {Dadarlat, Maria C and O'Doherty, Joseph E and Sabes, Philip N},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature Publishing Group 2014 Dadarlat.pdf:pdf},
journal = {Nat. Neurosci.},
month = {nov},
number = {1},
pages = {138--144},
title = {{A learning-based approach to artificial sensory feedback leads to optimal integration}},
volume = {18},
year = {2014}
}
@article{Buschman2012,
abstract = {Neuron, 76 (2012) 838-846. doi:10.1016/j.neuron.2012.09.029},
author = {Buschman, Timothy J and Denovellis, Eric L and Diogo, Cinira and Bullock, Daniel and Miller, Earl K},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neuron 2012 Buschman.pdf:pdf},
journal = {Neuron},
month = {nov},
number = {4},
pages = {838--846},
title = {{Synchronous Oscillatory Neural Ensembles for Rules in the Prefrontal Cortex}},
volume = {76},
year = {2012}
}
@article{Friston2007,
author = {Friston, Karl J and Stephan, Klaas E},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Synthese 2007 Friston.pdf:pdf},
journal = {Synthese},
month = {sep},
number = {3},
pages = {417--458},
title = {{Free-energy and the brain}},
volume = {159},
year = {2007}
}
@article{Barth2012,
abstract = {Trends in Neurosciences. 10.1016/j.tins.2012.03.008},
author = {Barth, Alison L and Poulet, James F A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Trends in Neurosciences 2012 Barth.pdf:pdf},
journal = {Trends Neurosci.},
month = {may},
number = {6},
pages = {1--11},
title = {{Experimental evidence for sparse firing in the neocortex}},
volume = {35},
year = {2012}
}
@article{Deneve2008,
abstract = {In the companion letter in this issue (“Bayesian Spiking Neurons I: In- ference”), we showed that 
the dynamics of spiking neurons can be in- terpreted as a form of Bayesian integration, accumulating
evidence over time about events in the external world or the body. We proceed to de-  ...},
author = {Deneve, S},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neural Computation 2008 Deneve.pdf:pdf},
journal = {Neural Comput.},
month = {jan},
title = {{Bayesian spiking neurons II: learning}},
year = {2008}
}
@article{Saul1996,
abstract = {Journal of Arti cial Intelligence Research 4 (1996) 61|76 Submitted 11/95; published 3/96 Mean Field  Theory for Sigmoid  Belief Networks Lawrence K. Saul lksaul@psyche.mit.edu Tommi Jaakkola ... 62 Page 3. Mean  Field  Theory for Sigmoid  Belief Networks −6 −4 −2 0 2 4 6 0 0.2 ... 
},
author = {Saul, L K and Jaakkola, T and Jordan, M I},
file = {:Users/hahosoya/Documents/Mendeley Desktop/1996 Saul.pdf:pdf},
month = {mar},
title = {{Mean Field Theory for Sigmoid Belief Networks}},
year = {1996}
}
@article{Dilokthanakul2016,
abstract = {We study a variant of the variational autoencoder model (VAE) with a Gaussian mixture as a prior distribution, with the goal of performing unsupervised clustering through deep generative models. We observe that the known problem of over-regularisation that has been shown to arise in regular VAEs also manifests itself in our model and leads to cluster degeneracy. We show that a heuristic called minimum information constraint that has been shown to mitigate this effect in VAEs can also be applied to improve unsupervised clustering performance with our model. Furthermore we analyse the effect of this heuristic and provide an intuition of the various processes with the help of visualizations. Finally, we demonstrate the performance of our model on synthetic data, MNIST and SVHN, showing that the obtained clusters are distinct, interpretable and result in achieving competitive performance on unsupervised clustering to the state-of-the-art results.},
archivePrefix = {arXiv},
arxivId = {1611.02648},
author = {Dilokthanakul, Nat and Mediano, Pedro A. M. and Garnelo, Marta and Lee, Matthew C. H. and Salimbeni, Hugh and Arulkumaran, Kai and Shanahan, Murray},
eprint = {1611.02648},
file = {:Users/hahosoya/Documents/Mendeley Desktop/6b6510f592ffb65e7578eb6ea0d35c29c38404c3.pdf:pdf},
journal = {arXiv.org},
month = {nov},
number = {2016},
pages = {1--12},
title = {{Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders}},
year = {2016}
}
@article{Tanaka1993,
abstract = {Are faces recognized using more holistic representations than other types of stimuli? Taking holistic representation to mean representation without an internal part structure, we interpret the available evidence on this issue and then design new empirical tests. Based on ...
},
author = {Tanaka, J W and Farah, M J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/The Quarterly journal of experimental psychology 1993 Tanaka.pdf:pdf},
journal = {Q. J. Exp. Psychol.},
month = {jan},
number = {2},
pages = {225--245},
title = {{Parts and wholes in face recognition}},
volume = {46A},
year = {1993}
}
@article{Hannagan2021,
abstract = {The visual word form area(VWFA)is a region of human in ferotemporal cortex that emerges at a fixed location in the occipitotemporal cortex during reading acquisition and systematically responds to written words in literate individuals. Accordingto the neuronal recycling hypothesis, this region arises through the repurposing, for letter recognition, of a subpart of the ventral visual pathway initially involved in face and object recognition. Furthermore, according to the biased connectivity hypothesis, its reproducible localization is due to preexisting connections from this subregiontoareas involved inspoken-language processing. Here, wee valuatethosehypotheses in an explicit computational model. We trained a deep convolutional neural network of the ventral visual pathway, first to categorize pictures and then to recognize writtenwords invariantly for case, font, and size. We show that the model can account for many properties of the VWFA, particularly when a subset of units possesses a biased connectivity to word output units. The network develops a sparse, invariant representation of written words, based on a restricted set of reading-selective units. Their activation mimics several properties of the VWFA, and their lesioning causes a reading-specific deficit. The model predicts that, in literate brains, written words are encoded by a compositional neural code with neurons tuned either to individual letters and their ordinal position relative to word start or word ending ortopairs of letters (bigrams).},
author = {Hannagan, T. and Agrawal, A. and Cohen, L. and Dehaene, S.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/e2104779118.full.pdf:pdf},
journal = {Proc. Natl. Acad. Sci. U. S. A.},
keywords = {Compositionality,Literacy,Neural network,Reading,VWFA},
number = {46},
pmid = {34750255},
title = {{Emergence of a compositional neural code for written words: Recycling of a convolutional neural network for reading}},
volume = {118},
year = {2021}
}
@article{David2008,
author = {David, Stephen V and Hayden, Benjamin Y and Mazer, James A and Gallant, Jack L},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neuron 2008 David.pdf:pdf},
journal = {Neuron},
month = {aug},
number = {3},
pages = {509--521},
title = {{Attention to Stimulus Features Shifts Spectral Tuning of V4 Neurons during Natural Vision}},
volume = {59},
year = {2008}
}
@inproceedings{NEURIPS2019_02ed8122,
abstract = {Memory is an important aspect of intelligence and plays a role in many deep reinforcement learning models. However, little progress has been made in understanding when specific memory systems help more than others and how well they generalize. The field also has yet to see a prevalent consistent and rigorous approach for evaluating agent performance on holdout data. In this paper, we aim to develop a comprehensive methodology to test different kinds of memory in an agent and assess how well the agent can apply what it learns in training to a holdout set that differs from the training set along dimensions that we suggest are relevant for evaluating memory-specific generalization. To that end, we first construct a diverse set of memory tasks1 that allow us to evaluate test-time generalization across multiple dimensions. Second, we develop and perform multiple ablations on an agent architecture that combines multiple memory systems, observe its baseline models, and investigate its performance against the task suite.},
archivePrefix = {arXiv},
arxivId = {1910.13406},
author = {Fortunato, Moire Meire and Tan, Melissa and Faulkner, Ryan and Hansen, Steven Sleven and Badla, Adrift Puifidomenech and Buttlmore, Gavin and Deck, Charles Charlie and Lelbo, Joel Z. and Blundell, Charles and {Puigdom{\`{e}}nech Badia}, Adri{\`{a}} and Buttimore, Gavin and Deck, Charles Charlie and Leibo, Joel Z and Blundell, Charles},
booktitle = {Adv. Neural Inf. Process. Syst.},
editor = {Wallach, H and Larochelle, H and Beygelzimer, A and d\textquotesingle Alch{\'{e}}-Buc, F and Fox, E and Garnett, R},
eprint = {1910.13406},
file = {:Users/hahosoya/Documents/Mendeley Desktop/bf3110c0fa86918d0b804c74ef1f577ecac01835.pdf:pdf},
number = {NeurIPS},
pages = {1--10},
title = {{Generalization of Reinforcement Learners with Working and Episodic Memory}},
volume = {32},
year = {2019}
}
@article{Hubel1987,
abstract = {Primate visual cortical area 18 (visual area 2), when stained for the enzyme cytochrome oxidase, shows a pattern of alternating dark and light stripes; in squirrel monkeys, the dark stripes are clearly of 2 alternating types, thick and thin. We have recorded from these 3 subdivisions in macaques and squirrel monkeys, and find that each has distinctive physiological properties: (1) Cells in one set of dark stripes, in squirrel monkeys the thin stripes, are not orientation-selective; a high proportion show color-opponency. (2) Cells in the other set of dark stripes (thick stripes) are orientation-selective; most of them are also selective for binocular disparity, suggesting that they are concerned with stereoscopic depth. (3) Cells in the pale stripes are also orientation-selective and more than half of them are end-stopped. Each of the 3 subdivisions receives a different input from area 17: the thin stripes from the blobs, the pale stripes from the interblobs, the thick stripes from layer 4B. The pale stripes are thus part of the parvocellular system, and the thick stripes part of the magnocellular system. The physiological properties of the cells in the thin and pale stripes reflect the properties of their antecedent cells in 17, but nevertheless exhibit differences that suggest the kinds of processing that might occur at this stage.},
address = {Department of Animal Neurobiology, Harvard Medical School, Boston, Massachusetts 02115.},
author = {Hubel, D H and Livingstone, M S},
file = {:Users/hahosoya/Documents/Mendeley Desktop/The Journal of neuroscience 1987 Hubel.pdf:pdf},
journal = {J. Neurosci.},
month = {nov},
number = {11},
pages = {3378--3415},
title = {{Segregation of form, color, and stereopsis in primate area 18.}},
volume = {7},
year = {1987}
}
@article{Grimes2005,
abstract = {Recent algorithms for sparse coding and independent component analysis (ICA) have demonstrated how localized features can be learned from natural images. However, these approaches do not take image transformations into account. We describe an unsupervised algorithm for learning both localized features and their transformations directly from images using a sparse bilinear generative model. We show that from an arbitrary set of natural images, the algorithm produces oriented basis filters that can simultaneously represent features in an image and their transformations. The learned generative model can be used to translate features to different locations, thereby reducing the need to learn the same feature at multiple locations, a limitation of previous approaches to sparse coding and ICA. Our results suggest that by explicitly modeling the interaction between local image features and their transformations, the sparse bilinear approach can provide a basis for achieving transformation-invariant vision.},
address = {Department of Computer Science and Engineering, University of Washington, Seattle, WA 98195-2350, USA. grimes@cs.washington.edu},
author = {Grimes, David B and Rao, Rajesh P N},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neural Computation 2005 Grimes.pdf:pdf},
journal = {Neural Comput.},
month = {jan},
number = {1},
pages = {47--73},
title = {{Bilinear sparse coding for invariant vision.}},
volume = {17},
year = {2005}
}
@article{Leibo2017,
abstract = {The primate brain contains a hierarchy of visual areas, dubbed the ventral stream, which rapidly computes object representations that are both specific for object identity and robust against identity-preserving transformations, like depth rotations [1, 2]. Current computational models of object recognition, including recent deep-learning networks, generate these properties through a hierarchy of alternating selectivity-increasing filtering and tolerance-increasing pooling operations, similar to simple-complex cells operations [3-6]. Here, we prove that a class of hierarchical architectures and a broad set of biologically plausible learning rules generate approximate invariance to identity-preserving transformations at the top level of the processing hierarchy. However, all past models tested failed to reproduce the most salient property of an intermediate representation of a three-level face-processing hierarchy in the brain: mirror-symmetric tuning to head orientation [7]. Here, we demonstrate that one specific biologically plausible Hebb-type learning rule generates mirror-symmetric tuning to bilaterally symmetric stimuli, like faces, at intermediate levels of the architecture and show why it does so. Thus, the tuning properties of individual cells inside the visual stream appear to result from group properties of the stimuli they encode and to reflect the learning rules that sculpted the information-processing system within which they reside.},
address = {Center for Brains, Minds, and Machines and McGovern Institute for Brain Research at MIT, 77 Massachusetts Avenue, Cambridge, MA 02139, USA. Electronic address: jzleibo@mit.edu.},
author = {Leibo, Joel Z and Liao, Qianli and Anselmi, Fabio and Freiwald, Winrich A and Poggio, Tomaso},
file = {:Users/hahosoya/Documents/Mendeley Desktop/BA2D615B-2548-4869-8336-04DA24A0BDE2.pdf:pdf},
journal = {Curr. Biol.},
month = {jan},
number = {1},
pages = {62--67},
title = {{View-Tolerant Face Recognition and Hebbian Learning Imply Mirror-Symmetric Neural Tuning to Head Orientation.}},
volume = {27},
year = {2017}
}
@article{Downing2005,
author = {Downing, P E and Chan, A W Y and Peelen, M V and Dodds, C M and Kanwisher, N},
file = {:Users/hahosoya/Documents/Mendeley Desktop/4BEF8110-5AAE-4E78-92DD-D9BD01A3B972.pdf:pdf},
journal = {Cereb. Cortex},
month = {dec},
number = {10},
pages = {1453--1461},
title = {{Domain Specificity in Visual Cortex}},
volume = {16},
year = {2005}
}
@article{Maddison2014,
abstract = {The problem of drawing samples from a discrete distribution can be converted into a discrete optimization problem [1, 2, 3, 4]. In this work, we show how sampling from a continuous distribution can be converted into an optimization problem over continuous space. Central to the method is a stochastic process recently described in mathematical statistics that we call the Gumbel process. We present a new construction of the Gumbel process and A∗ Sampling, a practical generic sampling algorithm that searches for the maximum of a Gumbel process using A∗ search. We analyze the correctness and convergence time of A∗ Sampling and demonstrate empirically that it makes more efficient use of bound and likelihood evaluations than the most closely related adaptive rejection sampling-based algorithms.},
author = {Maddison, Chris J. and Tarlow, Daniel and Minka, Tom},
file = {:Users/hahosoya/Documents/Mendeley Desktop/fb31854ab55163440b0896c377c002eadcbb579a.pdf:pdf},
journal = {Adv. Neural Inf. Process. Syst.},
number = {January},
pages = {3086--3094},
title = {{A∗ sampling}},
volume = {4},
year = {2014}
}
@article{Sinha2002,
abstract = {Abstract The success of any object recognition system, whether biological or artificial, lies in using appropriate representation schemes. The schemes should efficiently encode object concepts while being tolerant to appearance variations induced by changes in viewing ...
},
author = {Sinha, P},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Workshop on Biologically Motivated Computer Vision (Lecture Notes in Computer Science) 2002 Sinha.pdf:pdf},
journal = {Work. Biol. Motiv. Comput. Vis. (Lecture Notes Comput. Sci.},
month = {jan},
pages = {249--262},
title = {{Qualitative representations for recognition}},
volume = {2525},
year = {2002}
}
@article{Bau2020,
abstract = {Deep neural networks excel at finding hierarchical representations that solve complex tasks over large datasets. How can we humans understand these learned representations? In this work, we present network dissection, an analytic framework to systematically identify the semantics of individual hidden units within image classification and image generation networks. First, we analyze a convolutional neural network (CNN) trained on scene classification and discover units that match a diverse set of object concepts. We find evidence that the network has learned many object classes that play crucial roles in classifying scene classes. Second, we use a similar analytic method to analyze a generative adversarial network (GAN) model trained to generate scenes. By analyzing changes made when small sets of units are activated or deactivated, we find that objects can be added and removed from the output scenes while adapting to the context. Finally, we apply our analytic framework to understanding adversarial attacks and to semantic image editing.},
archivePrefix = {arXiv},
arxivId = {2009.05041},
author = {Bau, David and Zhu, Jun Yan and Strobelt, Hendrik and Lapedriza, Agata and Zhou, Bolei and Torralba, Antonio},
eprint = {2009.05041},
file = {:Users/hahosoya/Documents/Mendeley Desktop/08d796a84c4e0f846d1f7416f1c34f57a6d535a1.pdf:pdf},
journal = {Proc. Natl. Acad. Sci. U. S. A.},
keywords = {Computer vision,Deep networks,Machine learning},
number = {48},
pages = {30071--30078},
pmid = {32873639},
title = {{Understanding the role of individual units in a deep neural network}},
volume = {117},
year = {2020}
}
@article{Weiss2002,
abstract = {The pattern of local image velocities on the retina encodes important environmental information. Although humans are generally able to extract this information, they can easily be deceived into seeing incorrect velocities. We show that these 'illusions' arise naturally in a system that attempts to estimate local image velocity. We formulated a model of visual motion perception using standard estimation theory, under the assumptions that (i) there is noise in the initial measurements and (ii) slower motions are more likely to occur than faster ones. We found that specific instantiation of such a velocity estimator can account for a wide variety of psychophysical phenomena.},
address = {School of Computer Science and Engineering, Hebrew University of Jerusalem, Givat Ram Campus, Jerusalem 91904, Israel. yweiss@cs.huji.ac.il},
author = {Weiss, Yair and Simoncelli, Eero P and Adelson, Edward H},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature neuroscience 2002 Weiss-1.pdf:pdf},
journal = {Nat. Neurosci.},
month = {jun},
number = {6},
pages = {598--604},
title = {{Motion illusions as optimal percepts.}},
volume = {5},
year = {2002}
}
@article{RatanMurty2021,
abstract = {Cortical regions apparently selective to faces, places, and bodies have provided important evidence for domain-specific theories of human cognition, development, and evolution. But claims of category selectivity are not quantitatively precise and remain vulnerable to empirical refutation. Here we develop artificial neural network-based encoding models that accurately predict the response to novel images in the fusiform face area, parahippocampal place area, and extrastriate body area, outperforming descriptive models and experts. We use these models to subject claims of category selectivity to strong tests, by screening for and synthesizing images predicted to produce high responses. We find that these high-response-predicted images are all unambiguous members of the hypothesized preferred category for each region. These results provide accurate, image-computable encoding models of each category-selective region, strengthen evidence for domain specificity in the brain, and point the way for future research characterizing the functional organization of the brain with unprecedented computational precision.},
author = {{Ratan Murty}, N. Apurva and Bashivan, Pouya and Abate, Alex and DiCarlo, James J. and Kanwisher, Nancy},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2c6f3c4ff9b88efc4de4bba2ed6c7fd7922d2b27.pdf:pdf},
journal = {Nat. Commun.},
number = {1},
pmid = {34545079},
title = {{Computational models of category-selective brain regions enable high-throughput tests of selectivity}},
volume = {12},
year = {2021}
}
@article{Fan2015,
author = {Fan, R H and Liu, S and DeAngelis, G C and Angelaki, D E},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2015 Fan.pdf:pdf},
journal = {J. Neurosci.},
month = {dec},
number = {50},
pages = {16303--16314},
title = {{Heading Tuning in Macaque Area V6}},
volume = {35},
year = {2015}
}
@article{Stryker2012,
abstract = {Neuron, 75 (2012) 230-249. doi:10.1016/j.neuron.2012.06.009},
author = {Stryker, J nbspSebastian Espinosa MichaelnbspP and Stryker, Michael P},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neuron 2012 Stryker.pdf:pdf},
journal = {Neuron},
month = {jul},
number = {2},
pages = {230--249},
title = {{Development and Plasticity of the Primary Visual Cortex}},
volume = {75},
year = {2012}
}
@article{Cadena2019,
abstract = {Despite great efforts over several decades, our best models of primary visual cortex (V1) still predict spiking activity quite poorly when probed with natural stimuli, highlighting our limited understanding of the nonlinear computations in V1. Recently, two approaches based on deep learning have emerged for modeling these nonlinear computations: transfer learning from artificial neural networks trained on object recognition and data-driven convolutional neural network models trained end-to-end on large populations of neurons. Here, we test the ability of both approaches to predict spiking activity in response to natural images in V1 of awake monkeys. We found that the transfer learning approach performed similarly well to the data-driven approach and both outperformed classical linear-nonlinear and wavelet-based feature representations that build on existing theories of V1. Notably, transfer learning using a pre-trained feature space required substantially less experimental time to achieve the same performance. In conclusion, multi-layer convolutional neural networks (CNNs) set the new state of the art for predicting neural responses to natural images in primate V1 and deep features learned for object recognition are better explanations for V1 computation than all previous filter bank theories. This finding strengthens the necessity of V1 models that are multiple nonlinearities away from the image domain and it supports the idea of explaining early visual cortex based on high-level functional goals.},
author = {Cadena, Santiago A and Denfield, George H and Walker, Edgar Y and Gatys, Leon A and Tolias, Andreas S and Bethge, Matthias and Ecker, Alexander S},
file = {:Users/hahosoya/Documents/Mendeley Desktop/16DE16CD-ABED-4828-85AA-2EE0A4491F42.pdf:pdf},
journal = {PLoS Comput. Biol.},
month = {apr},
number = {4},
pages = {e1006897},
title = {{Deep convolutional models improve predictions of macaque V1 responses to natural images}},
volume = {15},
year = {2019}
}
@article{Park2011,
author = {Park, Il Memming and Pillow, Jonathan W},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Advances in neural information processing systems 2011 Park.pdf:pdf},
journal = {Adv. Neural Inf. Process. Syst.},
month = {jan},
pages = {1692--1700},
title = {{Bayesian spike-triggered covariance analysis}},
year = {2011}
}
@article{Liang2015,
abstract = {In recent years, the convolutional neural network (CNN) has achieved great success in many computer vision tasks. Partially inspired by neuroscience, CNN shares many properties with the visual system of the brain. A prominent difference is that CNN is typically a feed-forward architecture while in the visual system recurrent connections are abundant. Inspired by this fact, we propose a recurrent CNN (RCNN) for object recognition by incorporating recurrent connections into each convolutional layer. Though the input is static, the activities of RCNN units evolve over time so that the activity of each unit is modulated by the activities of its neighboring units. This property enhances the ability of the model to integrate the context information, which is important for object recognition. Like other recurrent neural networks, unfolding the RCNN through time can result in an arbitrarily deep network with a fixed number of parameters. Furthermore, the unfolded network has multiple paths, which can facilitate the learning process. The model is tested on four benchmark object recognition datasets: CIFAR-10, CIFAR-100, MNIST and SVHN. With fewer trainable parameters, RCNN outperforms the state-of-the-art models on all of these datasets. Increasing the number of parameters leads to even better performance. These results demonstrate the advantage of the recurrent structure over purely feed-forward structure for object recognition.},
author = {Liang, Ming and Hu, Xiaolin},
file = {:Users/hahosoya/Documents/Mendeley Desktop/ec78e8bb4a64dc56e979b33de74d15cb56babca4.pdf:pdf},
isbn = {9781467369640},
journal = {Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit.},
number = {Figure 1},
pages = {3367--3375},
title = {{Recurrent convolutional neural network for object recognition}},
volume = {07-12-June},
year = {2015}
}
@article{Wang2013,
abstract = {When faced with ambiguous sensory inputs, subjective perception alternates between the different interpretations in a stochastic manner. Such multistable perception phenomena have intrigued scientists and laymen alike for over a century. Despite rigorous investigations, the underlying mechanisms of multistable perception remain elusive. Recent studies using multivariate pattern analysis revealed that activity patterns in posterior visual areas correlate with fluctuating percepts. However, increasing evidence suggests that vision-and perception at large-is an active inferential process involving hierarchical brain systems. We applied searchlight multivariate pattern analysis to functional magnetic resonance imaging signals across the human brain to decode perceptual content during bistable perception and simple unambiguous perception. Although perceptually reflective activity patterns during simple perception localized predominantly to posterior visual regions, bistable perception involved additionally many higher-order frontoparietal and temporal regions. Moreover, compared with simple perception, both top-down and bottom-up influences were dramatically enhanced during bistable perception. We further studied the intermittent presentation of ambiguous images-a condition that is known to elicit perceptual memory. Compared with continuous presentation, intermittent presentation recruited even more higher-order regions and was accompanied by further strengthened top-down influences but relatively weakened bottom-up influences. Taken together, these results strongly support an active top-down inferential process in perception.},
address = {National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892.},
author = {Wang, Megan and Arteaga, Daniel and He, Biyu J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proceedings of the National Academy of Sciences 2013 Wang.pdf:pdf},
journal = {Proc. Natl. Acad. Sci.},
month = {aug},
title = {{Brain mechanisms for simple perception and bistable perception.}},
year = {2013}
}
@article{Troyer1998,
abstract = {The origin of orientation selectivity in visual cortical responses is a central problem for understanding cerebral cortical circuitry. In cats, many experiments suggest that orientation selectivity arises from the arrangement of lateral geniculate nucleus (LGN) afferents to layer 4 simple cells. However, this explanation is not sufficient to account for the contrast invariance of orientation tuning. To understand contrast invariance, we first characterize the input to cat simple cells generated by the oriented arrangement of LGN afferents. We demonstrate that it has two components: a spatial-phase-specific component (i.e., one that depends on receptive field spatial phase), which is tuned for orientation, and a phase-nonspecific component, which is untuned. Both components grow with contrast. Second, we show that a correlation-based intracortical circuit, in which connectivity between cell pairs is determined by the correlation of their LGN inputs, is sufficient to achieve well tuned, contrast-invariant orientation tuning. This circuit generates both spatially opponent, "antiphase" inhibition ("push-pull"), and spatially matched, "same-phase" excitation. The inhibition, if sufficiently strong, suppresses the untuned input component and sharpens responses to the tuned component at all contrasts. The excitation amplifies tuned responses. This circuit agrees with experimental evidence showing spatial opponency between, and similar orientation tuning of, the excitatory and inhibitory inputs received by a simple cell. Orientation tuning is primarily input driven, accounting for the observed invariance of tuning width after removal of intracortical synaptic input, as well as for the dependence of orientation tuning on stimulus spatial frequency. The model differs from previous push-pull models in requiring dominant rather than balanced inhibition and in predicting that a population of layer 4 inhibitory neurons should respond in a contrast-dependent manner to stimuli of all orientations, although their tuning width may be similar to that of excitatory neurons. The model demonstrates that fundamental response properties of cortical layer 4 can be explained by circuitry expected to develop under correlation-based rules of synaptic plasticity, and shows how such circuitry allows the cortex to distinguish stimulus intensity from stimulus form.},
address = {Department of Psychiatry, University of California, San Francisco, California 94143-0444, USA.},
author = {Troyer, T W and Krukowski, A E and Priebe, N J and Miller, K D},
file = {:Users/hahosoya/Documents/Mendeley Desktop/The Journal of neuroscience 1998 Troyer.pdf:pdf},
journal = {J. Neurosci.},
month = {aug},
number = {15},
pages = {5908--5927},
title = {{Contrast-invariant orientation tuning in cat visual cortex: thalamocortical input tuning and correlation-based intracortical connectivity.}},
volume = {18},
year = {1998}
}
@book{Bishop2006,
author = {Bishop, Christopher M},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2006 Bishop.pdf:pdf},
month = {jan},
title = {{No Title}},
year = {2006}
}
@article{Tipping1999,
abstract = {Principal component analysis ( PCA ) is a ubiquitous technique for data analysis and processing, but one which is not based on a probability model. We demonstrate how the principal axes of a set of observed data vectors may be determined through maximum  ...},
author = {Tipping, M E and et Al},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of the Royal Statistical {\ldots} 1999 Tipping.pdf:pdf},
journal = {J. R. Stat.  {\ldots}},
month = {jan},
title = {{Probabilistic principal component analysis}},
year = {1999}
}
@article{Ziemba2016,
author = {Ziemba, Corey M and Freeman, Jeremy and Movshon, J Anthony and Simoncelli, Eero P},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proceedings of the National Academy of Sciences 2016 Ziemba.pdf:pdf},
journal = {Proc. Natl. Acad. Sci.},
month = {may},
pages = {201510847},
title = {{Selectivity and tolerance for visual texture in macaque V2}},
year = {2016}
}
@article{Kipf2019,
abstract = {A structured understanding of our world in terms of objects, relations, and hierarchies is an important component of human cognition. Learning such a structured world model from raw sensory data remains a challenge. As a step towards this goal, we introduce Contrastively-trained Structured World Models (C-SWMs). C-SWMs utilize a contrastive approach for representation learning in environments with compositional structure. We structure each state embedding as a set of object representations and their relations, modeled by a graph neural network. This allows objects to be discovered from raw pixel observations without direct supervision as part of the learning process. We evaluate C-SWMs on compositional environments involving multiple interacting objects that can be manipulated independently by an agent, simple Atari games, and a multi-object physics simulation. Our experiments demonstrate that C-SWMs can overcome limitations of models based on pixel reconstruction and outperform typical representatives of this model class in highly structured environments, while learning interpretable object-based representations.},
archivePrefix = {arXiv},
arxivId = {1911.12247},
author = {Kipf, Thomas and van der Pol, Elise and Welling, Max},
eprint = {1911.12247},
file = {:Users/hahosoya/Documents/Mendeley Desktop/90d71b9d600dc378d6f0e8721a3fecf3fccb9dfc.pdf:pdf},
pages = {1--21},
title = {{Contrastive Learning of Structured World Models}},
year = {2019}
}
@article{Barlow1972,
abstract = {The problem discussed is the relationship between the firing of single neurons in sensory pathways and subjectively experienced sensations. The conclusions are formulated as the following five dogmas: To understand nervous function one needs to look at interactions at a 
},
author = {Barlow, H B},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Perception 1972 Barlow.pdf:pdf},
journal = {Perception},
month = {jan},
pages = {371--394},
title = {{Single units and sensation: a neuron doctrine for perceptual psychology?}},
volume = {1},
year = {1972}
}
@article{OToole2018,
abstract = {Trends in Cognitive Sciences, 22 (2018) 794-809. doi:10.1016/j.tics.2018.06.006},
author = {O'Toole, Alice J and Castillo, Carlos D and Parde, Connor J and Hill, Matthew Q and Chellappa, Rama},
file = {:Users/hahosoya/Documents/Mendeley Desktop/BD896D82-EF74-4605-BA61-120DF2EE5702.pdf:pdf},
journal = {Trends Cogn. Sci.},
month = {sep},
number = {9},
pages = {794--809},
title = {{Face Space Representations in Deep Convolutional Neural Networks}},
volume = {22},
year = {2018}
}
@article{Takeda2015,
abstract = {Neuron, 86 (2015) 840-852. doi:10.1016/j.neuron.2015.03.047},
author = {Takeda, Masaki and Koyano, Kenji W and Hirabayashi, Toshiyuki and Adachi, Yusuke and Miyashita, Yasushi},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neuron 2015 Takeda.pdf:pdf},
journal = {Neuron},
month = {may},
number = {3},
pages = {840--852},
title = {{Top-Down Regulation of Laminar Circuit via Inter-Area Signal for Successful Object Memory Recall in Monkey Temporal Cortex}},
volume = {86},
year = {2015}
}
@article{Reed2015,
author = {Reed, Scott E and Zhang, Yi and Zhang, Yuting and Lee, Honglak},
file = {:Users/hahosoya/Documents/Mendeley Desktop/6F73036E-54C8-4839-8BD6-B166DFC2A46F.pdf:pdf},
journal = {Adv. Neural Inf. Process. Syst.},
month = {jan},
title = {{Deep Visual Analogy-Making.}},
year = {2015}
}
@article{Sacramento2018,
abstract = {Deep learning has seen remarkable developments over the last years, many of them inspired by neuroscience. However, the main learning mechanism behind these advances– error backpropagation–appears to be at odds with neurobiology. Here, we introduce a multilayer neuronal network model with simplified dendritic compartments in which error- driven synaptic plasticity adapts the network towards a global desired output. In contrast to previous work our model does not require separate phases and synaptic learning is driven {\ldots}
},
author = {Sacramento, Jo{\~{a}}o and Costa, Rui Ponte and Bengio, Yoshua and Senn, Walter},
file = {:Users/hahosoya/Documents/Mendeley Desktop/3F1739BC-24CB-4E5E-9B79-1AD88D33F804.pdf:pdf},
month = {jan},
pages = {8721--8732},
title = {{Dendritic cortical microcircuits approximate the backpropagation algorithm}},
year = {2018}
}
@article{Wang2017a,
abstract = {In this paper, we propose a principled Tag Disentangled Generative Adversarial Networks (TD-GAN) for re-rendering new images for the object of interest from a single image of it by specifying multiple scene properties (such as viewpoint, illumination , expression, etc.). The whole framework consists of a disentangling network, a generative network, a tag mapping net, and a discriminative network, which are trained jointly based on a given set of images that are completely/partially tagged (i.e., supervised/semi-supervised setting). Given an input image, the disentangling network extracts disentangled and interpretable representations, which are then used to generate images by the generative network. In order to boost the quality of disentangled representations, the tag mapping net is integrated to explore the consistency between the image and its tags. Furthermore, the discriminative network is introduced to implement the adversar-ial training strategy for generating more realistic images. Experiments on two challenging datasets demonstrate the state-of-the-art performance of the proposed framework in the problem of interest.},
author = {Wang, Chaoyue and Wang, C and Wang, Chaohui and Xu, Chang and Tao, Dacheng},
file = {:Users/hahosoya/Documents/Mendeley Desktop/9E327496-D510-43D3-8AB7-958171F95E21.pdf:pdf},
journal = {Int. Jt. Conf. Artif. Intell.},
month = {aug},
title = {{Tag Disentangled Generative Adversarial Networks for Object Image Re-rendering}},
year = {2017}
}
@article{Self2016,
abstract = {Nature Communications 8,  (2017). doi:10.1038/ncomms13804},
author = {Self, Matthew W and van Kerkoerle, Timo and Roelfsema, Pieter R},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature Communications 2016 Self.pdf:pdf},
journal = {Nat. Commun.},
month = {dec},
pages = {1--12},
title = {{Layer-specificity in the effects of attention and working memory on activity in primary visual cortex}},
volume = {8},
year = {2016}
}
@article{Sato2000,
abstract = {... The EM algorithm introduced in the previous sec- tion is based on batch learning (Xu et al., 1995); the parameters are ... Subsequently, the estimators for the model parameters are obtained by equations 4.5a, 4.5d, 4.7, and 4.8b ... 4.2 Equivalence of On - line and Batch EM Algorithms ... 
},
author = {Sato, M A and Ishii, S},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neural Computation 2000 Sato.pdf:pdf},
journal = {Neural Comput.},
month = {jan},
title = {{On-line EM algorithm for the normalized Gaussian network}},
year = {2000}
}
@article{Krizhevsky2012,
abstract = {Abstract We trained a large, deep convolutional neural network to classify the 1.3 million high-resolution images in the LSVRC-2010 ImageNet training set into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 39.7\% and 18.9\% ... 
},
author = {Krizhevsky, A and Sutskever, I and Hinton, G E},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Advances in neural information processing systems 2012 Krizhevsky.pdf:pdf},
journal = {Adv. Neural Inf. Process. Syst.},
month = {jan},
pages = {1097--1105},
title = {{ImageNet classification with deep convolutional neural networks}},
year = {2012}
}
@article{Hudson2018,
abstract = {We present the MAC network, a novel fully differentiable neural network architecture, designed to facilitate explicit and expressive reasoning. MAC moves away from monolithic black-box neural architectures towards a design that encourages both transparency and versatility. The model approaches problems by decomposing them into a series of attention-based reasoning steps, each performed by a novel recurrent Memory, Attention, and Composition (MAC) cell that maintains a separation between control and memory. By stringing the cells together and imposing structural constraints that regulate their interaction, MAC effectively learns to perform iterative reasoning processes that are directly inferred from the data in an end-to-end approach. We demonstrate the model's strength, robustness and interpretability on the challenging CLEVR dataset for visual reasoning, achieving a new state-of-the-art 98.9% accuracy, halving the error rate of the previous best model. More importantly, we show that the model is computationally-efficient and data-efficient, in particular requiring 5x less data than existing models to achieve strong results.},
archivePrefix = {arXiv},
arxivId = {1803.03067},
author = {Hudson, Drew A. and Manning, Christopher D.},
eprint = {1803.03067},
file = {:Users/hahosoya/Documents/Mendeley Desktop/6f3f7dd67d46bbe1aff36615834416c382109ffd.pdf:pdf},
journal = {arXiv},
pages = {1--20},
title = {{Compositional attention networks for machine reasoning}},
year = {2018}
}
@article{Vogels2002,
abstract = {Single unit activity in area TE was recorded from two macaques as they viewed 3D appearing rendered objects that were illuminated from different directions (without cast shadows) and intensities of illumination. The average modulation produced by changes in illumination intensity or direction was rather moderate, with the majority of the neurons responding invariantly to these lighting variables. When neural activity was affected by illumination direction, it was not manifested as a preference for a particular direction of illumination by a given neuron. Instead, the tuning appeared to be to the relative brightness of a given shaped surface at a given orientation. The modulation to changes in illumination direction was considerably smaller than that produced by changes in object shape. Most of the neurons that were unaffected by changes in illumination direction responded much less to silhouettes of these objects, indicating that these neurons were also sensitive to an object's inner features. The neuronal invariance for shading variations may provide the basis for the invariance of object recognition under changes in illumination.},
address = {Laboratorium voor Neuro-en Psychofysiologie, Faculteit der Geneeskunde, KU Leuven, Campus Gasthuisberg, Herestraat, B-3000 Leuven, Belgium.rufin.vogels@med.kuleuven.ac.be},
author = {Vogels, Rufin and Biederman, Irving},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Cereb. Cortex 2002 Vogels.pdf:pdf},
journal = {Cereb. Cortex},
month = {jul},
number = {7},
pages = {756--766},
title = {{Effects of illumination intensity and direction on object coding in macaque inferior temporal cortex.}},
volume = {12},
year = {2002}
}
@article{Mita2020,
abstract = {A large part of the literature on learning disentangled representations focuses on variational autoencoders (VAE). Recent developments demonstrate that disentanglement cannot be obtained in a fully unsupervised setting without inductive biases on models and data. However, Khemakhem et al., AISTATS, 2020 suggest that employing a particular form of factorized prior, conditionally dependent on auxiliary variables complementing input observations, can be one such bias, resulting in an identifiable model with guarantees on disentanglement. Working along this line, we propose a novel VAE-based generative model with theoretical guarantees on identifiability. We obtain our conditional prior over the latents by learning an optimal representation, which imposes an additional strength on their regularization. We also extend our method to semi-supervised settings. Experimental results indicate superior performance with respect to state-of-the-art approaches, according to several established metrics proposed in the literature on disentanglement.},
archivePrefix = {arXiv},
arxivId = {2010.09360},
author = {Mita, Graziano and Filippone, Maurizio and Michiardi, Pietro},
eprint = {2010.09360},
file = {:Users/hahosoya/Documents/Mendeley Desktop/abdf8a813deb0d5d757bba0fac4eb6eea953b6d1.pdf:pdf;:Users/hahosoya/Documents/Mendeley Desktop/mita21a-supp.pdf:pdf},
title = {{An Identifiable Double VAE For Disentangled Representations}},
year = {2020}
}
@article{Lee1999,
abstract = {Abstract We present an unsupervised classification algorithm based on an ICA mixture model. The ICA mixture model assumes that the observed data can be categorized into several mutually exclusive data classes in which the components in each class are ...
},
author = {Lee, T W and Lewicki, M S},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Advances in neural information processing systems 1999 Lee.pdf:pdf},
journal = {Adv. Neural Inf. Process. Syst.},
month = {jan},
title = {{Unsupervised classification with non-Gaussian mixture models using ICA}},
year = {1999}
}
@article{Ziemba2021,
abstract = {Sensory processing necessitates discarding some information in service of preserving and reformatting more behaviorally relevant information. Sensory neurons seem to achieve this by responding selectively to particular combinations of features in their inputs, while averaging over or ignoring irrelevant combinations. Here, we expose the perceptual implications of this tradeoff between selectivity and invariance, using stimuli and tasks that explicitly reveal their opposing effects on discrimination performance. We generate texture stimuli with statistics derived from natural photographs, and ask observers to perform two different tasks: Discrimination between images drawn from families with different statistics, and discrimination between image samples with identical statistics. For both tasks, the performance of an ideal observer improves with stimulus size. In contrast, humans become better at family discrimination but worse at sample discrimination. We demonstrate through simulations that these behaviors arise naturally in an observer model that relies on a common set of physiologically plausible local statistical measurements for both tasks.},
author = {Ziemba, Corey M. and Simoncelli, Eero P.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/700e3730717e7f1884de2523587fe18f056126f8.pdf:pdf},
journal = {Nat. Commun.},
number = {1},
pages = {1--11},
pmid = {34321483},
title = {{Opposing effects of selectivity and invariance in peripheral vision}},
volume = {12},
year = {2021}
}
@article{Ma2011,
author = {Ma, Wei Ji and Navalpakkam, Vidhya and Beck, Jeffrey M and van den Berg, Ronald and Pouget, Alexandre},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature Publishing Group 2011 Ma.pdf:pdf},
journal = {Nat. Neurosci.},
month = {may},
number = {6},
pages = {783--790},
title = {{Behavior and neural basis of near-optimal visual search}},
volume = {14},
year = {2011}
}
@article{Hallum2014,
abstract = {Single neurons in areas V1 and V2 of macaque visual cortex respond selectively to luminance-modulated stimuli. These responses are often influenced by context, for example when stimuli extend outside the classical receptive field (CRF). These contextual phenomena, observed in many sensory areas, reflect a fundamental cortical computation and may inform perception by signaling second-order visual features which are defined by spatial relationships of contrast, orientation and spatial frequency. In the anesthetized, paralyzed macaque, we measured single-unit responses to a drifting preferred sinusoidal grating; low spatial frequency sinusoidal contrast modulations were applied to the grating, creating contrast-modulated, second-order forms. Most neurons responded selectively to the orientation of the contrast modulation of the preferred grating and were therefore second-order orientation-selective. Second-order selectivity was created by the asymmetric spatial organization of the excitatory CRF and suppressive extraclassical surround. We modeled these receptive field subregions using spatial Gaussians, sensitive to the modulation of contrast (not luminance) of the preferred carrier grating, that summed linearly and were capable of recovering asymmetrical receptive field organizations. Our modeling suggests that second-order selectivity arises both from elongated excitatory CRFs, asymmetrically organized extraclassical surround suppression, or both. We validated the model by successfully testing its predictions against conventional surround suppression measurements and spike-triggered analysis of second-order form responses. Psychophysical adaptation measurements on human observers revealed a pattern of second-order form selectivity consistent with neural response patterns. We therefore propose that cortical cells in primates do double duty, providing signals about both first- and second-order forms.},
address = {Center for Neural Science, New York University, New York, NY 10003, United States. Electronic address: hallum@cns.nyu.edu.},
author = {Hallum, Luke E and Movshon, J Anthony},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Vision Research 2014 Hallum.pdf:pdf},
journal = {Vision Res.},
month = {nov},
pages = {24--35},
title = {{Surround suppression supports second-order feature encoding by macaque V1 and V2 neurons.}},
volume = {104},
year = {2014}
}
@article{Hosoya2012,
author = {Hosoya, Haruo},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2012 Hosoya.pdf:pdf},
month = {sep},
pages = {1},
title = {{Natural image learning predic1ng invariant representa1ons in V2}},
year = {2012}
}
@article{Gomez2015,
abstract = {Neuron, 85 (2015) 216-227. doi:10.1016/j.neuron.2014.12.027},
author = {Gomez, Jesse and Pestilli, Franco and Witthoft, Nathan and Golarai, Golijeh and Liberman, Alina and Poltoratski, Sonia and Yoon, Jennifer and Grill-Spector, Kalanit},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neuron 2015 Gomez.pdf:pdf},
journal = {Neuron},
month = {jan},
number = {1},
pages = {216--227},
title = {{Functionally Defined White Matter Reveals Segregated Pathways in Human Ventral Temporal Cortex Associated with Category-Specific Processing}},
volume = {85},
year = {2015}
}
@article{Nadler2009,
author = {Nadler, Jacob W and Nawrot, Mark and Angelaki, Dora E and DeAngelis, Gregory C},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neuron 2009 Nadler.pdf:pdf},
journal = {Neuron},
month = {aug},
number = {4},
pages = {523--532},
title = {{MT Neurons Combine Visual Motion with a Smooth Eye Movement Signal to Code Depth-Sign from Motion Parallax}},
volume = {63},
year = {2009}
}
@article{Michel2013,
abstract = {Nature Neuroscience 16, 1477 (2013). doi:10.1038/nn.3517},
author = {Michel, Melchi M and Chen, Yuzhi and Geisler, Wilson S and Seidemann, Eyal},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature Publishing Group 2013 Michel.pdf:pdf},
journal = {Nat. Neurosci.},
month = {sep},
number = {10},
pages = {1477--1483},
title = {{An illusion predicted by V1 population activity implicates cortical topography in shape perception}},
volume = {16},
year = {2013}
}
@article{Rezende2016a,
abstract = {Abstract A key goal of computer vision is to recover the underlying 3D structure that gives rise to 2D observations of the world. If endowed with 3D understanding, agents can abstract away from the complexity of the rendering process to form stable, disentangled representations of scene elements. In this paper we learn strong deep generative models of 3D structures, and recover these structures from 2D images via probabilistic inference. We ...
},
author = {Rezende, D J and Eslami, S M A and Mohamed, S},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Advances in neural {\ldots} 2016 Rezende.pdf:pdf},
journal = {Adv. Neural Inf. Process. Syst.},
month = {jan},
title = {{Unsupervised learning of 3d structure from images}},
year = {2016}
}
@article{Bornschein2017,
abstract = {Aiming to augment generative models with external memory, we interpret the output of a memory module with stochastic addressing as a conditional mixture distribution, where a read operation corresponds to sampling a discrete memory address and retrieving the corresponding content from memory. This perspective allows us to apply variational inference to memory addressing, which enables effective training of the memory module by using the target information to guide memory lookups. Stochastic addressing is particularly well-suited for generative models as it naturally encourages multimodality which is a prominent aspect of most high-dimensional datasets. Treating the chosen address as a latent variable also allows us to quantify the amount of information gained with a memory lookup and measure the contribution of the memory module to the generative process. To illustrate the advantages of this approach we incorporate it into a variational autoencoder and apply the resulting model to the task of generative few-shot learning. The intuition behind this architecture is that the memory module can pick a relevant template from memory and the continuous part of the model can concentrate on modeling remaining variations. We demonstrate empirically that our model is able to identify and access the relevant memory contents even with hundreds of unseen Omniglot characters in memory.},
archivePrefix = {arXiv},
arxivId = {1709.07116},
author = {Bornschein, J{\"{o}}rg and Mnih, Andriy and Zoran, Daniel and Rezende, Danilo J.},
eprint = {1709.07116},
file = {:Users/hahosoya/Documents/Mendeley Desktop/1d518b5a2e5e9a9f89e62fd54ce8ec971ae60c98.pdf:pdf},
journal = {Adv. Neural Inf. Process. Syst.},
pages = {3921--3930},
title = {{Variational memory addressing in generative models}},
volume = {2017-Decem},
year = {2017}
}
@article{Wainwright1999,
abstract = {Abstract The statistics of photographic images , when represented using multiscale (wavelet) bases, exhibit two striking types of non- Gaussian behavior. First, the marginal densities of the coe cients have extended heavy tails. Second, the joint densities exhibit variance  ...},
author = {Wainwright, M J and Simoncelli, E P},
file = {:Users/hahosoya/Documents/Mendeley Desktop/NIPS 1999 Wainwright.pdf:pdf},
journal = {NIPS},
month = {jan},
title = {{Scale mixtures of Gaussians and the statistics of natural images.}},
year = {1999}
}
@article{Torralba2003,
abstract = {In this paper we study the statistical properties of natural images belonging to different categories and their relevance for scene and object categorization tasks. We discuss how second-order statistics are correlated with image categories, scene scale and objects. We propose how scene categorization could be computed in a feedforward manner in order to provide top-down and contextual information very early in the visual processing chain. Results show how visual categorization based directly on low-level features, without grouping or segmentation stages, can benefit object localization and identification. We show how simple image statistics can be used to predict the presence and absence of objects in the scene before exploring the image.},
author = {Torralba, Antonio and Oliva, Aude},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2003 Torralba.pdf:pdf},
month = {jan},
title = {{Statistics of natural image categories}},
year = {2003}
}
@article{Kersten2003,
abstract = {The human visual system is the most complex pattern recognition device known. In ways that are yet to be fully understood, the visual cortex arrives at a simple and unambiguous interpretation of data from the retinal image that is useful for the decisions and actions of everyday life. ...},
author = {Kersten, D},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Curr. Opin. Neurobiol. 2003 Kersten.pdf:pdf},
journal = {Curr. Opin. Neurobiol.},
month = {jan},
title = {{Bayesian models of object perception}},
year = {2003}
}
@article{Smith2012,
author = {Smith, Stephen M and Miller, Karla L and Moeller, Steen and Xu, Junqian and Auerbach, Edward J and Woolrich, Mark W and Beckmann, Christian F and Jenkinson, Mark and Andersson, Jesper and Glasser, Matthew F},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proc. Natl. Acad. Sci. U.S.A. 2012 Smith.pdf:pdf},
journal = {Proc. Natl. Acad. Sci. U. S. A.},
month = {jan},
number = {8},
pages = {3131--3136},
title = {{Temporally-independent functional modes of spontaneous brain activity}},
volume = {109},
year = {2012}
}
@article{Kosai2014,
author = {Kosai, Y and El-Shamayleh, Y and Fyall, A M and Pasupathy, A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2014 Kosai.pdf:pdf},
journal = {J. Neurosci.},
month = {jun},
number = {25},
pages = {8570--8584},
title = {{The Role of Visual Area V4 in the Discrimination of Partially Occluded Shapes}},
volume = {34},
year = {2014}
}
@article{Ringach2002,
abstract = {Probing the visual system with the ensemble of signals that occur in the natural environment may reveal aspects of processing that are not evident in the neural responses to artificial stimulus sets, such as conventional bars and sinusoidal gratings. However, unsolved is the ...},
author = {Ringach, D L and Hawken, M J and Shapley, R},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Vision 2002 Ringach.pdf:pdf},
journal = {J. Vis.},
month = {jan},
number = {1},
pages = {2},
title = {{Receptive field structure of neurons in monkey primary visual cortex revealed by stimulation with natural image sequences}},
volume = {2},
year = {2002}
}
@article{Tsao2008,
author = {Tsao, Doris Y and Schweers, Nicole and Moeller, Sebastian and Freiwald, Winrich A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature neuroscience 2008 Tsao-1.pdf:pdf},
journal = {Nat. Neurosci.},
month = {jul},
number = {8},
pages = {877--879},
title = {{Patches of face-selective cortex in the macaque frontal lobe}},
volume = {11},
year = {2008}
}
@article{Rothschild2010,
abstract = {Cortical processing of auditory stimuli involves large populations of neurons with distinct individual response profiles. However, the functional organization and dynamics of local populations in the auditory cortex have remained largely unknown. Using in vivo two-photon calcium imaging, we examined the response profiles and network dynamics of layer 2/3 neurons in the primary auditory cortex (A1) of mice in response to pure tones. We found that local populations in A1 were highly heterogeneous in the large-scale tonotopic organization. Despite the spatial heterogeneity, the tendency of neurons to respond together (measured as noise correlation) was high on average. This functional organization and high levels of noise correlations are consistent with the existence of partially overlapping cortical subnetworks. Our findings may account for apparent discrepancies between ordered large-scale organization and local heterogeneity.},
address = {Department of Neurobiology, Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel.},
author = {Rothschild, Gideon and Nelken, Israel and Mizrahi, Adi},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature Publishing Group 2010 Rothschild.pdf:pdf},
journal = {Nat. Publ. Gr.},
month = {mar},
number = {3},
pages = {353--360},
title = {{Functional organization and population dynamics in the mouse primary auditory cortex.}},
volume = {13},
year = {2010}
}
@article{Kornblith2017,
abstract = {We are rapidly approaching a comprehensive understanding of the neural mechanisms behind object recognition. How we use this knowledge of the visual world to plan and act is comparatively mysterious. To fill this gap, we must understand how visual representations are transformed within cognitive regions, and how these cognitive representations of visual information act back upon earlier sensory representations. Here, we summarize our current understanding of visual representation in inferotemporal cortex (IT) and prefrontal cortex (PFC), and the interactions between them. We emphasize the apparent consistency of visual representation in PFC across tasks, and suggest ways to leverage advances in our understanding of high-level vision to better understand cognitive processing.},
author = {Kornblith, Simon and Tsao, Doris Y.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/1-s2.0-S095943881730140X-main.pdf:pdf},
journal = {Curr. Opin. Neurobiol.},
pages = {208--218},
title = {{How thoughts arise from sights: inferotemporal and prefrontal contributions to vision}},
volume = {46},
year = {2017}
}
@article{McManus2011,
abstract = {The ability to derive meaning from complex sensory input requires the integration of information over space and time, as well as cognitive mechanisms to shape that integration. We studied these processes in the primary visual cortex (V1), where neurons are thought to integrate visual inputs along contours defined by an association field (AF). We recorded extracellularly from single cells in macaque V1 to map the AF, by using an optimization algorithm to find the contours that maximally activated individual cells. We combined the algorithm with a delayed-match-to-sample task, to test how the optimal contours might be molded by the monkey's expectation for particular cue shapes. We found that V1 neurons were selective for complex shapes, a property previously ascribed to higher cortical areas. Furthermore, the shape selectivity was reprogrammed by perceptual task: Over the whole network, the optimal modes of geometric selectivity shifted between distinct subsets of the AF, alternately representing different stimulus features known to predominate in natural scenes. Our results suggest a general model of cortical function, whereby horizontal connections provide a broad domain of potential associations, and top-down inputs dynamically gate these linkages to task switch the function of a network.},
address = {Laboratory of Neurobiology, The Rockefeller University, New York, NY 10065, USA.},
author = {McManus, Justin N J and Li, Wu and Gilbert, Charles D},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proceedings of the National Academy of Sciences 2011 McManus.pdf:pdf},
journal = {Proc. Natl. Acad. Sci.},
month = {jun},
number = {24},
pages = {9739--9746},
title = {{Adaptive shape processing in primary visual cortex.}},
volume = {108},
year = {2011}
}
@article{Satoh2008,
abstract = {A mathematical model for filling -in at the blind spot is proposed. The general scheme of the standard regularization theory was used to derive the model deductively. First, we present the problems encountered with a diffusion equation, which is frequently used for various types of ...},
author = {Satoh, S},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2008 Satoh.pdf:pdf},
journal = {Neural Networks},
month = {jan},
title = {{Computational theory and applications of a filling-in process at the blind spot}},
year = {2008}
}
@article{Riesenhuber1999,
abstract = {Visual processing in cortex is classically modeled as a hierarchy of increasingly sophisticated representations, naturally extending the model of simple to complex cells of Hubel and Wiesel. Surprisingly, little quantitative modeling has been done to explore the biological feasibility of this class of models to explain aspects of higher-level visual processing such as object recognition. We describe a new hierarchical model consistent with physiological data from inferotemporal cortex that accounts for this complex visual task and makes testable predictions. The model is based on a MAX-like operation applied to inputs to certain cortical neurons that may have a general role in cortical function.},
address = {Department of Brain and Cognitive Sciences, Center for Biological and Computational Learning and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142, USA.},
author = {Riesenhuber, M and Poggio, T},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature neuroscience 1999 Riesenhuber-1.pdf:pdf},
journal = {Nat. Neurosci.},
month = {nov},
number = {11},
pages = {1019--1025},
title = {{Hierarchical models of object recognition in cortex.}},
volume = {2},
year = {1999}
}
@article{Byrnes2011,
abstract = {P1: ZXP NECO_a_00184-Byrnes NECO.cls June 30, 2011 14:48  ... Learning a Sparse Code  
for Temporal Sequences Using STDP and Sequence Compression   ... Sean Byrnes
smbyrnes@gmail.com Bionic Ear Institute, East Melbourne, Victoria 3002, Australia, and  ...},
author = {Byrnes, S and Burkitt, A N and et Al},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neural Computation 2011 Byrnes.pdf:pdf},
journal = {Neural Comput.},
month = {jan},
title = {{Learning a Sparse Code for Temporal Sequences Using STDP and Sequence Compression}},
year = {2011}
}
@article{Taylor2010,
abstract = {Abstract. We address the problem of learning good features for under- standing video data. We 
introduce a model that learns latent represen- tations of image sequences from pairs of successive
images. The convolu- tional architecture of our model allows it to scale to realistic image  ...},
author = {Taylor, G and Fergus, R and et Al},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Computer Vision–ECCV 2010 2010 Taylor.pdf:pdf},
journal = {Comput. Vision–ECCV 2010},
month = {jan},
title = {{Convolutional learning of spatio-temporal features}},
year = {2010}
}
@article{VanHateren1998a,
abstract = {...  Hateren , JH van 1992a Theoretical predictions of spatio - temporal receptive ¢elds of {\pounds}y LMCs, and experimental validation. ...  Hateren , JH van 1997 Processing of natural time series of inten- sities by the visual system ... Hurri, J. 1997 Independent component analysis of image data ...},
author = {van Hateren, J H and van der Schaaf, A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proceedings of the Royal Society B Biological Sciences 1998 van Hateren.pdf:pdf},
journal = {Proc. R. Soc. B Biol. Sci.},
month = {mar},
number = {1394},
pages = {359--366},
title = {{Independent component filters of natural images compared with simple cells in primary visual cortex}},
volume = {265},
year = {1998}
}
@article{Oja1985,
author = {Oja, Erkki and Karhunen, Juha},
file = {:Users/hahosoya/Documents/Mendeley Desktop/1985 Oja.pdf:pdf},
journal = {J. Math. Anal. Appl.},
month = {jan},
number = {1},
pages = {69--84},
title = {{On stochastic approximation of the eigenvectors and eigenvalues of the expectation of a random matrix}},
volume = {106},
year = {1985}
}
@article{Kim2017,
abstract = {Learning to transfer visual attributes requires supervision dataset. Corresponding images with varying attribute values with the same identity are required for learning the transfer function. This largely limits their applications, because capturing them is often a difficult task. To address the issue, we propose an unsupervised method to learn to transfer visual attribute. The proposed method can learn the transfer function without any corresponding images. Inspecting visualization results from various unsupervised attribute transfer tasks, we verify the effectiveness of the proposed method.},
author = {Kim, Taeksoo and Kim, Byoungjip and Cha, Moonsu and Kim, Jiwon},
file = {:Users/hahosoya/Documents/Mendeley Desktop/28D8A22F-20FF-47DD-809F-1C348E2A3C55.pdf:pdf},
journal = {CoRR},
month = {jan},
title = {{Unsupervised Visual Attribute Transfer with Reconfigurable Generative Adversarial Networks.}},
volume = {cs.CV},
year = {2017}
}
@article{Valdez2015,
author = {Valdez, A B and Papesh, M H and Treiman, D M and Smith, K A and Goldinger, S D and Steinmetz, P N},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2015 Valdez.pdf:pdf},
journal = {J. Neurosci.},
month = {apr},
number = {13},
pages = {5180--5186},
title = {{Distributed Representation of Visual Objects by Single Neurons in the Human Brain}},
volume = {35},
year = {2015}
}
@article{Santoro2018,
abstract = {Memory-based neural networks model temporal data by leveraging an ability to remember information for long periods. It is unclear, however, whether they also have an ability to perform complex relational reasoning with the information they remember. Here, we first confirm our intuitions that standard memory architectures may struggle at tasks that heavily involve an understanding of the ways in which entities are connected - i.e., tasks involving relational reasoning. We then improve upon these deficits by using a new memory module - a Relational Memory Core (RMC) - which employs multi-head dot product attention to allow memories to interact. Finally, we test the RMC on a suite of tasks that may profit from more capable relational reasoning across sequential information, and show large gains in RL domains (e.g. Mini PacMan), program evaluation, and language modeling, achieving state-of-the-art results on the WikiText-103, Project Gutenberg, and GigaWord datasets.},
archivePrefix = {arXiv},
arxivId = {1806.01822},
author = {Santoro, Adam and Faulkner, Ryan and Raposo, David and Rae, Jack and Chrzanowski, Mike and Weber, Th{\'{e}}ophane and Wierstra, Daan and Vinyals, Oriol and Pascanu, Razvan and Lillicrap, Timothy},
eprint = {1806.01822},
file = {:Users/hahosoya/Documents/Mendeley Desktop/1806.01822.pdf:pdf},
journal = {Adv. Neural Inf. Process. Syst.},
pages = {7299--7310},
title = {{Relational recurrent neural networks}},
volume = {2018-Decem},
year = {2018}
}
@inproceedings{Higgins2016,
abstract = {Learning an interpretable factorised representation of the independent data generative factors of the world without supervision is an important precursor for the development of artificial...},
author = {Higgins, Irina and Matthey, Loic and Pal, Arka and Burgess, Christopher and Glorot, Xavier and Botvinick, Matthew and Mohamed, Shakir and Lerchner, Alexander},
booktitle = {Int. Conf. Learn. Represent.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/379DC605-B0BD-4801-B5D4-FE6E07E3B378.pdf:pdf},
month = {nov},
title = {{beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework}},
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@article{Hutchison2012,
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@article{Ruff2017,
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month = {may},
number = {20},
pages = {E4085--E4094},
title = {{A normalization model suggests that attention changes the weighting of inputs between visual areas}},
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@article{Avitan2018,
abstract = {Trends in Neurosciences, 41 (2018) 599-608. doi:10.1016/j.tins.2018.05.011},
author = {Avitan, Lilach and Goodhill, Geoffrey J},
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journal = {Trends Neurosci.},
month = {sep},
number = {9},
pages = {599--609},
title = {{Code Under Construction: Neural Coding Over Development}},
volume = {41},
year = {2018}
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@article{Kisvarday1997,
abstract = {The topography of lateral excitatory and lateral inhibitory connec- tions was studied in relation to orientation maps obtained in areas 17 and 18. Small iontophoretic injections of biocytin were delivered to the superficial layers in regions where orientation selectivity had been ...},
author = {Kisvarday, Z},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Cerebral Cortex 1997 Kisvarday.pdf:pdf},
journal = {Cereb. Cortex},
month = {oct},
number = {7},
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title = {{Orientation-specific relationship between populations of excitatory and inhibitory lateral connections in the visual cortex of the cat}},
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@article{El-Shamayleh2011,
author = {El-Shamayleh, Y and Movshon, J A},
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journal = {J. Neurosci.},
month = {jun},
number = {23},
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title = {{Neuronal Responses to Texture-Defined Form in Macaque Visual Area V2}},
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}
@article{Srivastava2014,
author = {Srivastava, Nitish and Hinton, Geoffrey and Krizhevsky, Alex and Sutskever, Ilya and Salakhutdinov, Ruslan},
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@article{Mansouri2020,
abstract = {Various aspects of human cognition are shaped and enriched by abstract rules, which help to describe, link and classify discrete events and experiences into meaningful concepts. However, where and how these entities emerge in the primate brain and the neuronal mechanisms underlying them remain the subject of extensive research and debate. Evidence from imaging studies in humans and single-neuron recordings in monkeys suggests a pivotal role for the prefrontal cortex in the representation of abstract rules; however, behavioural studies in lesioned monkeys and data from neuropsychological examinations of patients with prefrontal damage indicate substantial functional dissociations and task dependency in the contribution of prefrontal cortical regions to rule-guided behaviour. This Review describes our current understanding of the dynamic emergence of abstract rules in primate cognition, and of the distributed neural network that supports abstract rule formation, maintenance, revision and task-dependent implementation.},
author = {Mansouri, Farshad Alizadeh and Freedman, David J. and Buckley, Mark J.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/s41583-020-0364-5.pdf:pdf},
journal = {Nat. Rev. Neurosci.},
number = {11},
pages = {595--610},
pmid = {32929262},
title = {{Emergence of abstract rules in the primate brain}},
volume = {21},
year = {2020}
}
@article{Auslander2012,
abstract = {Nature 487, 123 (2012). doi:10.1038/nature11149},
author = {Ausl{\"{a}}nder, Simon and Ausl{\"{a}}nder, David and M{\"{u}}ller, Marius and Wieland, Markus and Fussenegger, Martin},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature 2012 Ausl{\"{a}}nder.pdf:pdf},
journal = {Nature},
month = {jun},
number = {7405},
pages = {123--127},
title = {{Programmable single-cell mammalian biocomputers}},
volume = {487},
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}
@article{McNamee2021,
abstract = {Exploration, consolidation and planning depend on the generation of sequential state representations. However, these algorithms require disparate forms of sampling dynamics for optimal performance. We theorize how the brain should adapt internally generated sequences for particular cognitive functions and propose a neural mechanism by which this may be accomplished within the entorhinal–hippocampal circuit. Specifically, we demonstrate that the systematic modulation along the medial entorhinal cortex dorsoventral axis of grid population input into the hippocampus facilitates a flexible generative process that can interpolate between qualitatively distinct regimes of sequential hippocampal reactivations. By relating the emergent hippocampal activity patterns drawn from our model to empirical data, we explain and reconcile a diversity of recently observed, but apparently unrelated, phenomena such as generative cycling, diffusive hippocampal reactivations and jumping trajectory events.},
author = {McNamee, Daniel C. and Stachenfeld, Kimberly L. and Botvinick, Matthew M. and Gershman, Samuel J.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/s41593-021-00831-7.pdf:pdf},
journal = {Nat. Neurosci.},
pmid = {33846626},
title = {{Flexible modulation of sequence generation in the entorhinal–hippocampal system}},
year = {2021}
}
@article{Hurri2003a,
abstract = {We present a two-layer dynamic generative model of the statistical structure of natural image sequences. The second layer of the model is a linear mapping from simple-cell outputs to pixel values, as in most work on natural image statistics. The first layer models the dependencies of the activity levels (amplitudes or variances) of the simple cells, using a multivariate autoregressive model. The second layer shows the emergence of basis vectors that are localized, oriented and have different scales, just like in previous work. But in our new model, the first layer learns connections between the simple cells that are similar to complex cell pooling: connections are strong among cells with similar preferred location, frequency and orientation. In contrast to previous work in which one of the layers needed to be fixed in advance, the dynamic model enables us to estimate both of the layers simultaneously from natural data.},
address = {Neural Networks Research Centre, Helsinki University of Technology, PO Box 9800, 02015 HUT, Finland. jarmo.hurri@hut.fi},
author = {Hurri, Jarmo and Hyv{\"{a}}rinen, Aapo},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Network Computation in Neural Systems 2003 Hurri.pdf:pdf},
journal = {Netw. Comput. Neural Syst.},
month = {aug},
number = {3},
pages = {527--551},
title = {{Temporal and spatiotemporal coherence in simple-cell responses: a generative model of natural image sequences.}},
volume = {14},
year = {2003}
}
@article{Vintch2015,
author = {Vintch, B and Movshon, J A and Simoncelli, E P},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2015 Vintch.pdf:pdf},
journal = {J. Neurosci.},
month = {nov},
number = {44},
pages = {14829--14841},
title = {{A Convolutional Subunit Model for Neuronal Responses in Macaque V1}},
volume = {35},
year = {2015}
}
@article{Kohonen1972,
abstract = {Abstract-A new model for associative memory, based on a correla-matrix, and the rest of the elements are put equal to zero. In tion matrix, is suggested. In this model information is accumulated on a t-$\sim$$\sim$$\sim$$\sim$$\sim$$\sim$$\sim$$\sim$$\sim$$\sim$$\sim$$\sim$$\sim$ addition to such a ranldomly sam led matrix, we ...
},
author = {Kohonen, T},
file = {:Users/hahosoya/Documents/Mendeley Desktop/IEEE Transactions on Computers 1972 Kohonen.pdf:pdf},
journal = {IEEE Trans. Comput.},
month = {jan},
pages = {353--359},
title = {{Correlation matrix memories}},
volume = {c-21},
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}
@article{Hurri2003,
abstract = {Recently, statistical models of natural images have shown the emergence of several properties of the visual cortex. Most models have considered the nongaussian properties of static image patches, leading to sparse cod- ing or independent component analysis. Here we ...},
author = {Hurri, Jarmo and Hyv{\"{a}}rinen, Aapo},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neural Computation 2003 Hurri.pdf:pdf},
journal = {Neural Comput.},
month = {mar},
number = {3},
pages = {663--691},
title = {{Simple-Cell-Like Receptive Fields Maximize Temporal Coherence in Natural Video}},
volume = {15},
year = {2003}
}
@article{Raina2003,
abstract = {Abstract Although discriminatively trained classifiers are usually more accurate when labeled training data is abundant, previous work has shown that when training data is limited, generative classifiers can out-perform them. This paper describes a hybrid model in which a high-dimensional subset of the parameters are trained to maximize generative likelihood, and another, small, subset of parameters are discriminatively trained to ...
},
author = {Raina, R and Shen, Y and Ng, A Y and McCallum, A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/NIPS 2003 Raina.pdf:pdf},
journal = {NIPS},
month = {jan},
title = {{Classification with hybrid generative/discriminative models.}},
year = {2003}
}
@article{Silverstein2017,
author = {Silverstein, Steven M. and Demmin, Docia L. and Bednar, James A.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/CPSY_a_00005.pdf:pdf},
journal = {Comput. Psychiatry},
keywords = {bayesian,dynamics,model selection,nosology,pathophysio,pathophysiology,psychiatry,psychopathology},
month = {dec},
pages = {102--131},
title = {{Computational Modeling of Contrast Sensitivity and Orientation Tuning in First-Episode and Chronic Schizophrenia}},
volume = {1},
year = {2017}
}
@article{Smith2018,
abstract = {Nature Neuroscience, doi:10.1038/s41593-018-0247-5},
author = {Smith, Gordon B and Hein, Bettina and Whitney, David E and Fitzpatrick, David and Kaschube, Matthias},
file = {:Users/hahosoya/Documents/Mendeley Desktop/ACE63934-B4D0-4D4A-A373-28532E6E89F5.pdf:pdf},
journal = {Nat. Neurosci.},
month = {oct},
number = {11},
pages = {1--16},
title = {{Distributed network interactions and their emergence in developing neocortex}},
volume = {21},
year = {2018}
}
@article{Pinsk2005,
author = {Pinsk, Mark A and DeSimone, Kevin and Moore, Tirin and Gross, Charles G and Kastner, Sabine},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proceedings of the National Academy of Sciences USA 2005 Pinsk.pdf:pdf},
journal = {Proc. Natl. Acad. Sci. USA},
month = {jan},
number = {19},
pages = {6996--7001},
title = {{Representations of faces and body parts in macaque temporal cortex: a functional MRI study}},
volume = {102},
year = {2005}
}
@article{Markov2014,
abstract = {ABSTRACT The laminar location of the cell bodies and terminals of interareal connections determines the hierarchical structural organization of the cortex and has been intensively studied. However, we still have only a rudimentary understanding of the connectional  ...},
author = {Markov, N T and Vezoli, J and Chameau, P and et Al},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of {\ldots} 2014 Markov.pdf:pdf},
journal = {J.  {\ldots}},
month = {jan},
title = {{Anatomy of hierarchy: Feedforward and feedback pathways in macaque visual cortex}},
year = {2014}
}
@article{Heeger1992a,
abstract = {Simple cells in the striate cortex have been depicted as half-wave-rectified linear operators. Complex cells have been depicted as energy mechanisms, constructed from the squared sum of the outputs of quadrature pairs of linear operators. However, the linear/energy model falls short of a complete explanation of striate cell responses. In this paper, a modified version of the linear/energy model is presented in which striate cells mutually inhibit one another, effectively normalizing their responses with respect to stimulus contrast. This paper reviews experimental measurements of striate cell responses, and shows that the new model explains a significantly larger body of physiological data.},
address = {NASA-Ames Research Center, Moffett Field, California.},
author = {Heeger, D J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Visual Neuroscience 1992 Heeger-2.pdf:pdf},
journal = {Vis. Neurosci.},
month = {aug},
number = {2},
pages = {181--197},
title = {{Normalization of cell responses in cat striate cortex.}},
volume = {9},
year = {1992}
}
@article{Huth2016,
abstract = {Nature 532, 453 (2016). doi:10.1038/nature17637},
author = {Huth, Alexander G and de Heer, Wendy A and Griffiths, Thomas L and Theunissen, Fr{\'{e}}d{\'{e}}ric E and Gallant, Jack L},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature 2016 Huth.pdf:pdf},
journal = {Nature},
month = {apr},
number = {7600},
pages = {453--458},
title = {{Natural speech reveals the semantic maps that tile human cerebral cortex}},
volume = {532},
year = {2016}
}
@article{Berkes2009,
abstract = {The visual system must learn to infer the presence of objects and features in the world from the images it encounters, and as such it must, either implicitly or explicitly, model the way these elements interact to create the image. Do the response properties of cells in the mammalian visual system reflect this constraint? To address this question, we constructed a probabilistic model in which the identity and attributes of simple visual elements were represented explicitly and learnt the parameters of this model from unparsed, natural video sequences. After learning, the behaviour and grouping of variables in the probabilistic model corresponded closely to functional and anatomical properties of simple and complex cells in the primary visual cortex (V1). In particular, feature identity variables were activated in a way that resembled the activity of complex cells, while feature attribute variables responded much like simple cells. Furthermore, the grouping of the attributes within the model closely parallelled the reported anatomical grouping of simple cells in cat V1. Thus, this generative model makes explicit an interpretation of complex and simple cells as elements in the segmentation of a visual scene into basic independent features, along with a parametrisation of their moment-by-moment appearances. We speculate that such a segmentation may form the initial stage of a hierarchical system that progressively separates the identity and appearance of more articulated visual elements, culminating in view-invariant object recognition.},
address = {Gatsby Computational Neuroscience Unit, London, United Kingdom. berkes@brandeis.edu},
author = {Berkes, Pietro and Turner, Richard E and Sahani, Maneesh},
editor = {K{\"{o}}rding, Konrad},
file = {:Users/hahosoya/Documents/Mendeley Desktop/PLoS Comp Biol 2009 Berkes.pdf:pdf},
journal = {PLoS Comput. Biol.},
month = {sep},
number = {9},
pages = {e1000495},
title = {{A structured model of video reproduces primary visual cortical organisation.}},
volume = {5},
year = {2009}
}
@article{Kozlov2016,
author = {Kozlov, Andrei S and Gentner, Timothy Q},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proceedings of the National Academy of Sciences 2016 Kozlov.pdf:pdf},
journal = {Proc. Natl. Acad. Sci.},
month = {jan},
pages = {201506903},
title = {{Central auditory neurons have composite receptive fields}},
year = {2016}
}
@article{Geisler2001,
abstract = {The human brain manages to correctly interpret almost every visual image it receives from the 
environment. Underlying this ability are contour grouping mechanisms that appropriately link
local edge elements into global contours . Although a general view of how the brain  ...},
author = {Geisler, W S and Perry, J S and et Al},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Vision Research 2001 Geisler.pdf:pdf},
journal = {Vision Res.},
month = {jan},
title = {{Edge co-occurrence in natural images predicts contour grouping performance}},
year = {2001}
}
@article{Landi2017,
abstract = {Familiarity alters face recognition: Familiar faces are recognized more accurately than unfamiliar ones and under difficult viewing conditions when unfamiliar face recognition fails. The neural basis for this fundamental difference remains unknown. Using whole-brain functional magnetic resonance imaging, we found that personally familiar faces engage the macaque face-processing network more than unfamiliar faces. Familiar faces also recruited two hitherto unknown face areas at anatomically conserved locations within the perirhinal cortex and the temporal pole. These two areas, but not the core face-processing network, responded to familiar faces emerging from a blur with a characteristic nonlinear surge, akin to the abruptness of familiar face recognition. In contrast, responses to unfamiliar faces and objects remained linear. Thus, two temporal lobe areas extend the core face-processing network into a familiar face-recognition system.},
address = {The Rockefeller University, 1230 York Avenue, New York, NY 10065, USA. slandi@rockefeller.edu wfreiwald@rockefeller.edu.},
author = {Landi, Sofia M and Freiwald, Winrich A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/84BD099E-0ADE-42A8-A122-ED0C18B9ECF7.pdf:pdf},
journal = {Science},
month = {aug},
number = {6351},
pages = {591--595},
title = {{Two areas for familiar face recognition in the primate brain.}},
volume = {357},
year = {2017}
}
@article{Olshausen2004,
abstract = {Several theoretical, computational, and experimental studies suggest that neurons encode sensory information using a small number of active neurons at any given point in time. This strategy, referred to as 'sparse coding', could possibly confer several advantages. First, it allows for ...},
author = {Olshausen, B A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Curr. Opin. Neurobiol. 2004 Olshausen.pdf:pdf},
journal = {Curr. Opin. Neurobiol.},
month = {jan},
title = {{Sparse coding of sensory inputs}},
year = {2004}
}
@article{Grunewald2004,
author = {Grunewald, A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2004 Grunewald.pdf:pdf},
journal = {J. Neurosci.},
month = {oct},
number = {41},
pages = {9185--9194},
title = {{The Integration of Multiple Stimulus Features by V1 Neurons}},
volume = {24},
year = {2004}
}
@article{Yi2018,
abstract = {We marry two powerful ideas: Deep representation learning for visual recognition and language understanding, and symbolic program execution for reasoning. Our neural-symbolic visual question answering (NS-VQA) system first recovers a structural scene representation from the image and a program trace from the question. It then executes the program on the scene representation to obtain an answer. Incorporating symbolic structure as prior knowledge offers three unique advantages. First, executing programs on a symbolic space is more robust to long program traces; our model can solve complex reasoning tasks better, achieving an accuracy of 99.8% on the CLEVR dataset. Second, the model is more data- and memory-efficient: It performs well after learning on a small number of training data; it can also encode an image into a compact representation, requiring less storage than existing methods for offline question answering. Third, symbolic program execution offers full transparency to the reasoning process; we are thus able to interpret and diagnose each execution step.},
author = {Yi, Kexin and Wu, Jiajun and Gan, Chuang and Torralba, Antonio and Kohli, Pushmeet and Tenenbaum, Joshua B.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/e908c5b80f6da99c5bad585deb2bc905927f82b1.pdf:pdf},
journal = {arXiv},
number = {NeurIPS},
title = {{Neural-symbolic VQA: Disentangling reasoning from vision and language understanding}},
year = {2018}
}
@article{Paninski2003,
abstract = {We analyse the convergence properties of three spike-triggered data analysis techniques. Our results are obtained in the setting of a probabilistic linear-nonlinear (LN) cascade neural encoding model; this model has recently become popular in the study of the neural coding of natural signals. We start by giving exact rate-of-convergence results for the common spike-triggered average technique. Next, we analyse a spike-triggered covariance method, variants of which have been recently exploited successfully by Bialek, Simoncelli and colleagues. Unfortunately, the conditions that guarantee that these two estimators will converge to the correct parameters are typically not satisfied by natural signal data. Therefore, we introduce an estimator for the LN model parameters which is designed to converge under general conditions to the correct model. We derive the rate of convergence of this estimator, provide an algorithm for its computation and demonstrate its application to simulated data as well as physiological data from the primary motor cortex of awake behaving monkeys. We also give lower bounds on the convergence rate of any possible LN estimator. Our results should prove useful in the study of the neural coding of high-dimensional natural signals.},
address = {Center for Neural Science, New York University, 4 Washington Place, New York, NY 10003, USA. liam@cns.nyu.edu},
author = {Paninski, Liam},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Network Computation in Neural Systems 2003 Paninski.pdf:pdf},
journal = {Netw. Comput. Neural Syst.},
month = {aug},
number = {3},
pages = {437--464},
title = {{Convergence properties of three spike-triggered analysis techniques.}},
volume = {14},
year = {2003}
}
@article{Kim2019,
abstract = {The distinct visual sensations of shape and texture have been studied separately in cortex; therefore, it remains unknown whether separate neuronal populations encode each of these properties or one population carries a joint encoding. We directly compared shape and texture selectivity of individual V4 neurons in awake macaques (1 male, 1 female) and found that V4 neurons lie along a continuum from strong tuning for boundary curvature of shapes to strong tuning for perceptual dimensions of texture. Among neurons tuned to both attributes, tuning for shape and texture were largely separable, with the latter delayed by $\sim$30 ms. We also found that shape stimuli typically evoked stronger, more selective responses than did texture patches, regardless of whether the latter were contained within or extended beyond the receptive field. These results suggest that there are separate specializations in mid-level cortical processing for visual attributes of shape and texture.},
author = {Kim, Taekjun and Bair, Wyeth and Pasupathy, Anitha},
file = {:Users/hahosoya/Documents/Mendeley Desktop/4760.full.pdf:pdf},
journal = {J. Neurosci.},
keywords = {Monkey,Object recognition,Rhesus macaque,Shape,Texture,Ventral visual pathway},
number = {24},
pages = {4760--4774},
title = {{Neural coding for shape and texture in macaque area V4}},
volume = {39},
year = {2019}
}
@article{Kimura2009,
author = {Kimura, R and Kimura, R and Ohzawa, I},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neurophysiology 2009 Kimura.pdf:pdf},
journal = {J. Neurophysiol.},
month = {feb},
number = {3},
pages = {1463--1479},
title = {{Time Course of Cross-Orientation Suppression in the Early Visual Cortex}},
volume = {101},
year = {2009}
}
@article{VonKugelgen2021,
abstract = {Self-supervised representation learning has shown remarkable success in a number of domains. A common practice is to perform data augmentation via hand-crafted transformations intended to leave the semantics of the data invariant. We seek to understand the empirical success of this approach from a theoretical perspective. We formulate the augmentation process as a latent variable model by postulating a partition of the latent representation into a content component, which is assumed invariant to augmentation, and a style component, which is allowed to change. Unlike prior work on disentanglement and independent component analysis, we allow for both nontrivial statistical and causal dependencies in the latent space. We study the identifiability of the latent representation based on pairs of views of the observations and prove sufficient conditions that allow us to identify the invariant content partition up to an invertible mapping in both generative and discriminative settings. We find numerical simulations with dependent latent variables are consistent with our theory. Lastly, we introduce Causal3DIdent, a dataset of high-dimensional, visually complex images with rich causal dependencies, which we use to study the effect of data augmentations performed in practice.},
archivePrefix = {arXiv},
arxivId = {2106.04619},
author = {von K{\"{u}}gelgen, Julius and Sharma, Yash and Gresele, Luigi and Brendel, Wieland and Sch{\"{o}}lkopf, Bernhard and Besserve, Michel and Locatello, Francesco},
eprint = {2106.04619},
file = {:Users/hahosoya/Documents/Mendeley Desktop/029d85bfd9077f1020379b043e7601905086440b.pdf:pdf},
pages = {1--28},
title = {{Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style}},
year = {2021}
}
@article{Gomez2017,
abstract = {Abstract How does cortical tissue change as brain function and behavior improve from childhood to adulthood? By combining quantitative and functional magnetic resonance imaging in children and adults, we find differential development of high-level visual areas that are involved in face and place recognition. Development of face-selective regions, but not place-selective regions, is dominated by microstructural proliferation. This tissue ...
},
author = {Gomez, J and Barnett, M A and Natu, V and Mezer, A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2017 Gomez.pdf:pdf},
month = {jan},
title = {{Microstructural proliferation in human cortex is coupled with the development of face processing}},
year = {2017}
}
@article{Hyvarinen1998,
abstract = {A number of neural learning rules have been recently proposed for independent component analysis (ICA). The rules are usually derived from information-theoretic criteria such as maximum entropy or minimum mutual information. In this paper, we show that in fact, ICA  ...},
author = {Hyv{\"{a}}rinen, Aapo and Oja, E},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Signal Processing 1998 Hyv{\"{a}}rinen.pdf:pdf},
journal = {Signal Process. {\ldots}},
month = {jan},
title = {{Independent component analysis by general nonlinear Hebbian-like learning rules}},
year = {1998}
}
@article{Berndt2014,
author = {Berndt, A and Lee, S Y and Ramakrishnan, C and Deisseroth, K},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Science 2014 Berndt.pdf:pdf},
journal = {Science},
month = {apr},
number = {6182},
pages = {420--424},
title = {{Structure-Guided Transformation of Channelrhodopsin into a Light-Activated Chloride Channel}},
volume = {344},
year = {2014}
}
@article{Kanwisher1997,
abstract = {Using functional magnetic resonance imaging (fMRI), we found an area in the fusiform gyrus in 12 of the 15 subjects tested that was significantly more active when the subjects viewed faces than when they viewed assorted common objects. This face activation was used to define a specific region of interest individually for each subject, within which several new tests of face specificity were run. In each of five subjects tested, the predefined candidate "face area" also responded significantly more strongly to passive viewing of (1) intact than scrambled two-tone faces, (2) full front-view face photos than front-view photos of houses, and (in a different set of five subjects) (3) three-quarter-view face photos (with hair concealed) than photos of human hands; it also responded more strongly during (4) a consecutive matching task performed on three-quarter-view faces versus hands. Our technique of running multiple tests applied to the same region defined functionally within individual subjects provides a solution to two common problems in functional imaging: (1) the requirement to correct for multiple statistical comparisons and (2) the inevitable ambiguity in the interpretation of any study in which only two or three conditions are compared. Our data allow us to reject alternative accounts of the function of the fusiform face area (area "FF") that appeal to visual attention, subordinate-level classification, or general processing of any animate or human forms, demonstrating that this region is selectively involved in the perception of faces.},
address = {Department of Psychology, Harvard University, Cambridge, Massachusetts 02138, USA.},
author = {Kanwisher, N and McDermott, J and Chun, M M},
file = {:Users/hahosoya/Documents/Mendeley Desktop/The Journal of neuroscience 1997 Kanwisher.pdf:pdf},
journal = {J. Neurosci.},
month = {jun},
number = {11},
pages = {4302--4311},
title = {{The fusiform face area: a module in human extrastriate cortex specialized for face perception.}},
volume = {17},
year = {1997}
}
@article{Le2018,
abstract = {Introducing variability while maintaining coherence is a core task in learning to generate utterances in conversation. Standard neural encoder-decoder models and their extensions using conditional variational autoencoder often result in either trivial or digressive responses. To overcome this, we explore a novel approach that injects variability into neural encoder-decoder via the use of external memory as a mixture model, namely Variational Memory Encoder-Decoder (VMED). By associating each memory read with a mode in the latent mixture distribution at each timestep, our model can capture the variability observed in sequential data such as natural conversations. We empirically compare the proposed model against other recent approaches on various conversational datasets. The results show that VMED consistently achieves significant improvement over others in both metric-based and qualitative evaluations.},
archivePrefix = {arXiv},
arxivId = {1807.09950},
author = {Le, Hung and Tran, Truyen and Nguyen, Thin and Venkatesh, Svetha},
eprint = {1807.09950},
file = {:Users/hahosoya/Documents/Mendeley Desktop/8d6b93cc5049033fe7384166229a7f5afc813e3d.pdf:pdf},
journal = {Adv. Neural Inf. Process. Syst.},
pages = {1508--1518},
title = {{Variational memory encoder-decoder}},
volume = {2018-Decem},
year = {2018}
}
@article{Tong2006,
author = {Tong, Frank and Meng, Ming and Blake, Randolph},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Trends in cognitive Sciences 2006 Tong.pdf:pdf},
journal = {Trends Cogn. Sci.},
month = {nov},
number = {11},
pages = {502--511},
title = {{Neural bases of binocular rivalry}},
volume = {10},
year = {2006}
}
@article{Festa2021,
abstract = {Neuronal activity in sensory cortex fluctuates over time and across repetitions of the same input. This variability is often considered detrimental to neural coding. The theory of neural sampling proposes instead that variability encodes the uncertainty of perceptual inferences. In primary visual cortex (V1), modulation of variability by sensory and non-sensory factors supports this view. However, it is unknown whether V1 variability reflects the statistical structure of visual inputs, as would be required for inferences correctly tuned to the statistics of the natural environment. Here we combine analysis of image statistics and recordings in macaque V1 to show that probabilistic inference tuned to natural image statistics explains the widely observed dependence between spike count variance and mean, and the modulation of V1 activity and variability by spatial context in images. Our results show that the properties of a basic aspect of cortical responses—their variability—can be explained by a probabilistic representation tuned to naturalistic inputs.},
author = {Festa, Dylan and Aschner, Amir and Davila, Aida and Kohn, Adam and Coen-Cagli, Ruben},
file = {:Users/hahosoya/Documents/Mendeley Desktop/e8449872cebd7922d17c506f225e9bf7e440b063.pdf:pdf},
journal = {Nat. Commun.},
number = {1},
pages = {1--11},
pmid = {34131142},
title = {{Neuronal variability reflects probabilistic inference tuned to natural image statistics}},
volume = {12},
year = {2021}
}
@article{Kayser2003,
abstract = {Learning in neural networks is usually applied to parameters related to linear kernels and keeps the nonlinearity of the model fixed. Thus, for successful models, properties and parameters of the nonlinearity have to be specified using a priori knowledge, which often is missing. Here, we investigate adapting the nonlinearity simultaneously with the linear kernel. We use natural visual stimuli for training a simple model of the visual system. Many of the neurons converge to an energy detector matching existing models of complex cells. The overall distribution of the parameter describing the nonlinearity well matches recent physiological results. Controls with randomly shuffled natural stimuli and pink noise demonstrate that the match of simulation and experimental results depends on the higher-order statistical properties of natural stimuli.},
address = {Institute of Neuroinformatics, University / ETH Z{\"{u}}rich, Winterthurerstrasse 190, 8057 Z{\"{u}}rich, Switzerland. kayser@ini.phys.ethz.ch},
author = {Kayser, Christoph and Kording, Konrad P and K{\"{o}}nig, Peter},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neural Computation 2003 Kayser.pdf:pdf},
journal = {Neural Comput.},
month = {aug},
number = {8},
pages = {1751--1759},
title = {{Learning the nonlinearity of neurons from natural visual stimuli.}},
volume = {15},
year = {2003}
}
@article{Hoyer2003a,
author = {Hoyer, P O},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neurocomputing 2003 Hoyer.pdf:pdf},
journal = {Neurocomputing},
month = {jan},
pages = {547--552},
title = {{Modeling receptive fields with non-negative sparse coding}},
volume = {52},
year = {2003}
}
@article{Brenner2000,
abstract = {Abstract Adaptation is a widespread phenomenon in nervous systems, providing flexibility to function under varying external conditions. Here, we relate an adaptive property of a sensory system directly to its function as a carrier of information about input signals. We show that ... 
},
author = {Brenner, Naama and Bialek, William and {de Ruyter van Steveninck}, Rob},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neuron 2000 Brenner.pdf:pdf},
journal = {Neuron},
month = {jun},
number = {3},
pages = {695--702},
title = {{Adaptive Rescaling Maximizes Information Transmission}},
volume = {26},
year = {2000}
}
@article{Askenfelt1993,
author = {Askenfelt, Anders and Jansson, Erik V},
file = {:Users/hahosoya/Documents/Mendeley Desktop/J. Acoust. Soc. Am. 1993 Askenfelt.pdf:pdf},
journal = {J. Acoust. Soc. Am.},
month = {jan},
number = {4},
pages = {2181--2196},
title = {{From touch to string vibrations. III: String motion and spectra}},
volume = {93},
year = {1993}
}
@article{Wilkinson2006,
abstract = {...  Parallel  Bayesian  computation Darren Wilkinson School ... UK HPCSI 2006, Trinity College 
Dublin, August 23–25 2006 Darren Wilkinson — HPCSI 2006, Dublin, August 23–25 2006 Parallel  Bayesian  computation Page 2. Bayesian inference ...
},
author = {Wilkinson, D J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/STATISTICS TEXTBOOKS AND MONOGRAPHS 2006 Wilkinson.pdf:pdf},
journal = {Stat. Textb. Monogr.},
month = {jan},
title = {{Parallel bayesian computation}},
year = {2006}
}
@article{Raina2009,
abstract = {The promise of unsupervised learning meth- ods lies in their potential to use vast amounts of 
unlabeled data to learn complex, highly nonlinear models with millions of free param- eters. We
consider two well-known unsuper- vised learning models, deep belief networks (DBNs)  ...},
author = {Raina, R and et Al},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proceedings of the 26th Annual {\ldots} 2009 Raina.pdf:pdf},
journal = {Proc. 26th Annu.  {\ldots}},
month = {jan},
title = {{Large-scale deep unsupervised learning using graphics processors}},
year = {2009}
}
@article{Aronov2017,
abstract = {During spatial navigation, neural activity in the hippocampus and the medial entorhinal cortex (MEC) is correlated to navigational variables such as location, head direction, speed, and proximity to boundaries. These activity patterns are thought to provide a map-like representation of physical space. However, the hippocampal-entorhinal circuit is involved not only in spatial navigation, but also in a variety of memory-guided behaviours. The relationship between this general function and the specialized spatial activity patterns is unclear. A conceptual framework reconciling these views is that spatial representation is just one example of a more general mechanism for encoding continuous, task-relevant variables. Here we tested this idea by recording from hippocampal and entorhinal neurons during a task that required rats to use a joystick to manipulate sound along a continuous frequency axis. We found neural representation of the entire behavioural task, including activity that formed discrete firing fields at particular sound frequencies. Neurons involved in this representation overlapped with the known spatial cell types in the circuit, such as place cells and grid cells. These results suggest that common circuit mechanisms in the hippocampal-entorhinal system are used to represent diverse behavioural tasks, possibly supporting cognitive processes beyond spatial navigation.},
author = {Aronov, Dmitriy and Nevers, Rhino and Tank, David W.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/9a4052c5ee9d70a8b1f74feda7935a5b3c1d644f.pdf:pdf},
journal = {Nature},
number = {7647},
pages = {719--722},
pmid = {28358077},
title = {{Mapping of a non-spatial dimension by the hippocampal-entorhinal circuit}},
volume = {543},
year = {2017}
}
@article{Cully2015,
author = {Cully, Antoine and Clune, Jeff and Tarapore, Danesh and Mouret, Jean-Baptiste},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature 2015 Cully.pdf:pdf},
journal = {Nature},
month = {may},
number = {7553},
pages = {503--507},
title = {{Robots that can adapt like animals}},
volume = {521},
year = {2015}
}
@article{Poggio2012,
abstract = {This paper explores the theoretical consequences of a simple assumption: the computational goal of the feedforward path in the ventral stream --from V1, V2, V4 and to IT-- is to discount image transformations, after learning them during development.
},
author = {Poggio, T and Mutch, J and Leibo, J and Rosasco, L and Tacchetti, A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2012 Poggio.pdf:pdf},
month = {jan},
title = {{The computational magic of the ventral stream: sketch of a theory (and why some deep architectures work).}},
year = {2012}
}
@article{Baraduc2019,
abstract = {Concept cells in the human hippocampus encode the meaning conveyed by stimuli over their perceptual aspects. Here we investigate whether analogous cells in the macaque can form conceptual schemas of spatial environments. Each day, monkeys were presented with a familiar and a novel virtual maze, sharing a common schema but differing by surface features (landmarks). In both environments, animals searched for a hidden reward goal only defined in relation to landmarks. With learning, many neurons developed a firing map integrating goal-centered and task-related information of the novel maze that matched that for the familiar maze. Thus, these hippocampal cells abstract the spatial concepts from the superficial details of the environment and encode space into a schema-like representation.},
address = {Institut des Sciences Cognitives Marc Jeannerod, CNRS UMR 5229, Universit{\'{e}} Claude Barnard Lyon 1, 67 Boulevard Pinel, 69675 Bron, France. pierre.baraduc@gipsa-lab.fr sylvia.wirth@isc.cnrs.fr.},
author = {Baraduc, P and Duhamel, J-R and Wirth, S},
file = {:Users/hahosoya/Documents/Mendeley Desktop/80163797-97CD-4544-AAE2-4BB5D35F5F06.pdf:pdf},
journal = {Science},
month = {feb},
number = {6427},
pages = {635--639},
title = {{Schema cells in the macaque hippocampus.}},
volume = {363},
year = {2019}
}
@article{Ramachandran1992,
abstract = {Science in Pictures m щ Blind Spots Investigating how the visual system compensates for gaps in perception is helping researchers to elucidate how the brain processes images by Vilayanur S. Ramachandran As he dissected a human eye, the 17th-century French scientist Edme ...},
author = {Ramachandran, V S},
file = {:Users/hahosoya/Documents/Mendeley Desktop/1992 Ramachandran.pdf:pdf},
journal = {Sci. Am.},
month = {jan},
title = {{Blind spots}},
year = {1992}
}
@article{Hinton2007,
abstract = {To achieve its impressive performance in tasks such as speech perception or object 
recognition, the brain extracts multiple levels of representation from the sensory input. Backpropagation
was the first computationally efficient model of how neural networks could learn multiple ...},
author = {Hinton, Geoffrey E},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Trends in cognitive Sciences 2007 Hinton.pdf:pdf},
journal = {Trends Cogn. Sci.},
month = {jan},
title = {{Learning multiple layers of representation}},
year = {2007}
}
@article{Hinton2002,
abstract = {It is possible to combine multiple latent-variable models of the same data by multiplying their 
probability distributions together and then renor- malizing. This way of combining individual
“expert” models makes it hard to generate samples from the combined model but easy to  ...},
author = {Hinton, Geoffrey E},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neural Computation 2002 Hinton.pdf:pdf},
journal = {Neural Comput.},
month = {jan},
title = {{Training products of experts by minimizing contrastive divergence}},
year = {2002}
}
@article{OToole2018a,
abstract = {Trends in Cognitive Sciences, 22 (2018) 794-809. doi:10.1016/j.tics.2018.06.006},
author = {O'Toole, Alice J and Castillo, Carlos D and Parde, Connor J and Hill, Matthew Q and Chellappa, Rama},
file = {:Users/hahosoya/Documents/Mendeley Desktop/42E3218C-CC28-4A4F-A666-50B5D8A9A801.pdf:pdf},
journal = {Trends Cogn. Sci.},
month = {sep},
number = {9},
pages = {794--809},
title = {{Face Space Representations in Deep Convolutional Neural Networks}},
volume = {22},
year = {2018}
}
@article{Mundy2013,
author = {Mundy, M E and Downing, P E and Dwyer, D M and Honey, R C and Graham, K S},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2013 Mundy.pdf:pdf},
journal = {J. Neurosci.},
month = {jun},
number = {25},
pages = {10490--10502},
title = {{A Critical Role for the Hippocampus and Perirhinal Cortex in Perceptual Learning of Scenes and Faces: Complementary Findings from Amnesia and fMRI}},
volume = {33},
year = {2013}
}
@article{Drewes2016,
author = {Drewes, J and Goren, G and Zhu, W and Elder, J H},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2016 Drewes.pdf:pdf},
journal = {J. Neurosci.},
month = {jan},
number = {1},
pages = {185--192},
title = {{Recurrent Processing in the Formation of Shape Percepts}},
volume = {36},
year = {2016}
}
@article{Spratling2011,
abstract = {... of V1 cells. The model can be related to the biased-competition and predictive coding theories of cortical function ( Spratling , 2008a,b), and in turn to more general theories of hierarchical perceptual inference. It thus provides ...},
author = {Spratling, M W},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Vision Research 2011 Spratling-1.pdf:pdf},
journal = {Vision Res.},
month = {mar},
number = {6},
pages = {563--576},
title = {{A single functional model accounts for the distinct properties of suppression in cortical area V1}},
volume = {51},
year = {2011}
}
@article{Zhao2018,
address = {New York, New York, USA},
author = {Zhao, Bo and 0001, Xiao Wu and Cheng, Zhi-Qi and 0003, Hao Liu and Jie, Zequn and Feng, Jiashi},
file = {:Users/hahosoya/Documents/Mendeley Desktop/73224429-DE6A-491E-9085-1FBCF48DD5B1.pdf:pdf},
journal = {ACM Multimed.},
month = {jan},
pages = {383--391},
title = {{Multi-View Image Generation from a Single-View.}},
year = {2018}
}
@article{Jaiswal2020a,
abstract = {Self-supervised learning has gained popularity because of its ability to avoid the cost of annotating large-scale datasets. It is capable of adopting self-defined pseudolabels as supervision and use the learned representations for several downstream tasks. Specifically, contrastive learning has recently become a dominant component in self-supervised learning for computer vision, natural language processing (NLP), and other domains. It aims at embedding augmented versions of the same sample close to each other while trying to push away embeddings from different samples. This paper provides an extensive review of self-supervised methods that follow the contrastive approach. The work explains commonly used pretext tasks in a contrastive learning setup, followed by different architectures that have been proposed so far. Next, we present a performance comparison of different methods for multiple downstream tasks such as image classification, object detection, and action recognition. Finally, we conclude with the limitations of the current methods and the need for further techniques and future directions to make meaningful progress.},
archivePrefix = {arXiv},
arxivId = {2011.00362},
author = {Jaiswal, Ashish and Babu, Ashwin Ramesh and Zadeh, Mohammad Zaki and Banerjee, Debapriya and Makedon, Fillia},
eprint = {2011.00362},
file = {:Users/hahosoya/Documents/Mendeley Desktop/technologies-09-00002-v2.pdf:pdf},
journal = {Technologies},
keywords = {classification,contrastive learning,discriminative learning,image,object detection,self-supervised learning,transfer learning,unsupervised learning,video},
number = {1},
pages = {2},
title = {{A Survey on Contrastive Self-Supervised Learning}},
volume = {9},
year = {2020}
}
@article{Lee2000,
abstract = {Component Analysis (ICA) has been applied to signal processing problems including speech enhancement, telecommunications, and medical signal processing. ICA finds a linear nonorthogonal coordinate system in multivariate data determined by second-and ...
},
author = {Lee, T W and Lewicki, M S},
file = {:Users/hahosoya/Documents/Mendeley Desktop/IEEE Transactions on pattern Analysis and Machine Intelligence 2000 Lee.pdf:pdf},
journal = {IEEE Trans. Pattern Anal. Mach. Intell.},
month = {jan},
number = {10},
pages = {1078--1090},
title = {{ICA mixture models for unsupervised classification of non-Gaussian classes and automatic context switching in blind signal separation}},
volume = {22},
year = {2000}
}
@article{Horton2005,
author = {Horton, J C and Adams, D L},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Philosophical Transactions of the Royal Society B Biological Sciences 2005 Horton.pdf:pdf},
journal = {Philos. Trans. R. Soc. B Biol. Sci.},
month = {jan},
number = {1456},
pages = {837--862},
title = {{The cortical column: a structure without a function}},
volume = {360},
year = {2005}
}
@article{Eichenbaum2015,
abstract = {Neuron, 87 (2015) 9-11. doi:10.1016/j.neuron.2015.06.013},
author = {Eichenbaum, Howard},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neuron 2015 Eichenbaum.pdf:pdf},
journal = {Neuron},
month = {jul},
number = {1},
pages = {9--11},
title = {{The Hippocampus as a Cognitive Map &hellip; of Social Space}},
volume = {87},
year = {2015}
}
@article{Liu2017a,
author = {Liu, Ming-Yu and Breuel, Thomas and Kautz, Jan},
file = {:Users/hahosoya/Documents/Mendeley Desktop/CA7756A7-91C7-45A9-BB8A-DA29FD49ECD5.pdf:pdf},
journal = {Adv. Neural Inf. Process. Syst.},
month = {jan},
title = {{Unsupervised Image-to-Image Translation Networks.}},
year = {2017}
}
@article{Miyawaki2009,
abstract = {Image representation based on image bases provides a framework for understand- ing neural 
representation of visual perception. A recent fMRI study has shown that arbitrary contrast-defined
visual images can be reconstructed from fMRI ac- tivity patterns using a combination of  ...},
author = {Miyawaki, Y and Kamitani, Y},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Advances in neural information {\ldots} 2009 Miyawaki.pdf:pdf},
journal = {Adv. Neural Inf. {\ldots}},
month = {jan},
title = {{Estimating image bases for visual image reconstruction from human brain activity}},
year = {2009}
}
@article{Deneve2004,
abstract = {Our perception of the word is the result of combining information between several senses, such as vision, audition and proprioception. These sensory modalities use widely different frames of reference to represent the properties and locations of object. Moreover, multisensory ...},
author = {Deneve, S},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Physiology-Paris 2004 Deneve-1.pdf:pdf},
journal = {J. Physiol.},
month = {jan},
title = {{Bayesian multisensory integration and cross-modal spatial links}},
year = {2004}
}
@article{Echeveste2020,
abstract = {Sensory cortices display a suite of ubiquitous dynamical features, such as ongoing noise variability, transient overshoots and oscillations, that have so far escaped a common, principled theoretical account. We developed a unifying model for these phenomena by training a recurrent excitatory–inhibitory neural circuit model of a visual cortical hypercolumn to perform sampling-based probabilistic inference. The optimized network displayed several key biological properties, including divisive normalization and stimulus-modulated noise variability, inhibition-dominated transients at stimulus onset and strong gamma oscillations. These dynamical features had distinct functional roles in speeding up inferences and made predictions that we confirmed in novel analyses of recordings from awake monkeys. Our results suggest that the basic motifs of cortical dynamics emerge as a consequence of the efficient implementation of the same computational function—fast sampling-based inference—and predict further properties of these motifs that can be tested in future experiments.},
author = {Echeveste, Rodrigo and Aitchison, Laurence and Hennequin, Guillaume and Lengyel, M{\'{a}}t{\'{e}}},
file = {:Users/hahosoya/Documents/Mendeley Desktop/s41593-020-0671-1.pdf:pdf},
journal = {Nat. Neurosci.},
number = {9},
pages = {1138--1149},
pmid = {32778794},
title = {{Cortical-like dynamics in recurrent circuits optimized for sampling-based probabilistic inference}},
volume = {23},
year = {2020}
}
@article{Gibbs1997,
abstract = {Bayesian inference o ers us a powerful tool with which to tackle the problem of data 
modelling. However, the performance of Bayesian methods is crucially dependent on being able
to nd good models for our data. The principal focus of this thesis is the development of  ...},
author = {Gibbs, M N},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Unpublished doctoral dissertation 1997 Gibbs.pdf:pdf},
journal = {Unpubl. Dr. Diss.},
month = {jan},
title = {{Bayesian Gaussian processes for regression and classification}},
year = {1997}
}
@article{Neal1992,
abstract = {Connectionist learning procedures are presented for "sigmoid" and "noisy-OR" varieties of probabilistic belief networks. These networks have previously been seen primarily as a means of representing knowledge derived from experts. Here it is shown that the "Gibbs sampling" simulation procedure for such networks can support maximum-likelihood learning from empirical data through local gradient ascent. This learning procedure resembles that used for "Boltzmann machines", and like it, allows the use of "hidden" variables to model correlations between visible variables. Due to the directed nature of the connections in a belief network, however, the "negative phase" of Boltzmann machine learning is unnecessary. Experimental results show that, as a result, learning in a sigmoid belief network can be faster than in a Boltzmann machine. These networks have other advantages over Boltzmann machines in pattern classification and decision making applications, are naturally applicable to unsupervised learning problems, and provide a link between work on connectionist learning and work on the representation of expert knowledge. {\textcopyright} 1992.},
author = {Neal, Radford M.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/a120c05ad7cd4ce2eb8fb9697e16c7c4877208a5.pdf:pdf},
journal = {Artif. Intell.},
number = {1},
pages = {71--113},
title = {{Connectionist learning of belief networks}},
volume = {56},
year = {1992}
}
@article{Sugiura2015,
author = {Sugiura, Motoaki and Miyauchi, Carlos Makoto and Kotozaki, Yuka and Akimoto, Yoritaka and Nozawa, Takayuki and Yomogida, Yukihito and Hanawa, Sugiko and Yamamoto, Yuki and Sakuma, Atsushi and Nakagawa, Seishu and Kawashima, Ryuta},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Cerebral Cortex 2015 Sugiura.pdf:pdf},
journal = {Cereb. Cortex},
month = {aug},
number = {9},
pages = {2806--2814},
title = {{Neural Mechanism for Mirrored Self-face Recognition}},
volume = {25},
year = {2015}
}
@article{Zoccolan2007,
abstract = {Object recognition requires both selectivity among different objects and tolerance to vastly different retinal images of the same object, resulting from natural variation in (e.g.) position, size, illumination, and clutter. Thus, discovering neuronal responses that have object selectivity and tolerance to identity-preserving transformations is fundamental to understanding object recognition. Although selectivity and tolerance are found at the highest level of the primate ventral visual stream [the inferotemporal cortex (IT)], both properties are highly varied and poorly understood. If an IT neuron has very sharp selectivity for a unique combination of object features ("diagnostic features"), this might automatically endow it with high tolerance. However, this relationship cannot be taken as given; although some IT neurons are highly object selective and some are highly tolerant, the empirical connection of these key properties is unknown. In this study, we systematically measured both object selectivity and tolerance to different identity-preserving image transformations in the spiking responses of a population of monkey IT neurons. We found that IT neurons with high object selectivity typically have low tolerance (and vice versa), regardless of how object selectivity was quantified and the type of tolerance examined. The discovery of this trade-off illuminates object selectivity and tolerance in IT and unifies a range of previous, seemingly disparate results. This finding also argues against the idea that diagnostic conjunctions of features guarantee tolerance. Instead, it is naturally explained by object recognition models in which object selectivity is built through AND-like tuning mechanisms.},
address = {McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Center for Biological and Computational Learning, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142, USA.},
author = {Zoccolan, Davide and Kouh, Minjoon and Poggio, Tomaso and DiCarlo, James J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2007 Zoccolan.pdf:pdf},
journal = {J. Neurosci.},
month = {nov},
number = {45},
pages = {12292--12307},
title = {{Trade-off between object selectivity and tolerance in monkey inferotemporal cortex.}},
volume = {27},
year = {2007}
}
@article{Hanazawa2001,
abstract = {Abstract The texture of an object provides important cues for its recognition; however, little is known about the neural representation of texture. To investigate the representation of texture in the visual cortex, we recorded single-cell activities in area V4 of macaque monkeys. To  ...},
author = {Hanazawa, A and Komatsu, H},
file = {:Users/hahosoya/Documents/Mendeley Desktop/The Journal of neuroscience 2001 Hanazawa.pdf:pdf},
journal = {J. Neurosci.},
month = {jan},
title = {{Influence of the direction of elemental luminance gradients on the responses of V4 cells to textured surfaces}},
year = {2001}
}
@article{Suzuki2007,
abstract = {Whether the piano touch can affect the piano tone quality or not has been a subject of discussion for more than 70 years. Many studies have been conducted on this subject, but most of the conclusions so far were on the negative side. This is possibly because the ... 
},
author = {Suzuki, Hideo},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2007 Suzuki.pdf:pdf},
journal = {Acoust. Sci. Technol.},
month = {jan},
number = {1},
pages = {1--6},
title = {{Spectrum analysis and tone quality evaluation of piano sounds with hard and soft touches}},
volume = {28},
year = {2007}
}
@article{Caywood2004,
abstract = {It has been hypothesized that mammalian sensory systems are efficient because they reduce the redundancy of natural sensory input. If correct, this theory could unify our understanding of sensory coding; here, we test its predictions for color coding in the primate primary visual cortex (V1). We apply independent component analysis (ICA) to simulated cone responses to natural scenes, obtaining a set of colored independent component (IC) filters that form a redundancy-reducing visual code. We compare IC filters with physiologically measured V1 neurons, and find great spatial similarity between IC filters and V1 simple cells. On cursory inspection, there is little chromatic similarity; however, we find that many apparent differences result from biases in the physiological measurements and ICA analysis. After correcting these biases, we find that the chromatic tuning of IC filters does indeed resemble the population of V1 neurons, supporting the redundancy-reduction hypothesis.},
address = {Program in Neuroscience, University of California-San Francisco, 513 Parnassus Avenue, Room S-762, San Francisco, CA 94143-0444, USA. caywood@phy.ucsf.edu},
author = {Caywood, Matthew S and Willmore, Benjamin and Tolhurst, David J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neurophysiology 2004 Caywood.pdf:pdf},
journal = {J. Neurophysiol.},
month = {jun},
number = {6},
pages = {2859--2873},
title = {{Independent components of color natural scenes resemble V1 neurons in their spatial and color tuning.}},
volume = {91},
year = {2004}
}
@article{LeCun2012,
author = {LeCun, Yann A and Bottou, L{\'{e}}on and Orr, Genevieve B and M{\"{u}}ller, Klaus-Robert},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2012 LeCun.pdf:pdf},
journal = {Neural networks: Tricks of the trade},
month = {jan},
pages = {9--48},
title = {{Efficient backprop}},
year = {2012}
}
@article{Jiang2017,
abstract = {Clustering is among the most fundamental tasks in machine learning and artificial intelligence. In this paper, we propose Variational Deep Embedding (VaDE), a novel unsupervised generative clustering approach within the framework of Variational Auto-Encoder (VAE). Specifically, VaDE models the data generative procedure with a Gaussian Mixture Model (GMM) and a deep neural network (DNN): 1) the GMM picks a cluster; 2) from which a latent embedding is generated; 3) then the DNN decodes the latent embedding into an observable. Inference in VaDE is done in a variational way: a different DNN is used to encode observables to latent embeddings, so that the evidence lower bound (ELBO) can be optimized using the Stochastic Gradient Variational Bayes (SGVB) estimator and the reparameterization trick. Quantitative comparisons with strong baselines are included in this paper, and experimental results show that VaDE significantly outperforms the state-of-the-art clustering methods on 5 benchmarks from various modalities. Moreover, by VaDE's generative nature, we show its capability of generating highly realistic samples for any specified cluster, without using supervised information during training.},
archivePrefix = {arXiv},
arxivId = {arXiv:1611.05148v3},
author = {Jiang, Zhuxi and Zheng, Yin and Tan, Huachun and Tang, Bangsheng and Zhou, Hanning},
eprint = {arXiv:1611.05148v3},
file = {:Users/hahosoya/Documents/Mendeley Desktop/f708482e99ed40a2fb9e363577479e1fd15afc9f.pdf:pdf},
isbn = {9780999241103},
journal = {Int. Jt. Conf. Artif. Intell.},
pages = {1965--1972},
title = {{Variational deep embedding: An unsupervised generative approach to clustering}},
year = {2017}
}
@article{Bushnell2011,
abstract = {Past studies of shape coding in visual cortical area V4 have demonstrated that neurons can accurately represent isolated shapes in terms of their component contour features. However, rich natural scenes contain many partially occluded objects, which have "accidental" contours at the junction between the occluded and occluding objects. These contours do not represent the true shape of the occluded object and are known to be perceptually discounted. To discover whether V4 neurons differentially encode accidental contours, we studied the responses of single neurons in fixating monkeys to complex shapes and contextual stimuli presented either in isolation or adjoining each other to provide a percept of partial occlusion. Responses to preferred contours were suppressed when the adjoining context rendered those contours accidental. The observed suppression was reversed when the partial occlusion percept was compromised by introducing a small gap between the component stimuli. Control experiments demonstrated that these results likely depend on contour geometry at T-junctions and cannot be attributed to mechanisms based solely on local color/luminance contrast, spatial proximity of stimuli, or the spatial frequency content of images. Our findings provide novel insights into how occluded objects, which are fundamental to complex visual scenes, are encoded in area V4. They also raise the possibility that the weakened encoding of accidental contours at the junction between objects could mark the first step of image segmentation along the ventral visual pathway.},
address = {Department of Biological Structure and National Primate Research Center, University of Washington, Seattle, Washington 98195, USA.},
author = {Bushnell, Brittany N and Harding, Philip J and Kosai, Yoshito and Pasupathy, Anitha},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2011 Bushnell.pdf:pdf},
journal = {J. Neurosci.},
month = {mar},
number = {11},
pages = {4012--4024},
title = {{Partial occlusion modulates contour-based shape encoding in primate area V4.}},
volume = {31},
year = {2011}
}
@article{Moore2021,
abstract = {Three major pillars of hippocampal function are spatial navigation1, Hebbian synaptic plasticity2 and spatial selectivity3. The hippocampus is also implicated in episodic memory4, but the precise link between these four functions is missing. Here we report the multiplexed selectivity of dorsal CA1 neurons while rats performed a virtual navigation task using only distal visual cues5, similar to the standard water maze test of spatial memory1. Neural responses primarily encoded path distance from the start point and the head angle of rats, with a weak allocentric spatial component similar to that in primates but substantially weaker than in rodents in the real world. Often, the same cells multiplexed and encoded path distance, angle and allocentric position in a sequence, thus encoding a journey-specific episode. The strength of neural activity and tuning strongly correlated with performance, with a temporal relationship indicating neural responses influencing behaviour and vice versa. Consistent with computational models of associative and causal Hebbian learning6,7, neural responses showed increasing clustering8 and became better predictors of behaviourally relevant variables, with the average neurometric curves exceeding and converging to psychometric curves. Thus, hippocampal neurons multiplex and exhibit highly plastic, task- and experience-dependent tuning to path-centric and allocentric variables to form episodic sequences supporting navigation.},
author = {Moore, Jason J. and Cushman, Jesse D. and Acharya, Lavanya and Popeney, Briana and Mehta, Mayank R.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/s41586-021-03989-z.pdf:pdf},
journal = {Nature},
number = {7885},
pages = {442--448},
pmid = {34671157},
title = {{Linking hippocampal multiplexed tuning, Hebbian plasticity and navigation}},
volume = {599},
year = {2021}
}
@article{Fiser2004,
abstract = {During vision, it is believed that neural activity in the primary visual cortex is predominantly driven by sensory input from the environment. However, visual cortical neurons respond to repeated presentations of the same stimulus with a high degree of variability. Although this variability has been considered to be noise owing to random spontaneous activity within the cortex, recent studies show that spontaneous activity has a highly coherent spatio-temporal structure. This raises the possibility that the pattern of this spontaneous activity may shape neural responses during natural viewing conditions to a larger extent than previously thought. Here, we examine the relationship between spontaneous activity and the response of primary visual cortical neurons to dynamic natural-scene and random-noise film images in awake, freely viewing ferrets from the time of eye opening to maturity. The correspondence between evoked neural activity and the structure of the input signal was weak in young animals, but systematically improved with age. This improvement was linked to a shift in the dynamics of spontaneous activity. At all ages including the mature animal, correlations in spontaneous neural firing were only slightly modified by visual stimulation, irrespective of the sensory input. These results suggest that in both the developing and mature visual cortex, sensory evoked neural activity represents the modulation and triggering of ongoing circuit dynamics by input signals, rather than directly reflecting the structure of the input signal itself.},
address = {Center for Visual Science, University of Rochester, Rochester, New York 14627, USA.},
author = {Fiser, J{\'{o}}zsef and Chiu, Chiayu and Weliky, Michael},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature 2004 Fiser.pdf:pdf},
journal = {Nature},
month = {sep},
number = {7008},
pages = {573--578},
title = {{Small modulation of ongoing cortical dynamics by sensory input during natural vision.}},
volume = {431},
year = {2004}
}
@article{Foldiak1991,
author = {F{\"{o}}ldi{\'{a}}k, P},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neural Computation 1991 F{\"{o}}ldi{\'{a}}k.pdf:pdf},
journal = {Neural Comput.},
month = {jan},
number = {2},
pages = {194--200},
title = {{Learning invariance from transformation sequences}},
volume = {3},
year = {1991}
}
@article{Thiele2018,
abstract = {Neuron, 97 (2018) 769-785. doi:10.1016/j.neuron.2018.01.008},
author = {Thiele, Alexander and Bellgrove, Mark A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/A54D236F-60F8-47BE-81B4-FC2964E08870.pdf:pdf},
journal = {Neuron},
month = {feb},
number = {4},
pages = {769--785},
title = {{Neuromodulation of Attention}},
volume = {97},
year = {2018}
}
@article{Kaardal2013,
abstract = {Current dimensionality-reduction methods can identify relevant subspaces for neural computations but do not favor one basis over the other within the relevant subspace. Finding the appropriate basis can simplify the description of the nonlinear computation with respect to the relevant variables, making it easier to elucidate the underlying neural computation and make hypotheses about the neural circuitry, giving rise to the observed responses. Part of the problem is that although some of the dimensionality reduction methods can identify many of the relevant dimensions, it is usually difficult to map out or interpret the nonlinear transformation with respect to more than a few relevant dimensions simultaneously without some simplifying assumptions. While recent approaches make it possible to create predictive models based on many relevant dimensions simultaneously, there still remains the need to relate such predictive models to the mechanistic descriptions of the operation of underlying neural circuitry. Here we demonstrate that transforming to a basis within the relevant subspace where the neural computation is best described by a given nonlinear function often makes it easier to interpret the computation and describe it with a small number of parameters. We refer to the corresponding basis as the functional basis, and illustrate the utility of such transformation in the context of logical OR and logical AND functions. We show that although dimensionality-reduction methods such as spike-triggered covariance are able to find a relevant subspace, they often produce dimensions that are difficult to interpret and do not correspond to a functional basis. The functional features can be found using a maximum likelihood approach. The results are illustrated using simulated neurons and recordings from retinal ganglion cells. The resulting features are uniquely defined and nonorthogonal, and they make it easier to relate computational and mechanistic models to each other.},
address = {Computational Neurobiology Laboratory and Crick-Jacobs Center for Theoretical and Computational Biology, Salk Center for Biological Studies, La Jolla, CA 92037; and Center for Theoretical Biological Physics, University of California, San Diego, La Jolla, },
author = {Kaardal, Joel and Fitzgerald, Jeffrey D and Berry, Michael J and Sharpee, Tatyana O},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neural Computation 2013 Kaardal.pdf:pdf},
journal = {Neural Comput.},
month = {jul},
number = {7},
pages = {1870--1890},
title = {{Identifying functional bases for multidimensional neural computations.}},
volume = {25},
year = {2013}
}
@article{Nishimoto2011,
abstract = {Quantitative modeling of human brain activity can provide crucial insights about cortical representations [1, 2] and can form the basis for brain decoding devices [3-5]. Recent functional magnetic resonance imaging (fMRI) studies have modeled brain activity elicited by static visual patterns and have reconstructed these patterns from brain activity [6-8]. However, blood oxygen level-dependent (BOLD) signals measured via fMRI are very slow [9], so it has been difficult to model brain activity elicited by dynamic stimuli such as natural movies. Here we present a new motion-energy [10, 11] encoding model that largely overcomes this limitation. The model describes fast visual information and slow hemodynamics by separate components. We recorded BOLD signals in occipitotemporal visual cortex of human subjects who watched natural movies and fit the model separately to individual voxels. Visualization of the fit models reveals how early visual areas represent the information in movies. To demonstrate the power of our approach, we also constructed a Bayesian decoder [8] by combining estimated encoding models with a sampled natural movie prior. The decoder provides remarkable reconstructions of the viewed movies. These results demonstrate that dynamic brain activity measured under naturalistic conditions can be decoded using current fMRI technology.},
address = {Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA 94720, USA.},
author = {Nishimoto, Shinji and Vu, An T and Naselaris, Thomas and Benjamini, Yuval and Yu, Bin and Gallant, Jack L},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Curr. Biol. 2011 Nishimoto.pdf:pdf},
journal = {Curr. Biol.},
month = {oct},
number = {19},
pages = {1641--1646},
title = {{Reconstructing visual experiences from brain activity evoked by natural movies.}},
volume = {21},
year = {2011}
}
@article{Askenfelt1990,
author = {Askenfelt, Anders and Jansson, Erik V},
file = {:Users/hahosoya/Documents/Mendeley Desktop/J. Acoust. Soc. Am. 1990 Askenfelt.pdf:pdf},
journal = {J. Acoust. Soc. Am.},
month = {jan},
number = {1},
pages = {52--63},
title = {{From touch to string vibrations. I: Timing in the grand piano action}},
volume = {88},
year = {1990}
}
@article{Gondal2019,
abstract = {Learning meaningful and compact representations with disentangled semantic aspects is considered to be of key importance in representation learning. Since real-world data is notoriously costly to collect, many recent state-of-the-art disentanglement models have heavily relied on synthetic toy data-sets. In this paper, we propose a novel data-set which consists of over one million images of physical 3D objects with seven factors of variation, such as object color, shape, size and position. In order to be able to control all the factors of variation precisely, we built an experimental platform where the objects are being moved by a robotic arm. In addition, we provide two more datasets which consist of simulations of the experimental setup. These datasets provide for the first time the possibility to systematically investigate how well different disentanglement methods perform on real data in comparison to simulation, and how simulated data can be leveraged to build better representations of the real world. We provide a first experimental study of these questions and our results indicate that learned models transfer poorly, but that model and hyperparameter selection is an effective means of transferring information to the real world.},
author = {Gondal, Muhammad Waleed and W{\"{u}}thrich, Manuel and Miladinovic, Ðorde and Locatello, Francesco and Breidt, Martin and Volchkov, Valentin and Akpo, Joel and Bachem, Olivier and Sch{\"{o}}lkopf, Bernhard and Bauer, Stefan},
file = {:Users/hahosoya/Documents/Mendeley Desktop/a5bd9a1fbb8ae3192abd0e1ca8b00a5be7da142c.pdf:pdf},
journal = {Adv. Neural Inf. Process. Syst.},
number = {NeurIPS},
pages = {1--12},
title = {{On the transfer of inductive bias from simulation to the real world: A new disentanglement dataset}},
volume = {32},
year = {2019}
}
@article{Mehrer2021,
abstract = {Deep neural networks provide the current best models of visual information processing in the primate brain. Drawing on work from computer vision, the most commonly used networks are pretrained on data from the ImageNet Large Scale Visual Recognition Challenge. This dataset comprises images from 1,000 categories, selected to provide a challenging testbed for automated visual object recognition systems. Moving beyond this common practice, we here introduce ecoset, a collection of >1.5 million images from 565 basic-level categories selected to better capture the distribution of objects relevant to humans. Ecoset categories were chosen to be both frequent in linguistic usage and concrete, thereby mirroring important physical objects in the world. We test the effects of training on this ecologically more valid dataset using multiple instances of two neural network architectures: AlexNet and vNet, a novel architecture designed to mimic the progressive increase in receptive field sizes along the human ventral stream. We show that training on ecoset leads to significant improvements in predicting representations in human higher-level visual cortex and perceptual judgments, surpassing the previous state of the art. Significant and highly consistent benefits are demonstrated for both architectures on two separate functional magnetic resonance imaging (fMRI) datasets and behavioral data, jointly covering responses to 1,292 visual stimuli from a wide variety of object categories. These results suggest that computational visual neuroscience may take better advantage of the deep learning framework by using image sets that reflect the human perceptual and cognitive experience. Ecoset and trained network models are openly available to the research community.},
author = {Mehrer, Johannes and Spoerer, Courtney J. and Jones, Emer C. and Kriegeskorte, Nikolaus and Kietzmann, Tim C.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/a9e996bb0fce812a98991df5ebc24a436c6ea329.pdf:pdf},
journal = {Proc. Natl. Acad. Sci. U. S. A.},
keywords = {Computational neuroscience,Computer vision,Deep neural networks,Ecological relevance,Human visual system},
number = {8},
pages = {1--9},
pmid = {33593900},
title = {{An ecologically motivated image dataset for deep learning yields better models of human vision}},
volume = {118},
year = {2021}
}
@inproceedings{Kingma2015,
abstract = {Abstract: We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also ...
},
author = {Kingma, D and Ba, J},
booktitle = {Int. Conf. Learn. Represent.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/arXiv 2014 Kingma.pdf:pdf},
month = {jan},
title = {{Adam: A method for stochastic optimization}},
year = {2015}
}
@article{Groen2018,
abstract = {{\ldots} selective cortex represents only a subset of behaviorally relevant scene information. DOI: https://doi.org/ 10.7554 / eLife . 32962.001 Introduction Although researchers of visual perception often use simplified, highly controlled images {\ldots}
},
author = {Groen, I I A and Greene, M R and Baldassano, C and Fei-Fei, L},
file = {:Users/hahosoya/Documents/Mendeley Desktop/8D207E2E-0EF6-4B30-B3B2-F21CBD3D3C28.pdf:pdf},
journal = {Elife},
month = {jan},
pages = {e32962},
title = {{Distinct contributions of functional and deep neural network features to representational similarity of scenes in human brain and behavior}},
volume = {7},
year = {2018}
}
@article{Doi2014,
author = {Doi, Eizaburo and Lewicki, Michael S},
editor = {Bethge, Matthias},
file = {:Users/hahosoya/Documents/Mendeley Desktop/PLoS Comp Biol 2014 Doi.pdf:pdf},
journal = {PLoS Comput. Biol.},
month = {aug},
number = {8},
pages = {e1003761},
title = {{A Simple Model of Optimal Population Coding for Sensory Systems}},
volume = {10},
year = {2014}
}
@article{McDermott2011,
abstract = {Rainstorms, insect swarms, and galloping horses produce "sound textures"--the collective result of many similar acoustic events. Sound textures are distinguished by temporal homogeneity, suggesting they could be recognized with time-averaged statistics. To test this hypothesis, we processed real-world textures with an auditory model containing filters tuned for sound frequencies and their modulations, and measured statistics of the resulting decomposition. We then assessed the realism and recognizability of novel sounds synthesized to have matching statistics. Statistics of individual frequency channels, capturing spectral power and sparsity, generally failed to produce compelling synthetic textures; however, combining them with correlations between channels produced identifiable and natural-sounding textures. Synthesis quality declined if statistics were computed from biologically implausible auditory models. The results suggest that sound texture perception is mediated by relatively simple statistics of early auditory representations, presumably computed by downstream neural populations. The synthesis methodology offers a powerful tool for their further investigation.},
address = {Howard Hughes Medical Institute, New York University, New York, NY 10003, USA. jhm@cns.nyu.edu},
author = {McDermott, Josh H and Simoncelli, Eero P},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neuron 2011 McDermott.pdf:pdf},
journal = {Neuron},
month = {sep},
number = {5},
pages = {926--940},
title = {{Sound texture perception via statistics of the auditory periphery: evidence from sound synthesis.}},
volume = {71},
year = {2011}
}
@article{Liao2016,
abstract = {We discuss relations between Residual Networks (ResNet), Recurrent Neural Networks (RNNs) and the primate visual cortex. We begin with the observation that a shallow RNN is exactly equivalent to a very deep ResNet with weight sharing among the layers. A direct implementation of such a RNN, although having orders of magnitude fewer parameters, leads to a performance similar to the corresponding ResNet. We propose 1) a generalization of both RNN and ResNet architectures and 2) the conjecture that a class of moderately deep RNNs is a biologically-plausible model of the ventral stream in visual cortex. We demonstrate the effectiveness of the architectures by testing them on the CIFAR-10 dataset.},
archivePrefix = {arXiv},
arxivId = {1604.03640},
author = {Liao, Qianli and Poggio, Tomaso},
eprint = {1604.03640},
file = {:Users/hahosoya/Documents/Mendeley Desktop/e22789f9e39a14cd7dcf6da4160f81ef668ac2d3.pdf:pdf},
pages = {1--14},
title = {{Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex}},
year = {2016}
}
@article{Grimaldi2016,
abstract = {The neural circuits underlying face recognition provide a model for understanding visual object representation, social cognition, and hierarchical information processing. A fundamental piece of information lacking to date is the detailed anatomical connections of the face patches. Here, we injected retrograde tracers into four different face patches (PL, ML, AL, AM) to characterize their anatomical connectivity. We found that the patches are strongly and specifically connected to each other, and individual patches receive inputs from extrastriate cortex, the medial temporal lobe, and three subcortical structures (the pulvinar, claustrum, and amygdala). Inputs from prefrontal cortex were surprisingly weak. Patches were densely interconnected to one another in both feedforward and feedback directions, inconsistent with a serial hierarchy. These results provide the first direct anatomical evidence that the face patches constitute a highly specialized system and suggest that subcortical regions may play a vital role in routing face-related information to subsequent processing stages.},
address = {Howard Hughes Medical Institute, Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA. Electronic address: pcgrimaldi@gmail.com.},
author = {Grimaldi, Piercesare and Saleem, Kadharbatcha S and Tsao, Doris},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neuron 2016 Grimaldi.pdf:pdf},
journal = {Neuron},
month = {jun},
number = {6},
pages = {1325--1342},
title = {{Anatomical Connections of the Functionally Defined "Face Patches" in the Macaque Monkey.}},
volume = {90},
year = {2016}
}
@article{Leopold2006,
abstract = {The rich and immediate perception of a familiar face, including its identity, expression and even intent, is one of the most impressive shared faculties of human and non-human primate brains. Many visually responsive neurons in the inferotemporal cortex of macaque monkeys respond selectively to faces, sometimes to only one or a few individuals, while showing little sensitivity to scale and other details of the retinal image. Here we show that face-responsive neurons in the macaque monkey anterior inferotemporal cortex are tuned to a fundamental dimension of face perception. Using a norm-based caricaturization framework previously developed for human psychophysics, we varied the identity information present in photo-realistic human faces, and found that neurons of the anterior inferotemporal cortex were most often tuned around the average, identity-ambiguous face. These observations are consistent with face-selective responses in this area being shaped by a figural comparison, reflecting structural differences between an incoming face and an internal reference or norm. As such, these findings link the tuning of neurons in the inferotemporal cortex to psychological models of face identity perception.},
address = {Max Planck Institut f{\"{u}}r biologische Kybernetik, Spemannstrasse 38, 72076 T{\"{u}}bingen, Germany. leopoldd@mail.nih.gov},
author = {Leopold, David A and Bondar, Igor V and Giese, Martin A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature 2006 Leopold.pdf:pdf},
journal = {Nature},
month = {aug},
number = {7102},
pages = {572--575},
title = {{Norm-based face encoding by single neurons in the monkey inferotemporal cortex.}},
volume = {442},
year = {2006}
}
@article{Hosoya2017,
author = {Hosoya, Haruo and Hyv{\"{a}}rinen, Aapo},
file = {:Users/hahosoya/Documents/Mendeley Desktop/PLoS Comp Biol 2017 Hosoya.pdf:pdf},
journal = {PLoS Comput. Biol.},
month = {jul},
number = {7},
pages = {e1005667},
title = {{A mixture of sparse coding models explaining properties of face neurons related to holistic and parts-based processing}},
volume = {13},
year = {2017}
}
@article{Tsao2006,
abstract = {Abstract Face perception is a skill crucial to primates. In both humans and macaque monkeys, functional magnetic resonance imaging (fMRI) reveals a system of cortical regions that show increased blood flow when the subject views images of faces, compared with  ...},
author = {Tsao, D Y and Freiwald, W A and Tootell, R B H and Livingstone, M S},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Science 2006 Tsao.pdf:pdf},
journal = {Science},
month = {jan},
number = {5761},
pages = {670--674},
title = {{A cortical region consisting entirely of face-selective cells}},
volume = {311},
year = {2006}
}
@article{DeBeeck2000,
abstract = {... The authors provide the first detailed, quantitative data on the spatial sensitivity of neurons in the anterior part of the inferior  temporal cortex (area TE) in awake, fixating monkeys. ... Moreover, size selectivity of TE neurons was generally position invariant . ...},
author = {{De Beeck}, H Op},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2000 De Beeck.pdf:pdf},
journal = {J. Comp. {\ldots}},
month = {jan},
title = {{Spatial sensitivity of macaque inferior temporal neurons - Op De Beeck - 2000 - The Journal of Comparative Neurology - Wiley Online Library}},
year = {2000}
}
@article{VanHateren1998,
abstract = {References http://rspb.royalsocietypublishing.org/content/265/1412/2315#related-urls Article cited in: ... This journal is {\c{e}} 1998 The Royal Society ... 1Department of Neurobiophysics, University of Groningen, Nijenborgh 4, NL-9747 AG Groningen,The Netherlands 2Sloan Center ...},
author = {van Hateren, J H and Ruderman, D L},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proceedings of the Royal Society B Biological Sciences 1998 van Hateren-2.pdf:pdf},
journal = {Proc. R. Soc. B Biol. Sci.},
month = {dec},
number = {1412},
pages = {2315--2320},
title = {{Independent component analysis of natural image sequences yields spatio-temporal filters similar to simple cells in primary visual cortex}},
volume = {265},
year = {1998}
}
@article{Tan2016,
author = {Tan, Cheston and Poggio, Tomaso},
editor = {Hadjikhani, Nouchine},
file = {:Users/hahosoya/Documents/Mendeley Desktop/PLoS ONE 2016 Tan.PDF:PDF},
journal = {PLoS One},
month = {mar},
number = {3},
pages = {e0150980},
title = {{Neural Tuning Size in a Model of Primate Visual Processing Accounts for Three Key Markers of Holistic Face Processing}},
volume = {11},
year = {2016}
}
@article{Dosovitskiy2015,
abstract = {Abstract We train a generative convolutional neural network which is able to generate images of objects given object type, viewpoint, and color. We train the network in a supervised manner on a dataset of rendered 3D chair models. Our experiments show that the network does not merely learn all images by heart, but rather finds a meaningful representation of a 3D chair model allowing it to assess the similarity of different chairs, ...
},
author = {Dosovitskiy, A and Springenberg, J Tobias},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proceedings of the IEEE 2015 Dosovitskiy.pdf:pdf},
journal = {Comput. Vis. Pattern Recognit.},
month = {jan},
title = {{Learning to generate chairs with convolutional neural networks}},
year = {2015}
}
@article{Gutmann2012,
abstract = {Abstract We consider the task of estimating , from observed data, a probabilistic model that is parameterized by a finite number of parameters. In particular, we are considering the situation where the model probability density function is unnormalized . That is, the model  ... 
},
author = {Gutmann, M U and Hyv{\"{a}}rinen, A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2012 Gutmann.pdf:pdf},
journal = {J. Mach. Learn. Res.},
month = {jan},
title = {{Noise contrastive estimation of unnormalized statistical models with applications to natural image statistics - Google 検索}},
year = {2012}
}
@article{Meyers2015,
author = {Meyers, E M and Borzello, M and Freiwald, W A and Tsao, D},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2015 Meyers.pdf:pdf},
journal = {J. Neurosci.},
month = {may},
number = {18},
pages = {7069--7081},
title = {{Intelligent Information Loss: The Coding of Facial Identity, Head Pose, and Non-Face Information in the Macaque Face Patch System}},
volume = {35},
year = {2015}
}
@article{Sripati2010,
author = {Sripati, A P and Olson, C R},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2010 Sripati.pdf:pdf},
journal = {J. Neurosci.},
month = {jun},
number = {23},
pages = {7948--7960},
title = {{Responses to Compound Objects in Monkey Inferotemporal Cortex: The Whole Is Equal to the Sum of the Discrete Parts}},
volume = {30},
year = {2010}
}
@article{Constantinescu2016,
abstract = {It has been hypothesized that the brain organizes concepts into a mental map, allowing conceptual relationships to be navigated in a manner similar to that of space. Grid cells use a hexagonally symmetric code to organize spatial representations and are the likely source of a precise hexagonal symmetry in the functional magnetic resonance imaging signal. Humans navigating conceptual two-dimensional knowledge showed the same hexagonal signal in a set of brain regions markedly similar to those activated during spatial navigation. This gridlike signal is consistent across sessions acquired within an hour and more than a week apart. Our findings suggest that global relational codes may be used to organize nonspatial conceptual representations and that these codes may have a hexagonal gridlike pattern when conceptual knowledge is laid out in two continuous dimensions.},
author = {Constantinescu, Alexandra O and O'Reilly, Jill X. and Behrens, Timothy E.J.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/9405b02100cd1b08a8e790aeb264cfc06563baa9.pdf:pdf},
journal = {Science},
number = {6292},
pages = {1464--1468},
pmid = {27313047},
title = {{Organizing conceptual knowledge in humans with a gridlike code}},
volume = {352},
year = {2016}
}
@article{Silver2018,
abstract = {The game of chess is the longest-studied domain in the history of artificial intelligence. The strongest programs are based on a combination of sophisticated search techniques, domain-specific adaptations, and handcrafted evaluation functions that have been refined by human experts over several decades. By contrast, the AlphaGo Zero program recently achieved superhuman performance in the game of Go by reinforcement learning from self-play. In this paper, we generalize this approach into a single AlphaZero algorithm that can achieve superhuman performance in many challenging games. Starting from random play and given no domain knowledge except the game rules, AlphaZero convincingly defeated a world champion program in the games of chess and shogi (Japanese chess), as well as Go.},
address = {DeepMind, 6 Pancras Square, London N1C 4AG, UK. davidsilver@google.com dhcontact@google.com.},
author = {Silver, David and Hubert, Thomas and Schrittwieser, Julian and Antonoglou, Ioannis and Lai, Matthew and Guez, Arthur and Lanctot, Marc and Sifre, Laurent and Kumaran, Dharshan and Graepel, Thore and Lillicrap, Timothy and Simonyan, Karen and Hassabis, Demis},
file = {:Users/hahosoya/Documents/Mendeley Desktop/550EA0C9-E9F4-499C-A837-8852991DFB39.pdf:pdf},
journal = {Science},
month = {dec},
number = {6419},
pages = {1140--1144},
title = {{A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play.}},
volume = {362},
year = {2018}
}
@article{Yuille2006,
abstract = {We argue that the study of human vision should be aimed at determining how humans perform natural tasks with natural images. Attempts to understand the phenomenology of vision from artificial stimuli, although worthwhile as a starting point, can lead to faulty generalizations about visual systems, because of the enormous complexity of natural images. Dealing with this complexity is daunting, but Bayesian inference on structured probability distributions offers the ability to design theories of vision that can deal with the complexity of natural images, and that use 'analysis by synthesis' strategies with intriguing similarities to the brain. We examine these strategies using recent examples from computer vision, and outline some important imlications for cognitive science. {\textcopyright} 2006 Elsevier Ltd. All rights reserved.},
author = {Yuille, Alan and Kersten, Daniel},
file = {:Users/hahosoya/Documents/Mendeley Desktop/17ac6928c5e5b3838313cbacd6f0272e61fd55e9.pdf:pdf},
journal = {Trends Cogn. Sci.},
number = {7},
pages = {301--308},
pmid = {16784882},
title = {{Vision as Bayesian inference: analysis by synthesis?}},
volume = {10},
year = {2006}
}
@article{Levitt1994,
author = {Levitt, Jonathan B and Kiper, Daniel C and Movshon, J Anthony},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neurophysiology 1994 Levitt.pdf:pdf},
journal = {J. Neurophysiol.},
month = {jan},
number = {6},
pages = {2517--2542},
title = {{Receptive fields and functional architecture of macaque V2}},
volume = {71},
year = {1994}
}
@article{Lafer-Sousa2013,
abstract = {Nature Neuroscience 16, 1870 (2013). doi:10.1038/nn.3555},
author = {Lafer-Sousa, Rosa and Conway, Bevil R},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature Publishing Group 2013 Lafer-Sousa.pdf:pdf},
journal = {Nat. Neurosci.},
month = {oct},
number = {12},
pages = {1870--1878},
title = {{Parallel, multi-stage processing of colors, faces and shapes in macaque inferior temporal cortex}},
volume = {16},
year = {2013}
}
@article{Tkacik2010,
abstract = {Abstract The visual system is challenged with extracting and representing behaviorally relevant information contained in natural inputs of great complexity and detail. This task begins in the sensory periphery: retinal receptive fields and circuits are matched to the first  ...},
author = {Tka{\v{c}}ik, G and Prentice, J S and et Al},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proceedings of the National Academy of Sciences USA 2010 Tka{\v{c}}ik.pdf:pdf},
journal = {Proc. Natl. Acad. Sci. USA},
month = {jan},
title = {{Local statistics in natural scenes predict the saliency of synthetic textures}},
year = {2010}
}
@article{Beck2012,
abstract = {Abstract Recent experiments have demonstrated that humans and animals typically reason probabilistically about their environment. This ability requires a neural code that represents probability distributions and neural circuits that are capable of implementing the ... 
},
author = {Beck, J and Heller, K and Pouget, A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2012 Beck.pdf:pdf},
journal = {{\ldots} Neural Inf. Process. Syst. {\ldots}},
month = {jan},
title = {{Complex Inference in Neural Circuits with Probabilistic Population Codes and Topic Models}},
year = {2012}
}
@article{Kaas1997,
abstract = {In all mammals, much of the neocortex consists of orderly representations or maps of receptor 
surfaces that are typically topographic at a global level, while being modular at the local
level. These representations appear to emerge in development as a result of a few  ...},
author = {Kaas, J H},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Brain research bulletin 1997 Kaas.pdf:pdf},
journal = {Brain Res. Bull.},
month = {jan},
title = {{Topographic maps are fundamental to sensory processing}},
year = {1997}
}
@article{Loaiza-Ganem2019,
abstract = {Variational autoencoders (VAE) have quickly become a central tool in machine learning, applicable to a broad range of data types and latent variable models. By far the most common first step, taken by seminal papers and by core software libraries alike, is to model MNIST data using a deep network parameterizing a Bernoulli likelihood. This practice contains what appears to be and what is often set aside as a minor inconvenience: the pixel data is [0,1] valued, not {0,1} as supported by the Bernoulli likelihood. Here we show that, far from being a triviality or nuisance that is convenient to ignore, this error has profound importance to VAE, both qualitative and quantitative. We introduce and fully characterize a new [0,1]-supported, single parameter distribution: the continuous Bernoulli, which patches this pervasive bug in VAE. This distribution is not nitpicking; it produces meaningful performance improvements across a range of metrics and datasets, including sharper image samples, and suggests a broader class of performant VAE.},
archivePrefix = {arXiv},
arxivId = {1907.06845},
author = {Loaiza-Ganem, Gabriel and Cunningham, John P.},
eprint = {1907.06845},
file = {:Users/hahosoya/Documents/Mendeley Desktop/ace7261ed21618c445b385dd76cb3f2712a660fb.pdf:pdf},
number = {NeurIPS},
pages = {1--11},
title = {{The continuous Bernoulli: fixing a pervasive error in variational autoencoders}},
year = {2019}
}
@article{Biswal2010,
author = {Biswal, B B and Mennes, M and Zuo, X N and Gohel, S and Kelly, C and Smith, S M and Beckmann, C F and Adelstein, J S and Buckner, R L and Colcombe, S and Dogonowski, A M and Ernst, M and Fair, D and Hampson, M and Hoptman, M J and Hyde, J S and Kiviniemi, V J and Kotter, R and Li, S J and Lin, C P and Lowe, M J and Mackay, C and Madden, D J and Madsen, K H and Margulies, D S and Mayberg, H S and McMahon, K and Monk, C S and Mostofsky, S H and Nagel, B J and Pekar, J J and Peltier, S J and Petersen, S E and Riedl, V and Rombouts, S A R B and Rypma, B and Schlaggar, B L and Schmidt, S and Seidler, R D and Siegle, G J and Sorg, C and Teng, G J and Veijola, J and Villringer, A and Walter, M and Wang, L and Weng, X C and Whitfield-Gabrieli, S and Williamson, P and Windischberger, C and Zang, Y F and Zhang, H Y and Castellanos, F X and Milham, M P},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proceedings of the National Academy of Sciences 2010 Biswal.pdf:pdf},
journal = {Proc. Natl. Acad. Sci.},
month = {mar},
number = {10},
pages = {4734--4739},
title = {{Toward discovery science of human brain function}},
volume = {107},
year = {2010}
}
@article{Yue2014,
author = {Yue, X and Pourladian, I S and Tootell, R B H and Ungerleider, L G},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proceedings of the National Academy of Sciences 2014 Yue.pdf:pdf},
journal = {Proc. Natl. Acad. Sci.},
month = {aug},
number = {33},
pages = {E3467--E3475},
title = {{Curvature-processing network in macaque visual cortex}},
volume = {111},
year = {2014}
}
@article{Lamme2010a,
abstract = {Consciousness is considered one of the 'final frontiers' in modern science. The phenomenon seems to escape all attempts to scientific reduction, and some philosphers argue that we may never be able to reveal its true nature. During the last decades, the subject has been ...
},
author = {Lamme, V A F},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2010 Lamme.pdf:pdf},
journal = {Unpubl. Artic.},
month = {jan},
title = {{Can neuroscience reveal the true nature of consciousness?}},
year = {2010}
}
@article{Srivastava2012,
abstract = {Abstract A Deep Boltzmann Machine is described for learning a generative model of data that consists of multiple and diverse input modalities. The model can be used to extract a unified representation that fuses modalities together. We find that this representation is  ...},
author = {Srivastava, N and Salakhutdinov, R},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Advances in neural information {\ldots} 2012 Srivastava.pdf:pdf},
journal = {Adv. Neural Inf. {\ldots}},
month = {jan},
title = {{Multimodal learning with deep boltzmann machines}},
year = {2012}
}
@article{Dorr2010,
author = {Dorr, M and Martinetz, T and Gegenfurtner, K R and Barth, E},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Vision 2010 Dorr.pdf:pdf},
journal = {J. Vis.},
month = {aug},
number = {10},
pages = {28},
title = {{Variability of eye movements when viewing dynamic natural scenes}},
volume = {10},
year = {2010}
}
@article{Freeman2013,
abstract = {There is no generally accepted account of the function of the second visual cortical area (V2), partly because no simple response properties robustly distinguish V2 neurons from those in primary visual cortex (V1). We constructed synthetic stimuli replicating the higher-order statistical dependencies found in natural texture images and used them to stimulate macaque V1 and V2 neurons. Most V2 cells responded more vigorously to these textures than to control stimuli lacking naturalistic structure; V1 cells did not. Functional magnetic resonance imaging (fMRI) measurements in humans revealed differences between V1 and V2 that paralleled the neuronal measurements. The ability of human observers to detect naturalistic structure in different types of texture was well predicted by the strength of neuronal and fMRI responses in V2 but not in V1. Together, these results reveal a particular functional role for V2 in the representation of natural image structure.},
address = {Center for Neural Science, New York University, New York, New York, USA. freemanj11@janelia.hhmi.org},
author = {Freeman, Jeremy and Ziemba, Corey M and Heeger, David J and Simoncelli, Eero P and Movshon, J Anthony},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature neuroscience 2013 Freeman-1.pdf:pdf},
journal = {Nat. Neurosci.},
month = {jul},
number = {7},
pages = {974--981},
title = {{A functional and perceptual signature of the second visual area in primates.}},
volume = {16},
year = {2013}
}
@article{Zhu,
abstract = {Image-to-image translation is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output image using a training set of aligned image pairs. However, for many tasks, paired training data will not be available. We {\ldots}
},
author = {Zhu, J Y and Park, T and Isola, P and arXiv Preprint, A A Efros and 2017},
file = {:Users/hahosoya/Documents/Mendeley Desktop/E6D0184E-032C-4419-A5B5-A70F8EB17E91.pdf:pdf},
journal = {openaccess.thecvf.com },
title = {{Unpaired image-to-image translation using cycle-consistent adversarial networks}},
}
@article{Agrawal2016,
abstract = {Recently, a number of deep-learning based models have been proposed for the task of Visual Question Answering (VQA). The performance of most models is clustered around 60-70%. In this paper we propose systematic methods to analyze the behavior of these models as a first step towards recognizing their strengths and weaknesses, and identifying the most fruitful directions for progress. We analyze two models, one each from two major classes of VQA models - with-attention and without-attention and show the similarities and differences in the behavior of these models. We also analyze the winning entry of the VQA Challenge 2016. Our behavior analysis reveals that despite recent progress, today's VQA models are “myopic” (tend to fail on sufficiently novel instances), often “jump to conclusions” (converge on a predicted answer after 'listening' to just half the question), and are “stubborn” (do not change their answers across images).},
archivePrefix = {arXiv},
arxivId = {1606.07356},
author = {Agrawal, Aishwarya and Batra, Dhruv and Parikh, Devi},
eprint = {1606.07356},
file = {:Users/hahosoya/Documents/Mendeley Desktop/0914eb61b743300828c84f9e235ce6165a171be5.pdf:pdf},
isbn = {9781945626258},
journal = {EMNLP 2016 - Conf. Empir. Methods Nat. Lang. Process. Proc.},
pages = {1955--1960},
title = {{Analyzing the behavior of visual question answering models}},
year = {2016}
}
@article{Tononi2004,
abstract = {BACKGROUND:Consciousness poses two main problems. The first is understanding the conditions that determine to what extent a system has conscious experience. For instance, why is our consciousness generated by certain parts of our brain, such as the thalamocortical system, and not by other parts, such as the cerebellum? And why are we conscious during wakefulness and much less so during dreamless sleep? The second problem is understanding the conditions that determine what kind of consciousness a system has. For example, why do specific parts of the brain contribute specific qualities to our conscious experience, such as vision and audition?

PRESENTATION OF THE HYPOTHESIS:This paper presents a theory about what consciousness is and how it can be measured. According to the theory, consciousness corresponds to the capacity of a system to integrate information. This claim is motivated by two key phenomenological properties of consciousness: differentiation - the availability of a very large number of conscious experiences; and integration - the unity of each such experience. The theory states that the quantity of consciousness available to a system can be measured as the Phi value of a complex of elements. Phi is the amount of causally effective information that can be integrated across the informational weakest link of a subset of elements. A complex is a subset of elements with Phi>0 that is not part of a subset of higher Phi. The theory also claims that the quality of consciousness is determined by the informational relationships among the elements of a complex, which are specified by the values of effective information among them. Finally, each particular conscious experience is specified by the value, at any given time, of the variables mediating informational interactions among the elements of a complex.

TESTING THE HYPOTHESIS:The information integration theory accounts, in a principled manner, for several neurobiological observations concerning consciousness. As shown here, these include the association of consciousness with certain neural systems rather than with others; the fact that neural processes underlying consciousness can influence or be influenced by neural processes that remain unconscious; the reduction of consciousness during dreamless sleep and generalized seizures; and the time requirements on neural interactions that support consciousness.

IMPLICATIONS OF THE HYPOTHESIS:The theory entails that consciousness is a fundamental quantity, that it is graded, that it is present in infants and animals, and that it should be possible to build conscious artifacts.},
address = {Department of Psychiatry, University of Wisconsin, Madison, USA. gtononi@wisc.edu},
author = {Tononi, Giulio},
file = {:Users/hahosoya/Documents/Mendeley Desktop/BMC neuroscience 2004 Tononi.pdf:pdf},
journal = {BMC Neurosci.},
month = {nov},
pages = {42},
title = {{An information integration theory of consciousness.}},
volume = {5},
year = {2004}
}
@article{Becker1993,
abstract = {... perceptual learning. We have demonstrated that a network can learn, entirely 
unsupervised, to build translation- invariant object detectors based on the assumption
of temporal  coherence about the input. This procedure should ...
},
author = {Becker, S},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Advances in neural information processing systems 1993 Becker.pdf:pdf},
journal = {Adv. Neural Inf. Process. Syst.},
month = {jan},
title = {{Learning to categorize objects using temporal coherence}},
year = {1993}
}
@book{Beyeler2019,
abstract = {Supported by recent computational studies, there is increasing evidence that a wide range of neuronal responses can be understood as an emergent property of nonnegative sparse coding (NSC), an efficient population coding scheme based on dimensionality reduction and sparsity constraints. We review evidence that NSC might be employed by sensory areas to efficiently encode external stimulus spaces, by some associative areas to conjunctively represent multiple behaviorally relevant variables, and possibly by the basal ganglia to coordinate movement. In addition, NSC might provide a useful theoretical framework under which to understand the often complex and nonintuitive response properties of neurons in other brain areas. Although NSC might not apply to all brain areas (for example, motor or executive function areas) the success of NSC-based models, especially in sensory areas, warrants further investigation for neural correlates in other regions.},
author = {Beyeler, Michael and Rounds, Emily L. and Carlson, Kristofor D. and Dutt, Nikil and Krichmar, Jeffrey L.},
booktitle = {PLoS Comput. Biol.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/journal.pcbi.1006908.pdf:pdf},
isbn = {1111111111},
number = {6},
pages = {e1006908},
title = {{Neural correlates of sparse coding and dimensionality reduction}},
volume = {15},
year = {2019}
}
@article{RatanMurty2018,
author = {{Ratan Murty}, N Apurva and Arun, S P},
file = {:Users/hahosoya/Documents/Mendeley Desktop/61EA9C5F-1B5C-44AB-8E16-AC6471D3EE48.pdf:pdf},
journal = {Proc. Natl. Acad. Sci. U. S. A.},
month = {apr},
number = {14},
pages = {E3276--E3285},
title = {{Multiplicative mixing of object identity and image attributes in single inferior temporal neurons}},
volume = {115},
year = {2018}
}
@article{Hubel1965,
author = {Hubel, David H and Wiesel, Torsten N},
file = {:Users/hahosoya/Documents/Mendeley Desktop/1965 Hubel.pdf:pdf},
journal = {J. neurophysiol},
month = {jan},
number = {2},
pages = {229--289},
title = {{Receptive fields and functional architecture in two nonstriate visual areas (18 and 19) of the cat}},
volume = {28},
year = {1965}
}
@article{Minka2001,
abstract = {... In belief networks with loops it is known that approximate marginal distributions can be obtained ... 
more general than belief  propagation in two ways: (1) like variational methods, it can use ... and
(2) it can impose useful constraints on functional form, such as multivariate Gaus- sian. ...
},
author = {Minka, T P},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Uncertainty in Artificial Intelligence 2001 Minka.pdf:pdf},
journal = {Uncertain. Artif. Intell.},
month = {jan},
title = {{Expectation propagation for approximate Bayesian inference}},
year = {2001}
}
@article{Tsao2018,
abstract = {Nature, doi:10.1038/s41586-018-0459-6},
author = {Tsao, Albert and Sugar, J{\o}rgen and Lu, Li and Wang, Cheng and Knierim, James J and Moser, May-Britt and Moser, Edvard I},
file = {:Users/hahosoya/Documents/Mendeley Desktop/BEFE5EB6-CEBB-48B8-8836-83EC291E1692.pdf:pdf},
journal = {Nature},
month = {aug},
number = {7721},
pages = {1--21},
title = {{Integrating time from experience in the lateral entorhinal cortex}},
volume = {561},
year = {2018}
}
@article{Li2012,
abstract = {Neuron, 75 (2012) 250-264. doi:10.1016/j.neuron.2012.06.030},
author = {Li, CharlesnbspD Gilbert Wu and Li, Wu},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neuron 2012 Li.pdf:pdf},
journal = {Neuron},
month = {jul},
number = {2},
pages = {250--264},
title = {{Adult Visual Cortical Plasticity}},
volume = {75},
year = {2012}
}
@article{Freiwald2021,
abstract = {Faces are complex objects of great variety, which the visual brain somehow manages to organize by similarity. Two such orderings in fact exist and one, a new study finds, is transformed into the other over time, enhancing a face's distinctiveness.},
author = {Freiwald, Winrich A. and Hosoya, Haruo},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Freiwald_Hosoya_Current Biology Dispatch 2021.pdf:pdf},
journal = {Curr. Biol.},
number = {1},
pages = {R13--R15},
title = {{Neuroscience: A Face's Journey through Space and Time}},
volume = {31},
year = {2021}
}
@article{Tanaka2003,
abstract = {Cells in the  cortex (area TE) selectively respond to complex visual object features and those that respond to similar features cluster in a columnar region elongated vertical to the cortical surface. What are the functional roles of the column structure in the },
author = {Tanaka, K},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Cerebral Cortex 2003 Tanaka.pdf:pdf},
journal = {Cereb. Cortex},
month = {jan},
title = {{Columns for complex visual object features in the inferotemporal cortex: clustering of cells with similar but slightly different stimulus selectivities}},
year = {2003}
}
@article{Bentin1996,
author = {Bentin, Shlomo and Allison, Truett and Puce, Aina and Perez, Erik and McCarthy, Gregory},
file = {:Users/hahosoya/Documents/Mendeley Desktop/J Cogn Neurosci 1996 Bentin.pdf:pdf},
journal = {J. Cogn. Neurosci.},
month = {nov},
number = {6},
pages = {551--565},
title = {{Electrophysiological Studies of Face Perception in Humans}},
volume = {8},
year = {1996}
}
@article{Xie2016,
abstract = {Clustering is central to many data-driven application domains and has been studied extensively in terms of distance functions and grouping algorithms. Relatively little work has focused on learning representations for clustering. In this paper, we propose Deep Embedded Clustering (DEC), a method that simultaneously learns feature representations and cluster assignments using deep neural networks. DEC learns a mapping from the data space to a lower-dimensional feature space in which it iteratively optimizes a clustering objective. Our experimental evaluations on image and text corpora show significant improvement over state-of-the-art methods.},
archivePrefix = {arXiv},
arxivId = {1511.06335},
author = {Xie, Junyuan and Girshick, Ross and Farhadi, Ali},
eprint = {1511.06335},
file = {:Users/hahosoya/Documents/Mendeley Desktop/97090d0161aef092db3261504ad8244853d9d627.pdf:pdf},
isbn = {9781510829008},
journal = {33rd Int. Conf. Mach. Learn. ICML 2016},
pages = {740--749},
title = {{Unsupervised deep embedding for clustering analysis}},
volume = {1},
year = {2016}
}
@article{Berkes2006,
abstract = {In this letter, we introduce some mathematical and numerical tools to analyze and interpret inhomogeneous quadratic forms. The resulting characterization is in some aspects similar to that given by experimental studies of cortical cells, making it particularly suitable for application to second-order approximations and theoretical models of physiological receptive fields. We first discuss two ways of analyzing a quadratic form by visualizing the coefficients of its quadratic and linear term directly and by considering the eigenvectors of its quadratic term. We then present an algorithm to compute the optimal excitatory and inhibitory stimuli--those that maximize and minimize the considered quadratic form, respectively, given a fixed energy constraint. The analysis of the optimal stimuli is completed by considering their invariances, which are the transformations to which the quadratic form is most insensitive, and by introducing a test to determine which of these are statistically significant. Next we propose a way to measure the relative contribution of the quadratic and linear term to the total output of the quadratic form. Furthermore, we derive simpler versions of the above techniques in the special case of a quadratic form without linear term. In the final part of the letter, we show that for each quadratic form, it is possible to build an equivalent two-layer neural network, which is compatible with (but more general than) related networks used in some recent articles and with the energy model of complex cells. We show that the neural network is unique only up to an arbitrary orthogonal transformation of the excitatory and inhibitory subunits in the first layer.},
address = {Institute for Theoretical Biology, Humboldt University Berlin, D-10115 Berlin, Germany. p.berkes@biologie.hu-berlin.de},
author = {Berkes, Pietro and Wiskott, Laurenz},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neural Computation 2006 Berkes.pdf:pdf},
journal = {Neural Comput.},
month = {aug},
number = {8},
pages = {1868--1895},
title = {{On the analysis and interpretation of inhomogeneous quadratic forms as receptive fields.}},
volume = {18},
year = {2006}
}
@article{OostwoudWijdenes2015,
author = {{Oostwoud Wijdenes}, L and Marshall, L and Bays, P M},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2015 Oostwoud Wijdenes.pdf:pdf},
journal = {J. Neurosci.},
month = {jul},
number = {28},
pages = {10146--10153},
title = {{Evidence for Optimal Integration of Visual Feature Representations across Saccades}},
volume = {35},
year = {2015}
}
@article{Hirayama2013,
author = {Hirayama},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2013 Hirayama.pdf:pdf},
month = {jun},
pages = {1--9},
title = {{Analyzing connected activities of latent sources with a simple two-layer generative model }},
year = {2013}
}
@article{Smolyanskaya2013,
author = {Smolyanskaya, A and Ruff, D A and Born, R T},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neurophysiology 2013 Smolyanskaya.pdf:pdf},
journal = {J. Neurophysiol.},
month = {dec},
number = {12},
pages = {2806--2816},
title = {{Joint tuning for direction of motion and binocular disparity in macaque MT is largely separable}},
volume = {110},
year = {2013}
}
@article{Ringach1997a,
abstract = {...  Dynamics of orientation tuning in macaque primary visual cortex Dario L. Ringach , Michael J. Hawken & ... the presentation of a particular orientation in the NATURE I VOL 387115 MAY 1997 281 ... We studied the dynamics of orientation tuning by looking at how the distributions rr(0 ...},
author = {Ringach, D L and Hawken, M J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature 1997 Ringach.pdf:pdf},
journal = {Nature},
month = {jan},
title = {{Dynamics of orientation tuning in macaque primary visual cortex}},
year = {1997}
}
@article{Olshausen2007,
abstract = {ABSTRACT Previous work on unsupervised learning has shown that it is possible to learn Gabor-like feature representations, similar to those employed in the primary visual cortex, from the statistics of natural images . However, such representations are still not readily ... 
},
author = {Olshausen, Bruno A and Cadieu, Charles and Culpepper, Jack and Warland, David K},
editor = {Rogowitz, Bernice E and Pappas, Thrasyvoulos N and Daly, Scott J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Electronic Imaging 2007 2007 Olshausen.pdf:pdf},
journal = {Electron. Imaging 2007},
month = {feb},
pages = {649206--649210},
title = {{<title>Bilinear models of natural images</title>}},
volume = {6492},
year = {2007}
}
@article{Kar2021,
abstract = {Distributed neural population spiking patterns in macaque inferior temporal (IT) cortex that support core object recognition require additional time to develop for specific, “late-solved” images. This suggests the necessity of recurrent processing in these computations. Which brain circuits are responsible for computing and transmitting these putative recurrent signals to IT? To test whether the ventrolateral prefrontal cortex (vlPFC) is a critical recurrent node in this system, here, we pharmacologically inactivated parts of vlPFC and simultaneously measured IT activity while monkeys performed object discrimination tasks. vlPFC inactivation deteriorated the quality of late-phase (>150 ms from image onset) IT population code and produced commensurate behavioral deficits for late-solved images. Finally, silencing vlPFC caused the monkeys' IT activity and behavior to become more like those produced by feedforward-only ventral stream models. Together with prior work, these results implicate fast recurrent processing through vlPFC as critical to producing behaviorally sufficient object representations in IT.},
author = {Kar, Kohitij and DiCarlo, James J.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/1-s2.0-S0896627320307595-main.pdf:pdf},
journal = {Neuron},
keywords = {core object recognition,deep neural networks,inferior temporal cortex,muscimol,population codes,ventrolateral PFC},
number = {1},
pages = {164--176.e5},
pmid = {33080226},
title = {{Fast Recurrent Processing via Ventrolateral Prefrontal Cortex Is Needed by the Primate Ventral Stream for Robust Core Visual Object Recognition}},
volume = {109},
year = {2021}
}
@article{Olshausen2005,
abstract = {A wide variety of papers have reviewed what is known about the function of primary visual cortex. In this review, rather than stating what is known, we attempt to estimate how much is still unknown about V1 function. In particular, we identify five problems with the current view of V1 that stem largely from experimental and theoretical biases, in addition to the contributions of nonlinearities in the cortex that are not well understood. Our purpose is to open the door to new theories, a number of which we describe, along with some proposals for testing them.},
author = {Olshausen, Bruno A and Field, David J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neural Computation 2005 Olshausen.pdf:pdf},
journal = {Neural Comput.},
month = {aug},
number = {8},
pages = {1665--1699},
title = {{How Close Are We to Understanding V1?}},
volume = {17},
year = {2005}
}
@article{Lillicrap2020a,
abstract = {During learning, the brain modifies synapses to improve behaviour. In the cortex, synapses are embedded within multilayered networks, making it difficult to determine the effect of an individual synaptic modification on the behaviour of the system. The backpropagation algorithm solves this problem in deep artificial neural networks, but historically it has been viewed as biologically problematic. Nonetheless, recent developments in neuroscience and the successes of artificial neural networks have reinvigorated interest in whether backpropagation offers insights for understanding learning in the cortex. The backpropagation algorithm learns quickly by computing synaptic updates using feedback connections to deliver error signals. Although feedback connections are ubiquitous in the cortex, it is difficult to see how they could deliver the error signals required by strict formulations of backpropagation. Here we build on past and recent developments to argue that feedback connections may instead induce neural activities whose differences can be used to locally approximate these signals and hence drive effective learning in deep networks in the brain.},
author = {Lillicrap, Timothy P. and Santoro, Adam and Marris, Luke and Akerman, Colin J. and Hinton, Geoffrey},
file = {:Users/hahosoya/Documents/Mendeley Desktop/s41583-020-0277-3.pdf:pdf},
journal = {Nat. Rev. Neurosci.},
number = {6},
pages = {335--346},
pmid = {32303713},
title = {{Backpropagation and the brain}},
volume = {21},
year = {2020}
}
@book{Knill1996,
abstract = {This book provides an introduction to and critical analysis of the Bayesian paradigm.},
author = {Knill, David C},
isbn = {9780521461092},
month = {jan},
pages = {516},
title = {{No Title}},
year = {1996}
}
@article{Paik2011,
abstract = {The orientation map is a hallmark of primary visual cortex in higher mammals. It is not yet known how orientation maps develop, what function they have in visual processing and why some species lack them. Here we advance the notion that quasi-periodic orientation maps are established by moir{\'{e}} interference of regularly spaced ON- and OFF-center retinal ganglion cell mosaics. A key prediction of the theory is that the centers of iso-orientation domains must be arranged in a hexagonal lattice on the cortical surface. Here we show that such a pattern is observed in individuals of four different species: monkeys, cats, tree shrews and ferrets. The proposed mechanism explains how orientation maps can develop without requiring precise patterns of spontaneous activity or molecular guidance. Further, it offers a possible account for the emergence of orientation tuning in single neurons despite the absence of orderly orientation maps in rodents species.},
address = {Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, California, USA.},
author = {Paik, Se-Bum and Ringach, Dario L},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature neuroscience 2011 Paik-1.pdf:pdf},
journal = {Nat. Neurosci.},
month = {jan},
number = {7},
pages = {919--925},
title = {{Retinal origin of orientation maps in visual cortex.}},
volume = {14},
year = {2011}
}
@article{Girman1999,
abstract = {The rat is used widely to study various aspects of vision including developmental events and numerous pathologies, but surprisingly little is known about the functional properties of single neurons in the rat primary visual cortex (V1). These were investigated in the anesthetized ( ...},
author = {Girman, S V and Sauv{\'{e}}, Y},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neurophysiology 1999 Girman.pdf:pdf},
journal = {J. Neurophysiol.},
month = {jan},
title = {{Receptive Field Properties of Single Neurons in Rat Primary Visual Cortex}},
year = {1999}
}
@article{Sugase-Miyamoto2014,
abstract = {To investigate the effect of face inversion and thatcherization (eye inversion) on temporal processing stages of facial information, single neuron activities in the temporal cortex (area TE) of two rhesus monkeys were recorded. Test stimuli were colored pictures of monkey faces (four with four different expressions), human faces (three with four different expressions), and geometric shapes. Modifications were made in each face-picture, and its four variations were used as stimuli: upright original, inverted original, upright thatcherized, and inverted thatcherized faces. A total of 119 neurons responded to at least one of the upright original facial stimuli. A majority of the neurons (71%) showed activity modulations depending on upright and inverted presentations, and a lesser number of neurons (13%) showed activity modulations depending on original and thatcherized face conditions. In the case of face inversion, information about the fine category (facial identity and expression) decreased, whereas information about the global category (monkey vs human vs shape) was retained for both the original and thatcherized faces. Principal component analysis on the neuronal population responses revealed that the global categorization occurred regardless of the face inversion and that the inverted faces were represented near the upright faces in the principal component analysis space. By contrast, the face inversion decreased the ability to represent human facial identity and monkey facial expression. Thus, the neuronal population represented inverted faces as faces but failed to represent the identity and expression of the inverted faces, indicating that the neuronal representation in area TE cause the perceptual effect of face inversion.},
address = {Human Technology Research Institute, National Institute of Advanced Industrial Science and Technology, Tsukuba 305-8568, Japan, y-sugase@aist.go.jp.},
author = {Sugase-Miyamoto, Yasuko and Matsumoto, Narihisa and Ohyama, Kaoru and Kawano, Kenji},
file = {:Users/hahosoya/Documents/Mendeley Desktop/33473FE9-50A3-4CDD-B119-921526AB14C0.pdf:pdf},
journal = {J. Neurosci.},
month = {sep},
number = {37},
pages = {12457--12469},
title = {{Face inversion decreased information about facial identity and expression in face-responsive neurons in macaque area TE.}},
volume = {34},
year = {2014}
}
@article{Nayebi2018,
abstract = {Feed-forward convolutional neural networks (CNNs) are currently state-of-the-art for object classification tasks such as ImageNet. Further, they are quantitatively accurate models of temporally-averaged responses of neurons in the primate brain's visual system. However, biological visual systems have two ubiquitous architectural features not shared with typical CNNs: local recurrence within cortical areas, and long-range feedback from downstream areas to upstream areas. Here we explored the role of recurrence in improving classification performance. We found that standard forms of recurrence (vanilla RNNs and LSTMs) do not perform well within deep CNNs on the ImageNet task. In contrast, novel cells that incorporated two structural features, bypassing and gating, were able to boost task accuracy substantially. We extended these design principles in an automated search over thousands of model architectures, which identified novel local recurrent cells and long-range feedback connections useful for object recognition. Moreover, these task-optimized ConvRNNs matched the dynamics of neural activity in the primate visual system better than feedforward networks, suggesting a role for the brain's recurrent connections in performing difficult visual behaviors.},
author = {Nayebi, Aran and Bear, Daniel and Kubilius, Jonas and Kar, Kohitij and Ganguli, Surya and Sussillo, David and DiCarlo, James J. and Yamins, Daniel L.K.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/eb667a11aaded97df03f6552508839d0ba750280.pdf:pdf},
journal = {arXiv},
number = {NeurIPS},
title = {{Task-driven convolutional recurrent models of the visual system}},
year = {2018}
}
@article{Kriegeskorte2019,
abstract = {Encoding and decoding models are widely used in systems, cognitive, and computational neuroscience to make sense of brain-activity data. However, the interpretation of their results requires care. Decoding models can help reveal whether particular information is present in a brain region in a format the decoder can exploit. Encoding models make comprehensive predictions about representational spaces. In the context of sensory experiments, where stimuli are experimentally controlled, encoding models enable us to test and compare brain-computational theories. Encoding and decoding models typically include fitted linear-model components. Sometimes the weights of the fitted linear combinations are interpreted as reflecting, in an encoding model, the contribution of different sensory features to the representation or, in a decoding model, the contribution of different measured brain responses to a decoded feature. Such interpretations can be problematic when the predictor variables or their noise components are correlated and when priors (or penalties)are used to regularize the fit. Encoding and decoding models are evaluated in terms of their generalization performance. The correct interpretation depends on the level of generalization a model achieves (e.g. to new response measurements for the same stimuli, to new stimuli from the same population, or to stimuli from a different population). Significant decoding or encoding performance of a single model (at whatever level of generality)does not provide strong constraints for theory. Many models must be tested and inferentially compared for analyses to drive theoretical progress.},
archivePrefix = {arXiv},
arxivId = {1812.00278},
author = {Kriegeskorte, Nikolaus and Douglas, Pamela K.},
eprint = {1812.00278},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Kriegeskorte_Douglas_2019_Interpreting encoding and decoding models.pdf:pdf},
journal = {Curr. Opin. Neurobiol.},
number = {April},
pages = {167--179},
title = {{Interpreting encoding and decoding models}},
volume = {55},
year = {2019}
}
@article{Goodfellow2014,
abstract = {Abstract We propose a new framework for estimating generative models via adversarial nets, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample 
},
author = {Goodfellow, I and Pouget-Abadie, J and Mirza, M},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Advances in neural information processing systems 2014 Goodfellow.pdf:pdf},
journal = {Adv. Neural Inf. Process. Syst.},
month = {jan},
title = {{Generative adversarial nets}},
year = {2014}
}
@article{Felleman2015,
author = {Felleman, Daniel J and Lim, Heejin and Xiao, Youping and Wang, Yi and Eriksson, Anastasia and Parajuli, Arun},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Cerebral Cortex 2015 Felleman.pdf:pdf},
journal = {Cereb. Cortex},
month = {aug},
number = {9},
pages = {2354--2369},
title = {{The Representation of Orientation in Macaque V2: Four Stripes Not Three}},
volume = {25},
year = {2015}
}
@article{Cope2018,
author = {Cope, Alex J and Vasilaki, Eleni and Minors, Dorian and Sabo, Chelsea and Marshall, James A R and Barron, Andrew B},
editor = {Ayers, Joseph},
file = {:Users/hahosoya/Documents/Mendeley Desktop/3F980E93-EAE7-4C57-B07E-65708CEDD8CC.pdf:pdf},
journal = {PLoS Comput. Biol.},
month = {sep},
number = {9},
pages = {e1006435--21},
title = {{Abstract concept learning in a simple neural network inspired by the insect brain}},
volume = {14},
year = {2018}
}
@article{Girolami2006,
author = {Girolami, M and Rogers, S},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neural Computation 2006 Girolami.pdf:pdf},
journal = {Neural Comput.},
month = {jan},
number = {8},
pages = {1790--1817},
title = {{Variational bayesian multinomial probit regression with gaussian process priors}},
volume = {18},
year = {2006}
}
@article{Hurri2002,
author = {Hurri, Jarmo and Hyv{\"{a}}rinen, Aapo},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Advances in neural information processing systems 2002 Hurri.pdf:pdf},
journal = {Adv. Neural Inf. Process. Syst.},
month = {jan},
pages = {141--148},
title = {{Temporal coherence, natural image sequences, and the visual cortex}},
year = {2002}
}
@article{Chen2014,
abstract = {Neuron, 82 (2014) 682-694. doi:10.1016/j.neuron.2014.03.023},
author = {Chen, Minggui and Yan, Yin and Gong, Xiajing and Gilbert, Charles D and Liang, Hualou and Li, Wu},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neuron 2014 Chen.pdf:pdf},
journal = {Neuron},
month = {may},
number = {3},
pages = {682--694},
title = {{Incremental Integration of Global Contours through Interplay between Visual Cortical Areas}},
volume = {82},
year = {2014}
}
@article{Watters2019,
abstract = {We present a simple neural rendering architecture that helps variational autoencoders (VAEs) learn disentangled representations. Instead of the deconvolutional network typically used in the decoder of VAEs, we tile (broadcast) the latent vector across space, concatenate fixed X- and Y-"coordinate" channels, and apply a fully convolutional network with 1x1 stride. This provides an architectural prior for dissociating positional from non-positional features in the latent distribution of VAEs, yet without providing any explicit supervision to this effect. We show that this architecture, which we term the Spatial Broadcast decoder, improves disentangling, reconstruction accuracy, and generalization to held-out regions in data space. It provides a particularly dramatic benefit when applied to datasets with small objects. We also emphasize a method for visualizing learned latent spaces that helped us diagnose our models and may prove useful for others aiming to assess data representations. Finally, we show the Spatial Broadcast Decoder is complementary to state-of-the-art (SOTA) disentangling techniques and when incorporated improves their performance.},
archivePrefix = {arXiv},
arxivId = {1901.07017},
author = {Watters, Nicholas and Matthey, Loic and Burgess, Christopher P. and Lerchner, Alexander},
eprint = {1901.07017},
file = {:Users/hahosoya/Documents/Mendeley Desktop/ef1fd3e181d66ffb18596fe03a0897963fa12ab2.pdf:pdf},
pages = {1--35},
title = {{Spatial Broadcast Decoder: A Simple Architecture for Learning Disentangled Representations in VAEs}},
year = {2019}
}
@article{Amari,
author = {Amari, S and Cichocki, A and Yang, H H},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Amari.pdf:pdf},
title = {{A New Learning Algorithm for Blind Signal Separation}},
}
@article{Hosoya2019a,
address = {Brentwood, Tennessee, USA},
author = {Hosoya, Haruo},
file = {:Users/hahosoya/Documents/Mendeley Desktop/38b66c19d9bb61df1e98c6f5f54fec378f858476.pdf:pdf},
journal = {2019 Conf. Cogn. Comput. Neurosci.},
keywords = {invariant representation,variational autoencoder},
title = {{A deep generative model explaining tuning properties of monkey face processing patches}},
year = {2019}
}
@article{Hinton2011,
abstract = {The artificial neural networks that are used to recognize shapes typically use one or more layers of learned feature detectors that produce scalar outputs. By contrast, the computer vision community uses complicated, hand-engineered features, like SIFT [6], that produce a 
},
author = {Hinton, G and Krizhevsky, A and Wang, S},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Artificial Neural Networks and Machine Learning (ICANN 2011) 2011 Hinton.pdf:pdf},
journal = {Artif. Neural Networks Mach. Learn. (ICANN 2011)},
month = {jan},
pages = {44--51},
title = {{Transforming auto-encoders}},
volume = {6791},
year = {2011}
}
@article{Samonds2018,
abstract = {Nature Neuroscience, doi:10.1038/s41593-018-0255-5},
author = {Samonds, Jason M and Geisler, Wilson S and Priebe, Nicholas J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/4EF56587-0DC4-48B0-BCE0-3D5236FC876E.pdf:pdf},
journal = {Nat. Neurosci.},
month = {oct},
number = {11},
pages = {1--14},
title = {{Natural image and receptive field statistics predict saccade sizes}},
volume = {21},
year = {2018}
}
@article{Shen2019,
author = {Shen, Guohua and Horikawa, Tomoyasu and Majima, Kei and Kamitani, Yukiyasu},
editor = {O'Reilly, Jill},
file = {:Users/hahosoya/Documents/Mendeley Desktop/BF38EA13-2E20-4EE2-AAAE-E09A62AC1C07.pdf:pdf},
journal = {PLoS Comput. Biol.},
month = {jan},
number = {1},
pages = {e1006633--23},
title = {{Deep image reconstruction from human brain activity}},
volume = {15},
year = {2019}
}
@article{Smyth2008,
abstract = {Distributed learning is a problem of fundamental interest in machine learning and cognitive 
science. In this paper, we present asynchronous distributed learning al- gorithms for two
well-known unsupervised learning frameworks: Latent Dirichlet Allocation (LDA) and  ...},
author = {Smyth, P and Welling, M},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Advances in neural information {\ldots} 2008 Smyth.pdf:pdf},
journal = {Adv. Neural Inf. {\ldots}},
month = {jan},
title = {{Asynchronous distributed learning of topic models}},
year = {2008}
}
@article{Zhivago2016,
author = {Zhivago, Kalathupiriyan A and Arun, S P},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neurophysiology 2016 Zhivago.pdf:pdf},
journal = {J. Neurophysiol.},
month = {mar},
number = {3},
pages = {1512--1520},
title = {{Selective IT neurons are selective along many dimensions}},
volume = {115},
year = {2016}
}
@article{Bastos2012,
abstract = {Neuron, 76 (2012) 695-711. doi:10.1016/j.neuron.2012.10.038},
author = {Bastos, Andre M and Usrey, W Martin and Adams, Rick A and Mangun, George R and Fries, Pascal and Friston, Karl J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neuron 2012 Bastos-2.pdf:pdf},
journal = {Neuron},
month = {nov},
number = {4},
pages = {695--711},
title = {{Canonical Microcircuits for Predictive Coding}},
volume = {76},
year = {2012}
}
@article{Liang2017,
abstract = {Perceptual grouping of line segments into object contours has been thought to be mediated, in part, by long-range horizontal connectivity intrinsic to the primary visual cortex (V1), with a contribution by top-down feedback projections. To dissect the contributions of intraareal and interareal connections during contour integration, we applied conditional Granger causality analysis to assess directional influences among neural signals simultaneously recorded from visual cortical areas V1 and V4 of monkeys performing a contour detection task. Our results showed that discounting the influences from V4 markedly reduced V1 lateral interactions, indicating dependence on feedback signals of the effective connectivity within V1. On the other hand, the feedback influences were reciprocally dependent on V1 lateral interactions because the modulation strengths from V4 to V1 were greatly reduced after discounting the influences from other V1 neurons. Our findings suggest that feedback and lateral connections closely interact to mediate image grouping and segmentation.},
author = {Liang, Hualou and Gong, Xiajing and Chen, Minggui and Yan, Yin and Li, Wu and Gilbert, Charles D.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/8637.full.pdf:pdf},
journal = {Proc. Natl. Acad. Sci. U. S. A.},
keywords = {Contour integration,Feedback connection,Granger causality,Horizontal connection,Perceptual grouping},
number = {32},
pages = {8637--8642},
title = {{Interactions between feedback and lateral connections in the primary visual cortex}},
volume = {114},
year = {2017}
}
@article{Gray1989,
abstract = {A fundamental step in visual pattern recognition is the establishment of relations between spatially separate features. Recently, we have shown that neurons in the cat visual cortex have oscillatory responses in the range 40-60 Hz (refs 1, 2) which occur in synchrony for cells in a functional column and are tightly correlated with a local oscillatory field potential. This led us to hypothesize that the synchronization of oscillatory responses of spatially distributed, feature selective cells might be a way to establish relations between features in different parts of the visual field. In support of this hypothesis, we demonstrate here that neurons in spatially separate columns can synchronize their oscillatory responses. The synchronization has, on average, no phase difference, depends on the spatial separation and the orientation preference of the cells and is influenced by global stimulus properties.},
address = {Max-Planck-Institute for Brain Research, Frankfurt am Main, FRG.},
author = {Gray, C M and K{\"{o}}nig, P and Engel, A K and Singer, W},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature 1989 Gray.pdf:pdf},
journal = {Nature},
month = {mar},
number = {6213},
pages = {334--337},
title = {{Oscillatory responses in cat visual cortex exhibit inter-columnar synchronization which reflects global stimulus properties.}},
volume = {338},
year = {1989}
}
@article{Hill2019,
abstract = {The question of whether deep neural networks are good at generalising beyond their immediate training experience is of critical importance for learning-based approaches to AI. Here, we consider tests of out-of-sample generalisation that require an agent to respond to never-seen-before instructions by manipulating and positioning objects in a 3D Unity simulated room. We first describe a comparatively generic agent architecture that exhibits strong performance on these tests. We then identify three aspects of the training regime and environment that make a significant difference to its performance: (a) the number of object/word experiences in the training set; (b) the visual invariances afforded by the agent's perspective, or frame of reference; and (c) the variety of visual input inherent in the perceptual aspect of the agent's perception. Our findings indicate that the degree of generalisation that networks exhibit can depend critically on particulars of the environment in which a given task is instantiated. They further suggest that the propensity for neural networks to generalise in systematic ways may increase if, like human children, those networks have access to many frames of richly varying, multi-modal observations as they learn.},
archivePrefix = {arXiv},
arxivId = {1910.00571},
author = {Hill, Felix and Lampinen, Andrew and Schneider, Rosalia and Clark, Stephen and Botvinick, Matthew and McClelland, James L. and Santoro, Adam},
eprint = {1910.00571},
file = {:Users/hahosoya/Documents/Mendeley Desktop/de0ff5cf9114c0cf6c9506ebe9120bf3bf5e1441.pdf:pdf},
pages = {1--15},
title = {{Environmental drivers of systematicity and generalization in a situated agent}},
year = {2019}
}
@article{Manita2015,
abstract = {Neuron, 86 (2015) 1304-1316. doi:10.1016/j.neuron.2015.05.006},
author = {Manita, Satoshi and Suzuki, Takayuki and Homma, Chihiro and Matsumoto, Takashi and Odagawa, Maya and Yamada, Kazuyuki and Ota, Keisuke and Matsubara, Chie and Inutsuka, Ayumu and Sato, Masaaki and Ohkura, Masamichi and Yamanaka, Akihiro and Yanagawa, Yuchio and Nakai, Junichi and Hayashi, Yasunori and Larkum, Matthew E and Murayama, Masanori},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neuron 2015 Manita.pdf:pdf},
journal = {Neuron},
month = {jun},
number = {5},
pages = {1304--1316},
title = {{A Top-Down Cortical Circuit for Accurate Sensory Perception}},
volume = {86},
year = {2015}
}
@article{Cheng2011,
abstract = {(a) Layout of the prototype microscope. Laser pulses are emitted with a 12-ns period from a commercial 
ultrafast Ti:Al 2 0 3 laser. The beam is divided into four beams, which are delayed by 3 ns each
(1 m per 3 ns) and converged on the slow-axis scan mirror aperture, which is then  ...},
author = {Cheng, A and Gon{\c{c}}alves, J T and Golshani, P and et Al},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature Methods 2011 Cheng.pdf:pdf},
journal = {Nat. Methods},
month = {jan},
title = {{Simultaneous two-photon calcium imaging at different depths with spatiotemporal multiplexing}},
year = {2011}
}
@article{Tanaka2014,
abstract = {Neuron, 84 (2014) 347-354. doi:10.1016/j.neuron.2014.09.037},
author = {Tanaka, Kazumasa Z and Pevzner, Aleksandr and Hamidi, Anahita B and Nakazawa, Yuki and Graham, Jalina and Wiltgen, Brian J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neuron 2014 Tanaka.pdf:pdf},
journal = {Neuron},
month = {oct},
number = {2},
pages = {347--354},
title = {{Cortical Representations Are Reinstated by the Hippocampus during Memory Retrieval}},
volume = {84},
year = {2014}
}
@article{Satoa,
author = {Sato, Masaaki},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Sato.pdf:pdf},
title = {{Hand-written note on multinomial variational Bayes}},
}
@article{Sussillo2015,
author = {Sussillo, David and Churchland, Mark M and Kaufman, Matthew T and Shenoy, Krishna V},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature Publishing Group 2015 Sussillo.pdf:pdf},
journal = {Nat. Neurosci.},
month = {jun},
number = {7},
pages = {1025--1033},
title = {{A neural network that finds a naturalistic solution for the production of muscle activity}},
volume = {18},
year = {2015}
}
@article{Wang2007,
abstract = {As a dimension reduction algorithm, canonical correlation analysis (CCA) encounters the issue of selecting the number of canonical correlations. In this letter, we present a Bayesian model selection algorithm for CCA based on a probabilistic interpretation. A hierarchical Bayesian model is applied to probabilistic CCA and learned by variational approximation. This method not only estimates the model parameters, but also automatically determines the number of canonical correlations and avoids overfitting. Experiments show that it performs better compared with maximum likelihood and some other model selection methods.},
author = {Wang, Chong},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2010 Viinikanoja.pdf:pdf},
journal = {IEEE Trans. Neural Networks},
month = {may},
number = {3},
pages = {905--910},
title = {{Variational Bayesian Mixture of Robust CCA Models}},
volume = {18},
year = {2007}
}
@article{Loaiza-Ganem2019a,
abstract = {Variational autoencoders (VAE) have quickly become a central tool in machine learning, applicable to a broad range of data types and latent variable models. By far the most common first step, taken by seminal papers and by core software libraries alike, is to model MNIST data using a deep network parameterizing a Bernoulli likelihood. This practice contains what appears to be and what is often set aside as a minor inconvenience: the pixel data is [0,1] valued, not {0,1} as supported by the Bernoulli likelihood. Here we show that, far from being a triviality or nuisance that is convenient to ignore, this error has profound importance to VAE, both qualitative and quantitative. We introduce and fully characterize a new [0,1]-supported, single parameter distribution: the continuous Bernoulli, which patches this pervasive bug in VAE. This distribution is not nitpicking; it produces meaningful performance improvements across a range of metrics and datasets, including sharper image samples, and suggests a broader class of performant VAE.},
archivePrefix = {arXiv},
arxivId = {1907.06845},
author = {Loaiza-Ganem, Gabriel and Cunningham, John P.},
eprint = {1907.06845},
file = {:Users/hahosoya/Documents/Mendeley Desktop/cf5f1bdd79ca8495a680c09808a884bccf67abc2.pdf:pdf;:Users/hahosoya/Documents/Mendeley Desktop/cont_bern_appendix.pdf:pdf},
number = {NeurIPS},
pages = {1--11},
title = {{The continuous Bernoulli: fixing a pervasive error in variational autoencoders}},
year = {2019}
}
@article{Baruni2015,
abstract = {Nature Neuroscience 18, 1656 (2015). doi:10.1038/nn.4141},
author = {Baruni, Jalal K and Lau, Brian and Salzman, C Daniel},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature Publishing Group 2015 Baruni.pdf:pdf},
journal = {Nat. Neurosci.},
month = {oct},
number = {11},
pages = {1656--1663},
title = {{Reward expectation differentially modulates attentional behavior and activity in visual area V4}},
volume = {18},
year = {2015}
}
@article{Beck2011,
abstract = {A simple expression for a lower bound of Fisher information is derived for a network of recurrently connected spiking neurons that have been driven to a noise-perturbed steady state. We call this lower bound linear Fisher information, as it corresponds to the Fisher information that can be recovered by a locally optimal linear estimator. Unlike recent similar calculations, the approach used here includes the effects of nonlinear gain functions and correlated input noise and yields a surprisingly simple and intuitive expression that offers substantial insight into the sources of information degradation across successive layers of a neural network. Here, this expression is used to (1) compute the optimal (i.e., information-maximizing) firing rate of a neuron, (2) demonstrate why sharpening tuning curves by either thresholding or the action of recurrent connectivity is generally a bad idea, (3) show how a single cortical expansion is sufficient to instantiate a redundant population code that can propagate across multiple cortical layers with minimal information loss, and (4) show that optimal recurrent connectivity strongly depends on the covariance structure of the inputs to the network.},
address = {Gatsby Computational Neuroscience Unit, University College London, London, WC1N3AR, UK. jbeck@bcs.rochester.edu},
author = {Beck, Jeffrey and Bejjanki, Vikranth R and Pouget, Alexandre},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neural Computation 2011 Beck.pdf:pdf},
journal = {Neural Comput.},
month = {jun},
number = {6},
pages = {1484--1502},
title = {{Insights from a simple expression for linear fisher information in a recurrently connected population of spiking neurons.}},
volume = {23},
year = {2011}
}
@article{Kim2014,
abstract = {Nature Neuroscience 18, 129 (2014). doi:10.1038/nn.3889},
author = {Kim, HyungGoo R and Angelaki, Dora E and DeAngelis, Gregory C},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature Publishing Group 2014 Kim.pdf:pdf},
journal = {Nat. Neurosci.},
month = {dec},
number = {1},
pages = {129--137},
title = {{A novel role for visual perspective cues in the neural computation of depth}},
volume = {18},
year = {2014}
}
@article{Martinez2001,
abstract = {In primary visual cortex, neurons are classified into simple cells and complex cells based on their response properties. Although the role of these two cell types in vision is still unknown, an attractive hypothesis is that simple cells are necessary to construct complex receptive fields. This hierarchical model puts forward two main predictions. First, simple cells should connect monosynaptically to complex cells. Second, complex cells should become silent when simple cells are inactivated. We have recently provided evidence for the first prediction, and here we do the same for the second. In summary, our results suggest that the receptive fields of most layer 2+3 complex cells are generated by a mechanism that requires simple cell inputs.},
address = {Laboratory of Neurobiology, The Rockefeller University, New York, NY 10021, USA.},
author = {Martinez, L M and Alonso, J M},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neuron 2001 Martinez.pdf:pdf},
journal = {Neuron},
month = {nov},
number = {3},
pages = {515--525},
title = {{Construction of complex receptive fields in cat primary visual cortex.}},
volume = {32},
year = {2001}
}
@article{Koster2010,
abstract = {We consider a hierarchical two-layer model of natural signals in which both layers are learned from the data. Estimation is accomplished by score matching, a recently proposed estimation principle for energy-based models. If the first-layer outputs are squared and the second-layer weights are constrained to be nonnegative, the model learns responses similar to complex cells in primary visual cortex from natural images. The second layer pools a small number of features with similar orientation and frequency, but differing in spatial phase. For speech data, we obtain analogous results. The model unifies previous extensions to independent component analysis such as subspace and topographic models and provides new evidence that localized, oriented, phase-invariant features reflect the statistical properties of natural image patches.},
address = {Department of Computer Science and Helsinki Institute for Information Technology, University of Helsinki, Helsinki, Finland. urs.koster@cs.helsinki.fi},
author = {K{\"{o}}ster, Urs and Hyv{\"{a}}rinen, Aapo},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neural Computation 2010 K{\"{o}}ster.pdf:pdf},
journal = {Neural Comput.},
month = {sep},
number = {9},
pages = {2308--2333},
title = {{A two-layer model of natural stimuli estimated with score matching.}},
volume = {22},
year = {2010}
}
@article{Goebl2005,
abstract = {This study investigated the temporal behavior of grand piano actions from different manufacturers under different touch conditions and dynamic levels. An experimental setup consisting of accelerometers and a calibrated microphone was used to capture key and hammer movements, as well as the sound signal. Five selected keys were played by pianists with two types of touch ("pressed touch" versus "struck touch") over the entire dynamic range. Discrete measurements were extracted from the accelerometer data for each of the over 2300 recorded tones (e.g., finger-key, hammer-string, and key bottom contact times, maximum hammer velocity). Travel times of the hammer (from finger-key to hammer-string) as a function of maximum hammer velocity varied clearly between the two types of touch, but only slightly between pianos. A travel time approximation used in earlier work [Goebl W., (2001). J. Acoust. Soc. Am. 110, 563-572] derived from a computer-controlled piano was verified. Constant temporal behavior over type of touch and low compression properties of the parts of the action (reflected in key bottom contact times) were hypothesized to be indicators for instrumental quality.},
address = {Austrian Research Institute for Artificial Intelligence (OFAI), Freyung 6/6, 1010 Vienna, Austria.},
author = {Goebl, Werner and Bresin, Roberto and Galembo, Alexander},
file = {:Users/hahosoya/Documents/Mendeley Desktop/J. Acoust. Soc. Am. 2005 Goebl.pdf:pdf},
journal = {J. Acoust. Soc. Am.},
month = {aug},
number = {2},
pages = {1154--1165},
title = {{Touch and temporal behavior of grand piano actions.}},
volume = {118},
year = {2005}
}
@article{El-Shamayleh2016,
author = {El-Shamayleh, Y and Pasupathy, A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2016 El-Shamayleh.pdf:pdf},
journal = {J. Neurosci.},
month = {may},
number = {20},
pages = {5532--5543},
title = {{Contour Curvature As an Invariant Code for Objects in Visual Area V4}},
volume = {36},
year = {2016}
}
@article{Keller2020,
abstract = {Animals sense the environment through pathways that link sensory organs to the brain. In the visual system, these feedforward pathways define the classical feedforward receptive field (ffRF), the area in space in which visual stimuli excite a neuron1. The visual system also uses visual context—the visual scene surrounding a stimulus—to predict the content of the stimulus2, and accordingly, neurons have been identified that are excited by stimuli outside their ffRF3–8. However, the mechanisms that generate excitation to stimuli outside the ffRF are unclear. Here we show that feedback projections onto excitatory neurons in the mouse primary visual cortex generate a second receptive field that is driven by stimuli outside the ffRF. The stimulation of this feedback receptive field (fbRF) elicits responses that are slower and are delayed in comparison with those resulting from the stimulation of the ffRF. These responses are preferentially reduced by anaesthesia and by silencing higher visual areas. Feedback inputs from higher visual areas have scattered receptive fields relative to their putative targets in the primary visual cortex, which enables the generation of the fbRF. Neurons with fbRFs are located in cortical layers that receive strong feedback projections and are absent in the main input layer, which is consistent with a laminar processing hierarchy. The observation that large, uniform stimuli—which cover both the fbRF and the ffRF—suppress these responses indicates that the fbRF and the ffRF are mutually antagonistic. Whereas somatostatin-expressing inhibitory neurons are driven by these large stimuli, inhibitory neurons that express parvalbumin and vasoactive intestinal peptide have mutually antagonistic fbRF and ffRF, similar to excitatory neurons. Feedback projections may therefore enable neurons to use context to estimate information that is missing from the ffRF and to report differences in stimulus features across visual space, regardless of whether excitation occurs inside or outside the ffRF. By complementing the ffRF, the fbRF that we identify here could contribute to predictive processing.},
author = {Keller, Andreas J. and Roth, Morgane M. and Scanziani, Massimo},
file = {:Users/hahosoya/Documents/Mendeley Desktop/s41586-020-2319-4.pdf:pdf},
journal = {Nature},
number = {7813},
pages = {545--549},
pmid = {32499655},
title = {{Feedback generates a second receptive field in neurons of the visual cortex}},
volume = {582},
year = {2020}
}
@article{Horikawa2017,
abstract = {Nature Communications 8, (2017). doi:10.1038/ncomms15037},
author = {Horikawa, Tomoyasu and Kamitani, Yukiyasu},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature Communications 2017 Horikawa.pdf:pdf;:Users/hahosoya/Documents/Mendeley Desktop/41467_2017_BFncomms15037_MOESM503_ESM.pdf:pdf},
journal = {Nat. Commun.},
month = {may},
pages = {1--15},
title = {{Generic decoding of seen and imagined objects using hierarchical visual features}},
volume = {8},
year = {2017}
}
@article{Al-Tahan2021,
abstract = {While vision evokes a dense network of feedforward and feedback neural processes in the brain, visual processes are primarily modeled with feedforward hierarchical neural networks, leaving the computational role of feedback processes poorly understood. Here, we developed a generative autoencoder neural network model and adversarially trained it on a categorically diverse data set of images. We hypothesized that the feedback processes in the ventral visual pathway can be represented by reconstruction of the visual information performed by the generative model. We compared representational similarity of the activity patterns in the proposed model with temporal (magnetoencephalography) and spatial (functional magnetic resonance imaging) visual brain responses. The proposed generative model identified two segregated neural dynamics in the visual brain. A temporal hierarchy of processes transforming low level visual information into high level semantics in the feedforward sweep, and a temporally later dynamics of inverse processes reconstructing low level visual information from a high level latent representation in the feedback sweep. Our results append to previous studies on neural feedback processes by presenting a new insight into the algorithmic function and the information carried by the feedback processes in the ventral visual pathway.},
author = {Al-Tahan, Haider and Mohsenzadeh, Yalda},
file = {:Users/hahosoya/Documents/Mendeley Desktop/journal.pcbi.1008775.pdf:pdf},
isbn = {1111111111},
journal = {PLOS Comput. Biol.},
number = {3},
pages = {e1008775},
title = {{Reconstructing feedback representations in the ventral visual pathway with a generative adversarial autoencoder}},
volume = {17},
year = {2021}
}
@article{Howe2005,
abstract = {Abstract Visual stimuli that entail the intersection of two or more straight lines elicit a variety of well known perceptual anomalies. Preeminent among these anomalies are the systematic overestimation of acute angles, the underestimation of obtuse angles, and the  ...},
author = {Howe, C Q and Purves, D},
file = {:Users/hahosoya/Documents/Mendeley Desktop/{\ldots} of the National Academy of Sciences {\ldots} 2005 Howe.pdf:pdf},
journal = {{\ldots}  Natl. Acad. Sci.  {\ldots}},
month = {jan},
title = {{Natural-scene geometry predicts the perception of angles and line orientation}},
year = {2005}
}
@article{Rezende2015,
abstract = {Abstract: The choice of approximate posterior distribution is one of the core problems in variational inference. Most applications of variational inference employ simple families of posterior approximations in order to allow for efficient inference, focusing on mean-field or other simple structured approximations. This restriction has a significant impact on the quality of inferences made using variational methods. We introduce a new approach for ...
},
author = {Rezende, D J and Mohamed, S},
file = {:Users/hahosoya/Documents/Mendeley Desktop/arXiv 2015 Rezende.pdf:pdf},
journal = {arXiv.org},
month = {jan},
title = {{Variational inference with normalizing flows}},
year = {2015}
}
@article{Olshausen2009,
abstract = {We describe a hierarchical, probabilistic model that learns to extract complex mo- tion from movies of the natural environment. The model consists of two hidden layers: the first layer produces a sparse representation of the image that is ex- pressed in terms of local amplitude and ...},
author = {Olshausen, B},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Advances in neural information {\ldots} 2009 Olshausen.pdf:pdf},
journal = {Adv. Neural Inf. {\ldots}},
month = {jan},
title = {{Learning transformational invariants from natural movies}},
year = {2009}
}
@article{Oizumi2016,
author = {Oizumi, Masafumi and Amari, Shun-ichi and Yanagawa, Toru and Fujii, Naotaka and Tsuchiya, Naotsugu},
editor = {Polani, Daniel},
file = {:Users/hahosoya/Documents/Mendeley Desktop/PLoS Comp Biol 2016 Oizumi.pdf:pdf},
journal = {PLoS Comput. Biol.},
month = {jan},
number = {1},
pages = {e1004654},
title = {{Measuring Integrated Information from the Decoding Perspective}},
volume = {12},
year = {2016}
}
@article{Perrett1991,
abstract = {An investigation was made into the sensitivity of cells in the macaque superior temporal sulcus (STS) to the sight of different perspective views of the head. This allowed assessment of (a) whether coding was 'viewer-centred' (view specific) or 'object-centred' (view invariant) and (b) whether viewer-centred cells were preferentially tuned to 'characteristic' views of the head. The majority of cells (110) were found to be viewer-centred and exhibited unimodal tuning to one view. 5 cells displayed object-centred coding responding equally to all views of the head. A further 5 cells showed 'mixed' properties, responding to all views of the head but also discriminating between views. 6 out of 56 viewer and object-centred cells exhibited selectivity for face identity or species. Tuning to view varied in sharpness. For most (54/73) cells the angle of perspective rotation reducing response to half maximal was 45-70 degrees but for 19/73 it was greater than 90 degrees. More cells were optimally tuned to characteristic views of the head (the full face or profile) than to other views. Some cells were, however, found tuned to intermediate views throughout the full 360 degree range. This coding of many distinct head views may have a role in the analysis of social signals based on the interpretation of the direction of other individuals' attention.},
address = {Psychological Laboratory, University of St Andrews, Fife, UK.},
author = {Perrett, D I and Oram, M W and Harries, M H and Bevan, R and Hietanen, J K and Benson, P J and Thomas, S},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Exp Brain Res 1991 Perrett.pdf:pdf},
journal = {Exp. brain Res.},
month = {jan},
number = {1},
pages = {159--173},
title = {{Viewer-centred and object-centred coding of heads in the macaque temporal cortex.}},
volume = {86},
year = {1991}
}
@article{Goodfellow2009,
abstract = {For many pattern recognition tasks, the ideal input feature would be invariant to multiple confounding 
properties (such as illumination and viewing angle, in com- puter vision applications).
Recently, deep architectures trained in an unsupervised manner have been proposed as  ...},
author = {Goodfellow, I and Le, Q and Saxe, A and et Al},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Advances in neural {\ldots} 2009 Goodfellow.pdf:pdf},
journal = {Adv. neural  {\ldots}},
month = {jan},
title = {{Measuring invariances in deep networks}},
year = {2009}
}
@article{Phillips2015,
abstract = {A broad neuron-centric conception of contextual modulation is reviewed and re-assessed in the light of recent neurobiological studies of amplification, suppression, and synchronization. Behavioural and computational studies of perceptual and higher cognitive functions that depend on these processes are outlined, and evidence that those functions and their neuronal mechanisms are impaired in schizophrenia is summarized. Finally, we compare and assess the long-term biological functions of contextual modulation at the level of computational theory as formalized by the theories of coherent infomax and free energy reduction. We conclude that those theories, together with the many empirical findings reviewed, show how contextual modulation at the neuronal level enables the cortex to flexibly adapt the use of its knowledge to current circumstances by amplifying and grouping relevant activities and by suppressing irrelevant activities.},
author = {Phillips, W. A. and Clark, A. and Silverstein, S. M.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Phillips et al_2015_On the functions, mechanisms, and malfunctions of intracortical contextual.pdf:pdf},
journal = {Neurosci. Biobehav. Rev.},
keywords = {Amplification,Cognitive coordination,Contextual modulation,Gestalt grouping,Predictive processing,Suppression},
pages = {1--20},
title = {{On the functions, mechanisms, and malfunctions of intracortical contextual modulation}},
volume = {52},
year = {2015}
}
@article{Konkle2013,
author = {Konkle, T and Caramazza, A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2013 Konkle.pdf:pdf},
journal = {J. Neurosci.},
month = {jun},
number = {25},
pages = {10235--10242},
title = {{Tripartite Organization of the Ventral Stream by Animacy and Object Size}},
volume = {33},
year = {2013}
}
@article{Schwartz2012,
abstract = {Nature Neuroscience 15, 1572 (2012). doi:10.1038/nn.3225},
author = {Schwartz, Gregory W and Okawa, Haruhisa and Dunn, Felice A and Morgan, Josh L and Kerschensteiner, Daniel and Wong, Rachel O and Rieke, Fred},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature Publishing Group 2012 Schwartz.pdf:pdf},
journal = {Nat. Neurosci.},
month = {sep},
number = {11},
pages = {1572--1580},
title = {{The spatial structure of a nonlinear receptive field}},
volume = {15},
year = {2012}
}
@article{Freeman2005,
author = {Freeman, A W},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neurophysiology 2005 Freeman.pdf:pdf},
journal = {J. Neurophysiol.},
month = {dec},
number = {6},
pages = {4412--4420},
title = {{Multistage Model for Binocular Rivalry}},
volume = {94},
year = {2005}
}
@inproceedings{Jayaraman2015,
abstract = {Abstract Understanding how images of objects and scenes behave in response to specific ego-motions is a crucial aspect of proper visual development, yet existing visual learning methods are conspicuously disconnected from the physical source of their images. We ...
},
author = {Jayaraman, D and Grauman, K},
booktitle = {Proc. IEEE Int. {\ldots}},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proceedings of the IEEE International {\ldots} 2015 Jayaraman.pdf:pdf},
month = {jan},
title = {{Learning image representations tied to ego-motion}},
year = {2015}
}
@article{Ji2019,
abstract = {We present a novel clustering objective that learns a neural network classifier from scratch, given only unlabelled data samples. The model discovers clusters that accurately match semantic classes, achieving state-of-the-art results in eight unsupervised clustering benchmarks spanning image classification and segmentation. These include STL10, an unsupervised variant of ImageNet, and CIFAR10, where we significantly beat the accuracy of our closest competitors by 6.6 and 9.5 absolute percentage points respectively. The method is not specialised to computer vision and operates on any paired dataset samples; in our experiments we use random transforms to obtain a pair from each image. The trained network directly outputs semantic labels, rather than high dimensional representations that need external processing to be usable for semantic clustering. The objective is simply to maximise mutual information between the class assignments of each pair. It is easy to implement and rigorously grounded in information theory, meaning we effortlessly avoid degenerate solutions that other clustering methods are susceptible to. In addition to the fully unsupervised mode, we also test two semi-supervised settings. The first achieves 88.8% accuracy on STL10 classification, setting a new global state-of-the-art over all existing methods (whether supervised, semi-supervised or unsupervised). The second shows robustness to 90% reductions in label coverage, of relevance to applications that wish to make use of small amounts of labels. github.com/xu-ji/IIC.},
archivePrefix = {arXiv},
arxivId = {1807.06653},
author = {Ji, Xu and Vedaldi, Andrea and Henriques, Joao},
eprint = {1807.06653},
file = {:Users/hahosoya/Documents/Mendeley Desktop/1a7763b3ce6fefef886b4548f833cd2ea433d22f.pdf:pdf},
isbn = {9781728148038},
journal = {Proc. IEEE Int. Conf. Comput. Vis.},
pages = {9864--9873},
title = {{Invariant information clustering for unsupervised image classification and segmentation}},
year = {2019}
}
@article{Bell1997,
abstract = {It has previously been suggested that neurons with line and edge selectivities found in primary visual cortex of cats and monkeys form a sparse, distributed representation of natural scenes, and it has been reasoned that such responses should emerge from an unsupervised learning algorithm that attempts to find a factorial code of independent visual features. We show here that a new unsupervised learning algorithm based on information maximization, a nonlinear "infomax" network, when applied to an ensemble of natural scenes produces sets of visual filters that are localized and oriented. Some of these filters are Gabor-like and resemble those produced by the sparseness-maximization network. In addition, the outputs of these filters are as independent as possible, since this infomax network performs Independent Components Analysis or ICA, for sparse (super-gaussian) component distributions. We compare the resulting ICA filters and their associated basis functions, with other decorrelating filters produced by Principal Components Analysis (PCA) and zero-phase whitening filters (ZCA). The ICA filters have more sparsely distributed (kurtotic) outputs on natural scenes. They also resemble the receptive fields of simple cells in visual cortex, which suggests that these neurons form a natural, information-theoretic coordinate system for natural images.},
address = {Howard Hughes Medical Institute, Salt Institute, La Jolla, CA 92037, USA.},
author = {Bell, A J and Sejnowski, T J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Vision Research 1997 Bell.pdf:pdf},
journal = {Vision Res.},
month = {dec},
number = {23},
pages = {3327--3338},
title = {{The "independent components" of natural scenes are edge filters.}},
volume = {37},
year = {1997}
}
@article{Campana2016,
author = {Campana, F and Rebollo, I and Urai, A and Wyart, V and Tallon-Baudry, C},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2016 Campana.pdf:pdf},
journal = {J. Neurosci.},
month = {may},
number = {19},
pages = {5200--5213},
title = {{Conscious Vision Proceeds from Global to Local Content in Goal-Directed Tasks and Spontaneous Vision}},
volume = {36},
year = {2016}
}
@article{Bonin2016,
abstract = {Nature 532, 370 (2016). doi:10.1038/nature17192},
author = {Bonin, Vincent and Reed, Michael and Graham, Brett J and Hood, Greg and Glattfelder, Katie and Lee, Wei-Chung Allen and Reid, R Clay},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature 2016 Bonin.pdf:pdf},
journal = {Nature},
month = {apr},
number = {7599},
pages = {370--374},
title = {{Anatomy and function of an excitatory network in the visual cortex}},
volume = {532},
year = {2016}
}
@article{Elder2002,
abstract = {Although numerous studies have measured the strength of visual grouping cues for controlled 
psychophysical stimuli, little is known about the statistical utility of these various cues for natural
images. In this study, we conducted experiments in which human participants trace  ...},
author = {Elder, James H and Goldberg, Richard M},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Vision 2002 Elder.pdf:pdf},
journal = {J. Vis.},
month = {jan},
title = {{Ecological statistics of Gestalt laws for the perceptual organization of contours}},
year = {2002}
}
@article{Dean2005,
abstract = {Our current understanding of the primate cerebral cor- tex (neocortex) and in particular the posterior, sensory association cortex has matured to a point where it is possible to develop a family of graphical models that capture the structure, scale and power of the neocor- tex ...},
author = {Dean, T},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2005 Dean.pdf:pdf},
journal = {{\ldots} Natl. Conf. Artif. {\ldots}},
month = {jan},
title = {{A computational model of the cerebral cortex}},
year = {2005}
}
@article{Angelaki2009,
abstract = {The vestibular system helps maintain equilibrium and clear vision through reflexes, but it also contributes to spatial perception. In recent years, research in the vestibular field has expanded to higher-level processing involving the cortex. Vestibular contributions to spatial cognition have been difficult to study because the circuits involved are inherently multisensory. Computational methods and the application of Bayes theorem are used to form hypotheses about how information from different sensory modalities is combined together with expectations based on past experience in order to obtain optimal estimates of cognitive variables like current spatial orientation. To test these hypotheses, neuronal populations are being recorded during active tasks in which subjects make decisions based on vestibular and visual or somatosensory information. This review highlights what is currently known about the role of vestibular information in these processes, the computations necessary to obtain the appropriate signals, and the benefits that have emerged thus far.},
address = {Department of Neurobiology, Washington University School of Medicine, St. Louis, MO 63110, USA. angelaki@wustl.edu},
author = {Angelaki, Dora E and Klier, Eliana M and Snyder, Lawrence H},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neuron 2009 Angelaki.pdf:pdf},
journal = {Neuron},
month = {nov},
number = {4},
pages = {448--461},
title = {{A vestibular sensation: probabilistic approaches to spatial perception.}},
volume = {64},
year = {2009}
}
@article{Hardoon2010,
abstract = {...  Sparse  CCA (SCCA) minimises the number of features used in both the primal and dual projections while maximising the correlation between the two views. ... Subsequent to Hardoon and Shawe-Taylor (2007) an application of sparse  CCA has been proposed by Torres et al. ... 
},
author = {Hardoon, David R and Shawe-Taylor, John},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Mach Learn 2010 Hardoon.pdf:pdf},
journal = {Mach. Learn.},
month = {nov},
number = {3},
pages = {331--353},
title = {{Sparse canonical correlation analysis}},
volume = {83},
year = {2010}
}
@article{Vinyals2016,
abstract = {Learning from a few examples remains a key challenge in machine learning. Despite recent advances in important domains such as vision and language, the standard supervised deep learning paradigm does not offer a satisfactory solution for learning new concepts rapidly from little data. In this work, we employ ideas from metric learning based on deep neural features and from recent advances that augment neural networks with external memories. Our framework learns a network that maps a small labelled support set and an unlabelled example to its label, obviating the need for fine-tuning to adapt to new class types. We then define one-shot learning problems on vision (using Omniglot, ImageNet) and language tasks. Our algorithm improves one-shot accuracy on ImageNet from 87.6% to 93.2% and from 88.0% to 93.8% on Omniglot compared to competing approaches. We also demonstrate the usefulness of the same model on language modeling by introducing a one-shot task on the Penn Treebank.},
archivePrefix = {arXiv},
arxivId = {1606.04080},
author = {Vinyals, Oriol and Blundell, Charles and Lillicrap, Timothy and Kavukcuoglu, Koray and Wierstra, Daan},
eprint = {1606.04080},
file = {:Users/hahosoya/Documents/Mendeley Desktop/bf4e3c77c9db2a2ba6ca063ac6177cb0714bb1c5.pdf:pdf},
journal = {Adv. Neural Inf. Process. Syst.},
pages = {3637--3645},
title = {{Matching networks for one shot learning}},
year = {2016}
}
@article{Khan2018,
abstract = {How learning enhances neural representations for behaviorally relevant stimuli via activity changes of cortical cell types remains unclear. We simultaneously imaged responses of pyramidal cells (PYR) along with parvalbumin (PV), somatostatin (SOM), and vasoactive intestinal peptide (VIP) inhibitory interneurons in primary visual cortex while mice learned to discriminate visual patterns. Learning increased selectivity for task-relevant stimuli of PYR, PV and SOM subsets but not VIP cells. Strikingly, PV neurons became as selective as PYR cells, and their functional interactions reorganized, leading to the emergence of stimulus-selective PYR-PV ensembles. Conversely, SOM activity became strongly decorrelated from the network, and PYR-SOM coupling before learning predicted selectivity increases in individual PYR cells. Thus, learning differentially shapes the activity and interactions of multiple cell classes: while SOM inhibition may gate selectivity changes, PV interneurons become recruited into stimulus-specific ensembles and provide more selective inhibition as the network becomes better at discriminating behaviorally relevant stimuli.},
address = {Biozentrum, University of Basel, Basel, Switzerland. khan.adil@kcl.ac.uk.},
author = {Khan, Adil G and Poort, Jasper and Chadwick, Angus and Blot, Antonin and Sahani, Maneesh and Mrsic-Flogel, Thomas D and Hofer, Sonja B},
file = {:Users/hahosoya/Documents/Mendeley Desktop/1C63D567-2E45-46C7-A5A6-1BF635C63645.pdf:pdf},
journal = {Nat. Neurosci.},
month = {jun},
number = {6},
pages = {851--859},
title = {{Distinct learning-induced changes in stimulus selectivity and interactions of GABAergic interneuron classes in visual cortex.}},
volume = {21},
year = {2018}
}
@article{Lee2020,
abstract = {A salient characteristic of monkey inferior temporal (IT) cortex is the IT face processing network. Its hallmarks include: “face neurons” that respond more to faces than non-face objects, strong spatial clustering of those neurons in foci at each IT anatomical level (“face patches”), and the preferential interconnection of those foci. While some deep artificial neural networks (ANNs) are good predictors of IT neuronal responses, including face neurons, they do not explain those face network hallmarks. Here we ask if they might be explained with a simple, metabolically motivated addition to current ANN ventral stream models. Specifically, we designed and successfully trained topographic deep ANNs (TDANNs) to solve real-world visual recognition tasks (as in prior work), but, in addition, we also optimized each network to minimize a proxy for neuronal wiring length within its IT layers. We report that after this dual optimization, the model IT layers of TDANNs reproduce the hallmarks of the IT face network: the presence of face neurons, clusters of face neurons that quantitatively match those found in IT face patches, connectivity between those patches, and the emergence of face viewpoint invariance along the network hierarchy. We find that these phenomena emerge for a range of naturalistic experience, but not for highly unnatural training. Taken together, these results show that the IT face processing network could be a consequence of a basic hierarchical anatomy along the ventral stream, selection pressure on the visual system to accomplish general object categorization, and selection pressure to minimize axonal wiring length.

### Competing Interest Statement

The authors have declared no competing interest.},
author = {Lee, Hyodong and Margalit, Eshed and Jozwik, Kamila and Cohen, Michael and Kanwisher, Nancy and Yamins, Daniel and DiCarlo, James},
file = {:Users/hahosoya/Documents/Mendeley Desktop/63de8486123ef56cec0033e7e148fc4cc7812528.pdf:pdf},
pages = {1--16},
title = {{Topographic deep artificial neural networks reproduce the hallmarks of the primate inferior temporal cortex face processing network}},
year = {2020}
}
@article{Salakhutdinov2012a,
author = {Salakhutdinov, R and Hinton, G},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neural Computation 2012 Salakhutdinov.pdf:pdf},
journal = {Neural Comput.},
month = {jan},
number = {8},
pages = {1967--2006},
title = {{An efficient learning procedure for deep Boltzmann machines}},
volume = {24},
year = {2012}
}
@article{Touryan2005,
abstract = {... An alternative method, spike - triggered  covariance (STC), which computes the eigenvalues and eigenvectors of the second-order Wiener kernel, has been shown to be effective for analyzing the nonlinear RF properties in the fly visual system ([de Ruyter Van Steveninck and ...},
author = {Touryan, Jon and Felsen, Gidon and Dan, Yang},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neuron 2005 Touryan.pdf:pdf},
journal = {Neuron},
month = {mar},
number = {5},
pages = {781--791},
title = {{Spatial Structure of Complex Cell Receptive Fields Measured with Natural Images}},
volume = {45},
year = {2005}
}
@book{Doya2007,
abstract = {A Bayesian approach can contribute to an understanding of the brain on multiple levels, by giving normative predictions about how an ideal sensory system should combine prior knowledge and observation, by providing mechanistic interpretation of the dynamic {\ldots}
},
author = {Doya, K and Ishii, S and Pouget, A and Rao, R P N},
month = {jan},
title = {{No Title}},
year = {2007}
}
@article{Wang2010,
abstract = {Changes of ocular dominance in the visual cortex can be induced by visual manipulations during 
a critical period in early life. However, the role of critical period plasticity in normal development
is unknown. Here we show that at the onset of this time window, the preferred orientations  ...},
author = {Wang, B S and et Al},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neuron 2010 Wang.pdf:pdf},
journal = {Neuron},
month = {jan},
title = {{Critical period plasticity matches binocular orientation preference in the visual cortex}},
year = {2010}
}
@article{Sherrah2001,
abstract = {Real-time identity-independent estimation of head pose from prototype images is a perplexing task requiring pose-invariant face detection. The problem is exacerbated by changes in illumination, identity and facial position. We approach the problem using a ...
},
author = {Sherrah, J and Gong, S and Ong, E J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2001 Sherrah.pdf:pdf},
journal = {Image Vis. Comput.},
month = {jan},
title = {{Face distributions in similarity space under varying head pose}},
year = {2001}
}
@article{Guclu2014,
author = {G{\"{u}}{\c{c}}l{\"{u}}, Umut and van Gerven, Marcel A J},
editor = {Kriegeskorte, Nikolaus},
file = {:Users/hahosoya/Documents/Mendeley Desktop/PLoS Comp Biol 2014 G{\"{u}}{\c{c}}l{\"{u}}.pdf:pdf},
journal = {PLoS Comput. Biol.},
month = {aug},
number = {8},
pages = {e1003724},
title = {{Unsupervised Feature Learning Improves Prediction of Human Brain Activity in Response to Natural Images}},
volume = {10},
year = {2014}
}
@article{Fei-Fei2007,
abstract = {Current computational approaches to learning visual object categories require thousands of training images, are slow, cannot learn in an incremental manner and cannot incorporate prior information into the learning process. In addition, no algorithm presented in the ...
},
author = {Fei-Fei, L and Fergus, R and Perona, P},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2007 Fei-Fei.pdf:pdf},
journal = {Comput. Vis. Image Underst.},
month = {apr},
number = {1},
pages = {59--70},
title = {{Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories}},
volume = {106},
year = {2007}
}
@article{Buesing2011,
abstract = {The organization of computations in networks of spiking neurons in the brain is still largely unknown, in particular in view of the inherently stochastic features of their firing activity and the experimentally observed trial-to-trial variability of neural systems in the brain. In principle there exists a powerful computational framework for stochastic computations, probabilistic inference by sampling, which can explain a large number of macroscopic experimental data in neuroscience and cognitive science. But it has turned out to be surprisingly difficult to create a link between these abstract models for stochastic computations and more detailed models of the dynamics of networks of spiking neurons. Here we create such a link and show that under some conditions the stochastic firing activity of networks of spiking neurons can be interpreted as probabilistic inference via Markov chain Monte Carlo (MCMC) sampling. Since common methods for MCMC sampling in distributed systems, such as Gibbs sampling, are inconsistent with the dynamics of spiking neurons, we introduce a different approach based on non-reversible Markov chains that is able to reflect inherent temporal processes of spiking neuronal activity through a suitable choice of random variables. We propose a neural network model and show by a rigorous theoretical analysis that its neural activity implements MCMC sampling of a given distribution, both for the case of discrete and continuous time. This provides a step towards closing the gap between abstract functional models of cortical computation and more detailed models of networks of spiking neurons.},
address = {Institute for Theoretical Computer Science, Graz University of Technology, Graz, Austria.},
annote = {refactory period
postsynaptic potential},
author = {Buesing, Lars and Bill, Johannes and Nessler, Bernhard and Maass, Wolfgang},
file = {:Users/hahosoya/Documents/Mendeley Desktop/PLoS Comp Biol 2011 Buesing.pdf:pdf},
journal = {PLoS Comput. Biol.},
month = {nov},
number = {11},
pages = {e1002211},
title = {{Neural dynamics as sampling: a model for stochastic computation in recurrent networks of spiking neurons.}},
volume = {7},
year = {2011}
}
@article{Zhao2020,
abstract = {Deep clustering which adopts deep neural networks to obtain optimal representations for clustering has been widely studied recently. In this paper, we propose a novel deep image clustering framework to learn a category-style latent representation in which the category information is disentangled from image style and can be directly used as the cluster assignment. To achieve this goal, mutual information maximization is applied to embed relevant information in the latent representation. Moreover, augmentation-invariant loss is employed to disentangle the representation into category part and style part. Last but not least, a prior distribution is imposed on the latent representation to ensure the elements of the category vector can be used as the probabilities over clusters. Comprehensive experiments demonstrate that the proposed approach outperforms state-of-the-art methods significantly on five public datasets.},
archivePrefix = {arXiv},
arxivId = {2007.10004},
author = {Zhao, Junjie and Lu, Donghuan and Ma, Kai and Zhang, Yu and Zheng, Yefeng},
eprint = {2007.10004},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2007.10004.pdf:pdf},
keywords = {deep learning,image clustering,unsupervised learning},
pages = {1--22},
title = {{Deep Image Clustering with Category-Style Representation}},
year = {2020}
}
@article{Stollenga2014,
abstract = {gnuplot plot},
author = {Stollenga},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2014 stollenga.pdf:pdf},
month = {jul},
pages = {1--13},
title = {{Deep Networks with Internal Selective Attention through Feedback Connections}},
year = {2014}
}
@article{Mallat1993,
abstract = {Abstract The authors introduce an algorithm, called matching pursuit , that decomposes any signal into a linear expansion of waveforms that are selected from a redundant dictionary of functions. These waveforms are chosen in order to best match the signal structures.  ...},
author = {Mallat, S G and Zhang, Z},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Signal Processing 1993 Mallat.pdf:pdf},
journal = {Signal Process. {\ldots}},
month = {jan},
title = {{Matching pursuits with time-frequency dictionaries}},
year = {1993}
}
@article{Flack2019,
author = {Flack, Tessa R and Harris, Richard J and Young, Andrew W and Andrews, Timothy J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/CCA159BE-162D-47DE-A3B8-98CCF459B192.pdf:pdf},
journal = {J. Neurosci.},
month = {may},
number = {19},
pages = {3741--3751},
title = {{Symmetrical Viewpoint Representations in Face-Selective Regions Convey an Advantage in the Perception and Recognition of Faces}},
volume = {39},
year = {2019}
}
@book{JR,
author = {JR, L L OLIVEIRA and De inicia{\c{c}}{\~{a}}o cient{\'{i}}fica, C E THOMAZ Relat{\'{o}}rio and El{\'{e}}trica, Depto Eng and 2006},
file = {:Users/hahosoya/Documents/Mendeley Desktop/A3DBBF0D-C69A-4D87-9DA7-F67A9C9A5E90.pdf:pdf},
title = {{No Title}},
}
@article{Epstein2013,
abstract = {Neuron, 79 (2013) 615-617. doi:10.1016/j.neuron.2013.08.001},
author = {Epstein, Russell A and Julian, Joshua B},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neuron 2013 Epstein.pdf:pdf},
journal = {Neuron},
month = {aug},
number = {4},
pages = {615--617},
title = {{Scene Areas in Humans and Macaques}},
volume = {79},
year = {2013}
}
@article{Beyeler2016,
author = {Beyeler, M and Dutt, N and Krichmar, J L},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2016 Beyeler.pdf:pdf},
journal = {J. Neurosci.},
month = {aug},
number = {32},
pages = {8399--8415},
title = {{3D Visual Response Properties of MSTd Emerge from an Efficient, Sparse Population Code}},
volume = {36},
year = {2016}
}
@article{Kansky2017,
abstract = {The recent adaptation of deep neural network-based methods to reinforcement learning and planning domains has yielded remarkable progress on individual tasks. Nonetheless, progress on task-to-task transfer remains limited. In pursuit of efficient and robust generalization, we introduce the Schema Network, an object-oriented generative physics simulator capable of disentangling multiple causes of events and reasoning backward through causes to achieve goals. The richly structured architecture of the Schema Network can learn the dynamics of an environment directly from data. We compare Schema Networks with Asynchronous Advantage Actor-Critic and Progressive Networks on a suite of Breakout variations, reporting results on training efficiency and zero-shot generalization, consistently demonstrating faster, more robust learning and better transfer. We argue that generalizing from limited data and learning causal relationships are essential abilities on the path toward generally intelligent systems.},
archivePrefix = {arXiv},
arxivId = {arXiv:1706.04317v2},
author = {Kansky, Ken and Silver, Tom and M{\'{e}}ly, David A. and Eldawy, Mohamed and L{\'{a}}zaro-Gredilla, Miguel and Lou, Xinghua and Dorfman, Nimrod and Sidor, Szymon and Phoenix, Scott and George, Dileep},
eprint = {arXiv:1706.04317v2},
file = {:Users/hahosoya/Documents/Mendeley Desktop/f4d6d2d385c02023e6a2f893704eccd6d333b9cf.pdf:pdf},
isbn = {9781510855144},
journal = {34th Int. Conf. Mach. Learn. ICML 2017},
pages = {2879--2889},
title = {{Schema networks: Zero-shot transfer with a generative causal model of physics intuitive}},
volume = {4},
year = {2017}
}
@article{Coen-Cagli2019,
abstract = {Cortical responses to repeated presentations of a sensory stimulus are variable. This variability is sensitive to several stimulus dimensions, suggesting that it may carry useful information beyond the average firing rate. Many experimental manipulations that affect response variability are also known to engage divisive normalization, a widespread operation that describes neuronal activity as the ratio of a numerator (representing the excitatory stimulus drive) and denominator (the normalization signal). Although it has been suggested that normalization affects response variability, we lack a quantitative framework to determine the relation between the two. Here we extend the standard normalization model, by treating the numerator and the normalization signal as variable quantities. The resulting model predicts a general stabilizing effect of normalization on neuronal responses, and allows us to infer the single-trial normalization strength, a quantity that cannot be measured directly. We test the model on neuronal responses to stimuli of varying contrast, recorded in primary visual cortex of male macaques. We find that neurons that are more strongly normalized fire more reliably, and response variability and pairwise noise correlations are reduced during trials in which normalization is inferred to be strong. Our results thus suggest a novel functional role for normalization, namely, modulating response variability. Our framework could enable a direct quantification of the impact of single-trial normalization strength on the accuracy of perceptual judgments, and can be readily applied to other sensory and nonsensory factors.SIGNIFICANCE STATEMENT Divisive normalization is a widespread neural operation across sensory and nonsensory brain areas, which describes neuronal responses as the ratio between the excitatory drive to the neuron and a normalization signal. Normalization plays a key role in several important computations, including adjusting the neuron's dynamic range, reducing redundancy, and facilitating probabilistic inference. However, the relation between normalization and neuronal response variability (a fundamental aspect of neural coding) remains unclear. Here we develop a new model and test it on primary visual cortex responses. We show that normalization has a stabilizing effect on neuronal activity, beyond the known suppression of firing rate. This modulation of variability suggests a new functional role for normalization in neural coding and perception.},
author = {Coen-Cagli, Ruben and Solomon, Selina S.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/7344.full.pdf:pdf},
journal = {J. Neurosci.},
keywords = {divisive normalization,modeling,neuronal variability,single-trial inference,visual cortex},
number = {37},
pages = {7344--7356},
title = {{Relating Divisive Normalization to Neuronal Response Variability}},
volume = {39},
year = {2019}
}
@article{Sato,
author = {Sato, Masaaki},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Sato-2.pdf:pdf},
title = {{Hand-written note on Probit variational Bayes}},
}
@article{Le2011,
author = {Le, Quoc V and Karpenko, Alexandre and Ngiam, Jiquan and Ng, Andrew},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2011 Le-2.pdf:pdf},
journal = {Adv. Neural Inf. Process. Syst.},
month = {jan},
pages = {1017--1025},
title = {{Ica with reconstruction cost for efficient overcomplete feature learning}},
year = {2011}
}
@article{Yang2015,
abstract = {An important problem for both graphics and vision is to synthesize novel views of a 3D object from a single image. This is particularly challenging due to the partial observability inherent in projecting a 3D object onto the image space, and the ill-posedness of inferring object shape and pose. However, we can train a neural network to address the problem if we restrict our attention to specific object categories (in our case faces and chairs) for which we can gather ample training data. In this paper, we propose a novel recurrent convolutional encoder-decoder network that is trained end-to-end on the task of rendering rotated objects starting from a single image. The recurrent structure allows our model to capture long-term dependencies along a sequence of transformations. We demonstrate the quality of its predictions for human faces on the Multi-PIE dataset and for a dataset of 3D chair models, and also show its ability to disentangle latent factors of variation (e.g., identity and pose) without using full supervision.},
author = {Yang, Jimei and Reed, Scott and Yang, Ming-Hsuan and Lee, Honglak},
file = {:Users/hahosoya/Documents/Mendeley Desktop/3FBE6CCA-229E-4DC6-B9D6-3C13D05D01D1.pdf:pdf},
journal = {Adv. Neural Inf. Process. Syst.},
month = {jan},
title = {{Weakly-supervised Disentangling with Recurrent Transformations for 3D View Synthesis}},
year = {2015}
}
@article{Luo2015,
abstract = {Neuron, 86 (2015) 1182-1188. doi:10.1016/j.neuron.2015.05.007},
author = {Luo, Thomas Zhihao and Maunsell, John H R},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neuron 2015 Luo.pdf:pdf},
journal = {Neuron},
month = {jun},
number = {5},
pages = {1182--1188},
title = {{Neuronal Modulations in Visual Cortex Are Associated with Only One of Multiple Components of Attention}},
volume = {86},
year = {2015}
}
@article{Komatsu2000,
abstract = {When visual stimuli that cover the entire blind spot are presented monocularly, the color and brightness of the surrounding field are seen within the blind spot, although it receives no retinal input. Important questions about such perceptual filling-in are whether neurons in the ...},
author = {Komatsu, H and Kinoshita, M},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2000 Komatsu.pdf:pdf},
journal = {J. Neurosci.},
month = {jan},
title = {{Neural Responses in the Retinotopic Representation of the Blind Spot in the Macaque V1 to Stimuli for Perceptual Filling-In}},
year = {2000}
}
@article{Yamane2006,
abstract = {We investigated object representation in area TE, the anterior part of monkey inferotemporal (IT) cortex, with a combination of optical and extracellular recordings in anesthetized monkeys. We found neurons that respond to visual stimuli composed of naturally distinguishable parts. These neurons were sensitive to a particular spatial arrangement of parts but less sensitive to differences in local features within individual parts. Thus these neurons were activated when arbitrary local features were arranged in a particular spatial configuration, suggesting that they may be responsible for representing the spatial configuration of object images. Previously it has been reported that many neurons in area TE respond to visual features less complex than natural objects, but it has remained unclear whether these features are related to local features of object images or to more global features. These results indicate that TE neurons represent not only local features but also global features such as the spatial relationship among object parts.},
address = {Laboratory for Integrative Neural Systems, RIKEN Brain Science Institute, 2-1 Hirosawa, Wako-shi, Saitama 351-0198, Japan.},
author = {Yamane, Yukako and Tsunoda, Kazushige and Matsumoto, Madoka and Phillips, Adam N and Tanifuji, Manabu},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neurophysiology 2006 Yamane.pdf:pdf},
journal = {J. Neurophysiol.},
month = {dec},
number = {6},
pages = {3147--3156},
title = {{Representation of the spatial relationship among object parts by neurons in macaque inferotemporal cortex.}},
volume = {96},
year = {2006}
}
@article{Ito2010,
author = {Ito, Minami and Goda, Naokazu},
file = {:Users/hahosoya/Documents/Mendeley Desktop/European Journal of Neuroscience 2010 Ito.pdf:pdf},
journal = {Eur. J. Neurosci.},
month = {nov},
number = {1},
pages = {130--142},
title = {{Mechanisms underlying the representation of angles embedded within contour stimuli in area V2 of macaque monkeys}},
volume = {33},
year = {2010}
}
@article{Thomas2002,
author = {Thomas, Owen M and Cumming, Bruce G and Parker, Andrew J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature neuroscience 2002 Thomas.pdf:pdf},
journal = {Nat. Neurosci.},
month = {apr},
number = {5},
pages = {472--478},
title = {{A specialization for relative disparity in V2}},
volume = {5},
year = {2002}
}
@article{Kar2019,
abstract = {Non-recurrent deep convolutional neural networks (CNNs) are currently the best at modeling core object recognition, a behavior that is supported by the densely recurrent primate ventral stream, culminating in the inferior temporal (IT) cortex. If recurrence is critical to this behavior, then primates should outperform feedforward-only deep CNNs for images that require additional recurrent processing beyond the feedforward IT response. Here we first used behavioral methods to discover hundreds of these 'challenge' images. Second, using large-scale electrophysiology, we observed that behaviorally sufficient object identity solutions emerged $\sim$30 ms later in the IT cortex for challenge images compared with primate performance-matched 'control' images. Third, these behaviorally critical late-phase IT response patterns were poorly predicted by feedforward deep CNN activations. Notably, very-deep CNNs and shallower recurrent CNNs better predicted these late IT responses, suggesting that there is a functional equivalence between additional nonlinear transformations and recurrence. Beyond arguing that recurrent circuits are critical for rapid object identification, our results provide strong constraints for future recurrent model development.},
address = {McGovern Institute for Brain Research and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA. kohitij@mit.edu.},
author = {Kar, Kohitij and Kubilius, Jonas and Schmidt, Kailyn and Issa, Elias B and DiCarlo, James J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/726A64D1-5442-483A-BF6C-35D8FDD02D9C.pdf:pdf},
journal = {Nat. Neurosci.},
month = {jun},
number = {6},
pages = {974--983},
title = {{Evidence that recurrent circuits are critical to the ventral stream's execution of core object recognition behavior.}},
volume = {22},
year = {2019}
}
@article{Ramalho2019,
abstract = {The ability to generalize quickly from few observations is crucial for intelligent systems. In this paper we introduce APL, an algorithm that approximates probability distributions by remembering the most surprising observations it has encountered. These past observations are recalled from an external memory module and processed by a decoder network that can combine information from different memory slots to generalize beyond direct recall. We show this algorithm can perform as well as state of the art baselines on few-shot classification benchmarks with a smaller memory footprint. In addition, its memory compression allows it to scale to thousands of unknown labels. Finally, we introduce a meta-learning reasoning task which is more challenging than direct classification. In this setting, APL is able to generalize with fewer than one example per class via deductive reasoning.},
archivePrefix = {arXiv},
arxivId = {1902.02527},
author = {Ramalho, Tiago and Garnelo, Marta},
eprint = {1902.02527},
file = {:Users/hahosoya/Documents/Mendeley Desktop/8727268fce4498ccfe72d998594e8d7346a2d9e7.pdf:pdf},
journal = {arXiv},
pages = {1--14},
title = {{Adaptive posterior learning: few-shot learning with a surprise-based memory module}},
year = {2019}
}
@article{Afraz2015,
author = {Afraz, Arash and Boyden, Edward S and DiCarlo, James J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proceedings of the National Academy of Sciences 2015 Afraz.pdf:pdf},
journal = {Proc. Natl. Acad. Sci.},
month = {may},
number = {21},
pages = {6730--6735},
title = {{Optogenetic and pharmacological suppression of spatial clusters of face neurons reveal their causal role in face gender discrimination}},
volume = {112},
year = {2015}
}
@article{Morel2018,
abstract = {Morel, (2018). Gramm: grammar of graphics plotting in Matlab. Journal of Open Source Software, 3(23), 568, https://doi.org/10.21105/joss.00568},
author = {Morel, Pierre},
file = {:Users/hahosoya/Documents/Mendeley Desktop/E343AA87-B26B-459F-A50B-FBF9189A58E7.pdf:pdf},
journal = {J. Open Source Softw.},
month = {mar},
number = {23},
pages = {568},
title = {{Gramm: grammar of graphics plotting in Matlab}},
volume = {3},
year = {2018}
}
@inproceedings{Lee2009,
abstract = {Abstract There has been much interest in unsupervised learning of hierarchical generative models such as deep belief networks. Scaling such models to full-sized, high-dimensional images remains a difficult problem. To address this problem, we present the convolutional ...
},
author = {Lee, H and Grosse, R and Ranganath, R and Ng, A Y},
booktitle = {Proc. 26th Annu. {\ldots}},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2009 Lee.pdf:pdf},
month = {jan},
title = {{Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations}},
year = {2009}
}
@article{Miller1994,
abstract = {... 2) (other factors Place Fig. 2 about here can also play a role; see Miller and MacKay, 1994 ). ... 
This represents convergence of two competing populations onto the same postsynaptic cells ,
but segregation within receptive elds, as in cortical simple  cells . ...
},
author = {Miller, K D},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 1994 Miller.pdf:pdf},
journal = {J. Neurosci.},
month = {jan},
title = {{A model for the development of simple cell receptive fields and the ordered arrangement of orientation columns through activity-dependent competition between  {\ldots}}},
year = {1994}
}
@article{Saremi2013,
abstract = {The analysis of natural images with independent component analysis (ICA) yields localized bandpass Gabor-type filters similar to receptive fields of simple cells in visual cortex. We applied ICA on a subset of patches called position-centered patches, selected for forming a translation-invariant representation of small patches. The resulting filters were qualitatively different in two respects. One novel feature was the emergence of filters we call double-Gabor filters. In contrast to Gabor functions that are modulated in one direction, double-Gabor filters are sinusoidally modulated in two orthogonal directions. In addition the filters were more extended in space and frequency compared to standard ICA filters and better matched the distribution in experimental recordings from neurons in primary visual cortex. We further found a dual role for double-Gabor filters as edge and texture detectors, which could have engineering applications.},
author = {Saremi, Saeed and Sejnowski, Terrence J and Sharpee, Tatyana O},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neural Computation 2013 Saremi.pdf:pdf},
journal = {Neural Comput.},
month = {apr},
number = {4},
pages = {922--939},
title = {{Double-Gabor Filters Are Independent Components of Small Translation-Invariant Image Patches}},
volume = {25},
year = {2013}
}
@article{Shao1996,
abstract = {Abstract Different cortical areas are linked reciprocally via forward and feedback connections. Forward connections are involved in the representation of retinal images, whereas feedback pathways may play a role in the selection and interpretation of visual  ...},
author = {Shao, Zhengwei and Burkhalter, Andreas},
file = {:Users/hahosoya/Documents/Mendeley Desktop/The Journal of neuroscience 1996 Shao.pdf:pdf},
journal = {J. Neurosci.},
month = {jan},
title = {{Different balance of excitation and inhibition in forward and feedback circuits of rat visual cortex}},
year = {1996}
}
@article{Yamane2008,
author = {Yamane, Yukako and Carlson, Eric T and Bowman, Katherine C and Wang, Zhihong and Connor, Charles E},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature neuroscience 2008 Yamane-1.pdf:pdf},
journal = {Nat. Neurosci.},
month = {oct},
number = {11},
pages = {1352--1360},
title = {{A neural code for three-dimensional object shape in macaque inferotemporal cortex}},
volume = {11},
year = {2008}
}
@article{Ringach1997,
abstract = {A new discrete-time reverse - correlation scheme for the study of visual neurons is proposed. The visual stimulus is generated by drawing with uniform probability, at each refresh time, an image from a finite set S of orthonormal images. We show that if the neuron can be modeled as a ...},
author = {Ringach, D L and Sapiro, G},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Vision Research 1997 Ringach.pdf:pdf},
journal = {Vision Res.},
month = {jan},
title = {{A subspace reverse-correlation technique for the study of visual neurons}},
year = {1997}
}
@article{Theves2019,
abstract = {The hippocampal formation encodes maps of the physical environment [1–5]. A key question in neuroscience is whether its spatial coding principles also provide a universal metric for the organization of non-spatial information. Initial evidence comes from studies revealing directional modulation of fMRI responses in humans [6, 7] during navigation through abstract spaces and the involvement of place and grid cells in encoding of non-spatial feature dimensions [8]. However, a critical feature of a map-like representation is information about distances between locations, which has yet only been demonstrated for physical space [4, 9]. Here, we probe whether the hippocampus similarly encodes distances between points in an abstract space spanned by continuous stimulus-feature dimensions that were relevant to the acquisition of a novel concept. We find that, after learning, two-dimensional distances between individual positions in the abstract space were represented in the hippocampal multivoxel pattern as well as in the univariate hippocampal signal as indexed by fMRI adaptation. These results support the notion that the hippocampus computes domain-general, multidimensional cognitive maps along continuous dimensions.},
author = {Theves, Stephanie and Fernandez, Guill{\'{e}}n and Doeller, Christian F.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/b67a0de77bd476db25aa9bbdfa5f0e22f2b5a324.pdf:pdf},
journal = {Curr. Biol.},
keywords = {cognitive geometry,cognitive map,conceptual knowledge,spatial coding},
number = {7},
pages = {1226--1231.e3},
pmid = {30905602},
title = {{The Hippocampus Encodes Distances in Multidimensional Feature Space}},
volume = {29},
year = {2019}
}
@article{Reynolds2000,
abstract = {When attention is directed to a location in the visual field, sensitivity to stimuli at that location is increased. At the neuronal level, this could arise either through a multiplicative increase in firing rate or through an increase in the effective strength of the stimulus. To test conflicting predictions of these alternative models, we recorded responses of V4 neurons to stimuli across a range of luminance contrasts and measured the change in response when monkeys attended to them in order to discriminate a target stimulus from nontargets. Attention caused greater increases in response at low contrast than at high contrast, consistent with an increase in effective stimulus strength. On average, attention increased the effective contrast of the attended stimulus by a factor of 1.51, an increase of 51% of its physical contrast.},
address = {National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland 20892, USA.},
author = {Reynolds, J H and Pasternak, T and Desimone, R},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neuron 2000 Reynolds.pdf:pdf},
journal = {Neuron},
month = {jun},
number = {3},
pages = {703--714},
title = {{Attention increases sensitivity of V4 neurons.}},
volume = {26},
year = {2000}
}
@article{Young1987,
abstract = {A new facial composites technique is demonstrated, in which photographs of the top and bottom halves of different familiar faces fuse to form unfamiliar faces when aligned with each other. The perception of a novel configuration in such composite stimuli is sufficiently convincing to interfere with identification of the constituent parts (experiment 1), but this effect disappears when stimuli are inverted (experiment 2). Difficulty in identifying the parts of upright composites is found even for stimuli made from parts of unfamiliar faces that have only ever been encountered as face fragments (experiment 3). An equivalent effect is found for composites made from internal and external facial features of well-known people (experiment 4). These findings demonstrate the importance of configurational information in face perception, and that configurations are only properly perceived in upright faces.},
address = {Department of Psychology, University of Lancaster, Bailrigg, UK.},
author = {Young, A W and Hellawell, D and Hay, D C},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Perception 1987 Young.pdf:pdf},
journal = {Perception},
month = {jan},
number = {6},
pages = {747--759},
title = {{Configurational information in face perception.}},
volume = {16},
year = {1987}
}
@article{Verhoef2017,
author = {Verhoef, Bram-Ernst and Maunsell, John H R},
file = {:Users/hahosoya/Documents/Mendeley Desktop/8D76B2C8-C62D-418D-BA9C-2B38A1F01688.pdf:pdf},
journal = {Nat. Neurosci.},
month = {may},
number = {7},
pages = {969--977},
title = {{Attention-related changes in correlated neuronal activity arise from normalization mechanisms}},
volume = {20},
year = {2017}
}
@article{Sabour2017,
abstract = {A capsule is a group of neurons whose activity vector represents the instantiation parameters of a specific type of entity such as an object or an object part. We use the length of the activity vector to represent the probability that the entity exists and its orientation to represent the instantiation parameters. Active capsules at one level make predictions, via transformation matrices, for the instantiation parameters of higher-level capsules. When multiple predictions agree, a higher level capsule becomes active. We show that a discrimininatively trained, multi-layer capsule system achieves state-of-the-art performance on MNIST and is considerably better than a convolutional net at recognizing highly overlapping digits. To achieve these results we use an iterative routing-by-agreement mechanism: A lower-level capsule prefers to send its output to higher level capsules whose activity vectors have a big scalar product with the prediction coming from the lower-level capsule.},
author = {Sabour, Sara and Frosst, Nicholas and Hinton, Geoffrey E},
file = {:Users/hahosoya/Documents/Mendeley Desktop/6190D99D-D60E-40FE-B8EA-02BA62965C2A.pdf:pdf},
journal = {arXiv.org},
month = {oct},
title = {{Dynamic Routing Between Capsules}},
volume = {cs.CV},
year = {2017}
}
@article{Heeger2017,
author = {Heeger, David J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proceedings of the National Academy of Sciences 2017 Heeger.pdf:pdf},
journal = {Proc. Natl. Acad. Sci.},
month = {feb},
number = {8},
pages = {1773--1782},
title = {{Theory of cortical function}},
volume = {114},
year = {2017}
}
@article{Aghajan2014,
abstract = {Nature Neuroscience 18, 121 (2014). doi:10.1038/nn.3884},
author = {Aghajan, Zahra M and Acharya, Lavanya and Moore, Jason J and Cushman, Jesse D and Vuong, Cliff and Mehta, Mayank R},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature Publishing Group 2014 Aghajan.pdf:pdf},
journal = {Nat. Neurosci.},
month = {nov},
number = {1},
pages = {121--128},
title = {{Impaired spatial selectivity and intact phase precession in two-dimensional virtual reality}},
volume = {18},
year = {2014}
}
@article{Bouchacourt2017,
abstract = {We would like to learn a representation of the data which decomposes an observation into factors of variation which we can independently control. Specifically, we want to use minimal supervision to learn a latent representation that reflects the semantics behind a specific grouping of the data, where within a group the samples share a common factor of variation. For example, consider a collection of face images grouped by identity. We wish to anchor the semantics of the grouping into a relevant and disentangled representation that we can easily exploit. However, existing deep probabilistic models often assume that the observations are independent and identically distributed. We present the Multi-Level Variational Autoencoder (ML-VAE), a new deep probabilistic model for learning a disentangled representation of a set of grouped observations. The ML-VAE separates the latent representation into semantically meaningful parts by working both at the group level and the observation level, while retaining efficient test-time inference. Quantitative and qualitative evaluations show that the ML-VAE model (i) learns a semantically meaningful disentanglement of grouped data, (ii) enables manipulation of the latent representation, and (iii) generalises to unseen groups.},
author = {Bouchacourt, Diane and Tomioka, Ryota and Nowozin, Sebastian},
file = {:Users/hahosoya/Documents/Mendeley Desktop/0C565398-E0D6-4B2D-AFED-0E5C1E764DEF.pdf:pdf},
journal = {arXiv.org},
month = {may},
title = {{Multi-Level Variational Autoencoder: Learning Disentangled Representations from Grouped Observations}},
volume = {cs.LG},
year = {2017}
}
@article{Tononi2012,
abstract = {This article presents an updated account of integrated information theory of consciousness (liT) and some of its implications. /IT stems from thought experiments that lead to phenomenological axioms (existence, compositionality, information, integration, exclusion) and corresponding ontological postulates. The information axiom asserts that every experience is spec$\sim$fic - it is what it is by differing in its particular way from a large repertoire of alternatives. The integration axiom asserts that each experience is unified- it cannot be reduced to independent components. The exclusion axiom asserts that every experience is definite - it is limited to particular things and not others and flows at a particular speed and resolution. /IT formalizes these intuitions with postulates. The information postulate states that only "differences that make a difference" from the intrinsic perpective of a system matter: a mechanism generates cause-effect information if its present state has selective past causes and selective future effects within a system. The integration postulate states that only information that is irreducible matters: mechanisms generate integrated information only to the extent that the information they generate cannot be partitioned into that generated within independent components. The exclusion postulate states that only maxima of integrated information matter: a mechanism specifies only one maximally irreducible set of past causes and future effects - a concept. A complex is a set of elements specifying a maximally irreducible constellation of concepts, where the maximum is evaluated over elements and at the optimal spatiatemporal scale. Its concepts specify a maximally integrated conceptual information structure or quale, which is identical with an experience. Finally, changes in information integration upon exposure to the environment reflect a system's ability to match the causal structure of the world. After introducing an updated definition of information integration and related quantities, the article presents some theoretical considerations about the relationship between information and causation and about the relational structure of concepts within a qua/e. It also explores the relationship between the temporal grain size of information integration and the dynamic of metastable states in the corticothalamic complex. Finally, it summarizes how liT accounts for empirical findings about the neural substrate of consciousness, and how various aspects of phenomenology may in principle be addressed in terms of the geometry of information integration.},
address = {Department of Psychiatry, University of Wisconsin, Madison, WI, USA. gtononi@wisc.edu},
author = {Tononi, G},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Arch Ital Biol 2012 Tononi.pdf:pdf},
journal = {Arch. Ital. Biol.},
month = {dec},
number = {4},
pages = {293--329},
title = {{Integrated information theory of consciousness: an updated account.}},
volume = {150},
year = {2012}
}
@article{Bashivan2019,
abstract = {Particular deep artificial neural networks (ANNs) are today's most accurate models of the primate brain's ventral visual stream. Using an ANN-driven image synthesis method, we found that luminous power patterns (i.e., images) can be applied to primate retinae to predictably push the spiking activity of targeted V4 neural sites beyond naturally occurring levels. This method, although not yet perfect, achieves unprecedented independent control of the activity state of entire populations of V4 neural sites, even those with overlapping receptive fields. These results show how the knowledge embedded in today's ANN models might be used to noninvasively set desired internal brain states at neuron-level resolution, and suggest that more accurate ANN models would produce even more accurate control.},
address = {Department of Brain and Cognitive Sciences, McGovern Institute for Brain Research, and Center for Brains, Minds, and Machines, Massachusetts Institute of Technology, Cambridge, MA, USA.},
author = {Bashivan, Pouya and Kar, Kohitij and DiCarlo, James J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/5286A716-C295-4C73-BAC9-56759E242230.pdf:pdf},
journal = {Science},
month = {may},
pages = {eaav9436},
title = {{Neural population control via deep image synthesis.}},
volume = {364},
year = {2019}
}
@article{Motter1993,
abstract = {Abstract 1. The activity of single neurons was recorded in Macaca mulatta monkeys while they performed tasks requiring them to select a cued stimulus from an array of three to eight stimuli and report the orientation of that stimulus . Stimuli were presented in a circular array  ...},
author = {Motter, B C},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neurophysiology 1993 Motter.pdf:pdf},
journal = {J. Neurophysiol.},
month = {jan},
title = {{Focal attention produces spatially selective processing in visual cortical areas V1, V2, and V4 in the presence of competing stimuli}},
year = {1993}
}
@article{Moeller2008,
author = {Moeller, S and Freiwald, W A and Tsao, D Y},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Moeller.SOM.pdf:pdf;:Users/hahosoya/Documents/Mendeley Desktop/Science 2008 Moeller-3.pdf:pdf},
journal = {Science},
month = {jun},
number = {5881},
pages = {1355--1359},
title = {{Patches with Links: A Unified System for Processing Faces in the Macaque Temporal Lobe}},
volume = {320},
year = {2008}
}
@article{Zhuang2019,
abstract = {Unsupervised approaches to learning in neural networks are of substantial interest for furthering artificial intelligence, both because they would enable the training of networks without the need for large numbers of expensive annotations, and because they would be better models of the kind of general-purpose learning deployed by humans. However, unsupervised networks have long lagged behind the performance of their supervised counterparts, especially in the domain of large-scale visual recognition. Recent developments in training deep convolutional embeddings to maximize non-parametric instance separation and clustering objectives have shown promise in closing this gap. Here, we describe a method that trains an embedding function to maximize a metric of local aggregation, causing similar data instances to move together in the embedding space, while allowing dissimilar instances to separate. This aggregation metric is dynamic, allowing soft clusters of different scales to emerge. We evaluate our procedure on several large-scale visual recognition datasets, achieving state-of-the-art unsupervised transfer learning performance on object recognition in ImageNet, scene recognition in Places 205, and object detection in PASCAL VOC.},
archivePrefix = {arXiv},
arxivId = {1903.12355},
author = {Zhuang, Chengxu and Zhai, Alex and Yamins, Daniel},
eprint = {1903.12355},
file = {:Users/hahosoya/Documents/Mendeley Desktop/76f513323a55e911651fe63d17a66485e09e0d6c.pdf:pdf},
isbn = {9781728148038},
journal = {Proc. IEEE Int. Conf. Comput. Vis.},
pages = {6001--6011},
title = {{Local aggregation for unsupervised learning of visual embeddings}},
volume = {2019-Octob},
year = {2019}
}
@article{Rust2005,
abstract = {Neurons in primary visual cortex (V1) are commonly classified as simple or complex based upon their sensitivity to the sign of stimulus contrast. The responses of both cell types can be described by a general model in which the outputs of a set of linear filters are nonlinearly combined. We estimated the model for a population of V1 neurons by analyzing the mean and covariance of the spatiotemporal distribution of random bar stimuli that were associated with spikes. This analysis reveals an unsuspected richness of neuronal computation within V1. Specifically, simple and complex cell responses are best described using more linear filters than the one or two found in standard models. Many filters revealed by the model contribute suppressive signals that appear to have a predominantly divisive influence on neuronal firing. Suppressive signals are especially potent in direction-selective cells, where they reduce responses to stimuli moving in the nonpreferred direction.},
address = {Center for Neural Science and New York University, New York, New York 10003, USA. rust@cns.nyu.edu},
author = {Rust, Nicole C and Schwartz, Odelia and Movshon, J Anthony and Simoncelli, Eero P},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neuron 2005 Rust.pdf:pdf},
journal = {Neuron},
month = {jun},
number = {6},
pages = {945--956},
title = {{Spatiotemporal elements of macaque V1 receptive fields.}},
volume = {46},
year = {2005}
}
@article{KOCH1994,
author = {KOCH, CHRISTOF AUTOR and Davis, Joel L},
file = {:Users/hahosoya/Documents/Mendeley Desktop/1994 KOCH.pdf:pdf},
month = {jan},
title = {{Large-scale neuronal theories of the brain}},
year = {1994}
}
@article{Geisler2002,
abstract = {...  Motion  illusions can be understood as optimal adaptations rather than mistakes in other cases 
as well; for example, a Bayesian analysis predicts correctly that changing the motion of a shadow
alone can change the apparent three-dimensional motion of an object 5 . However ...
},
author = {Geisler, W S and Kersten, D},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature neuroscience 2002 Geisler-1.pdf:pdf},
journal = {Nat. Neurosci.},
month = {jan},
title = {{Illusions, perception and Bayes}},
year = {2002}
}
@article{Pollen2002,
abstract = {Subfield analysis of the receptive fields (RFs) of parafoveal V4 complex cells demonstrates directly that most RFs are tiled by overlapping second-order excitatory inputs that for any given V4 cell are predominantly selective to the same preferred values of spatial frequency and orientation. These results extend hierarchical principles of RF organization in the spatial, orientation and spatial frequency domains, first recognized in V1, to an intermediate extrastriate cortex. Spatial interaction studies across subfields demonstrate that the responses of V4 neurons to paired stimuli may either decrease or increase as a function of inter-stimulus distance across the width axis. These intra-RF suppressions and facilitations vary independently in magnitude and spatial extent from cell to cell. These results taken together with the relatively large RF sizes of V4 neurons - as compared with RF sizes of their afferent inputs - lead us to hypothesize a novel property, namely that classes of stimulus configurations that enhance areal summation while reducing suppressive interactions between excitatory inputs will evoke especially robust responses. We tested, and found support for, this hypothesis by presenting stimuli consisting of optimally tuned sine-wave gratings visible only within an annular region and found that such stimuli vigorously activate V4 neurons at firing rates far higher than those evoked by comparable stimuli to either the full-field or central core. On the basis of these results we propose a framework for a new class of neural network models for the spatial RF organizations of prototypic V4 neurons.},
address = {Department of Neurology, University of Massachusetts Medical School, Worcester, MA 01655, USA. pollend@ummhc.org},
author = {Pollen, Daniel A and Przybyszewski, Andrzej W and Rubin, Mark A and Foote, Warren},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Cereb. Cortex 2002 Pollen.pdf:pdf},
journal = {Cereb. Cortex},
month = {jun},
number = {6},
pages = {601--616},
title = {{Spatial receptive field organization of macaque V4 neurons.}},
volume = {12},
year = {2002}
}
@article{Henaff2019,
abstract = {Many behaviors rely on predictions derived from recent visual input, but the temporal evolution of those inputs is generally complex and difficult to extrapolate. We propose that the visual system transforms these inputs to follow straighter temporal trajectories. To test this 'temporal straightening' hypothesis, we develop a methodology for estimating the curvature of an internal trajectory from human perceptual judgments. We use this to test three distinct predictions: natural sequences that are highly curved in the space of pixel intensities should be substantially straighter perceptually; in contrast, artificial sequences that are straight in the intensity domain should be more curved perceptually; finally, naturalistic sequences that are straight in the intensity domain should be relatively less curved. Perceptual data validate all three predictions, as do population models of the early visual system, providing evidence that the visual system specifically straightens natural videos, offering a solution for tasks that rely on prediction.},
address = {Center for Neural Science, New York University, New York, NY, USA. henaff@google.com.},
author = {H{\'{e}}naff, Olivier J and Goris, Robbe L T and Simoncelli, Eero P},
file = {:Users/hahosoya/Documents/Mendeley Desktop/FC997882-1532-4FA5-972B-2B25B342D041.pdf:pdf},
journal = {Nat. Neurosci.},
month = {jun},
number = {6},
pages = {984--991},
title = {{Perceptual straightening of natural videos.}},
volume = {22},
year = {2019}
}
@article{Hubel1959,
author = {Hubel, D H and WIESEL, T N},
file = {:Users/hahosoya/Documents/Mendeley Desktop/The Journal of Physiology 1959 Hubel.pdf:pdf},
journal = {J. Physiol.},
month = {oct},
pages = {574--591},
title = {{Receptive fields of single neurones in the cat's striate cortex.}},
volume = {148},
year = {1959}
}
@article{Høydal2019,
abstract = {Nature, doi:10.1038/s41586-019-1077-7},
author = {H{\o}ydal, {\O}yvind Arne and Skyt{\o}en, Emilie Ranheim and Andersson, Sebastian Ola and Moser, May-Britt and Moser, Edvard I},
file = {:Users/hahosoya/Documents/Mendeley Desktop/EC796267-2582-4774-980D-51013BF62086.pdf:pdf},
journal = {Nature},
month = {apr},
pages = {1--23},
title = {{Object-vector coding in the medial entorhinal cortex}},
year = {2019}
}
@article{Nandy2016,
abstract = {Neuron, Corrected proof. doi:10.1016/j.neuron.2016.11.029},
author = {Nandy, Anirvan S and Nassi, Jonathan J and Reynolds, John H},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neuron 2016 Nandy-2.pdf:pdf},
journal = {Neuron},
month = {nov},
number = {1},
pages = {1--13},
title = {{Laminar Organization of Attentional Modulation in Macaque Visual Area V4}},
volume = {93},
year = {2016}
}
@book{Weiss2016,
abstract = {Machine learning and data mining techniques have been used in numerous real-world applications. An assumption of traditional machine learning methodologies is the training data and testing data are taken from the same domain, such that the input feature space and data distribution characteristics are the same. However, in some real-world machine learning scenarios, this assumption does not hold. There are cases where training data is expensive or difficult to collect. Therefore, there is a need to create high-performance learners trained with more easily obtained data from different domains. This methodology is referred to as transfer learning. This survey paper formally defines transfer learning, presents information on current solutions, and reviews applications applied to transfer learning. Lastly, there is information listed on software downloads for various transfer learning solutions and a discussion of possible future research work. The transfer learning solutions surveyed are independent of data size and can be applied to big data environments.},
author = {Weiss, Karl and Khoshgoftaar, Taghi M. and Wang, Ding Ding},
booktitle = {J. Big Data},
file = {:Users/hahosoya/Documents/Mendeley Desktop/90abbe1f2f78493b9a596ba13b7145a07e50df84.pdf:pdf},
isbn = {4053701600},
keywords = {Data mining,Domain adaptation,Machine learning,Survey,Transfer learning},
number = {1},
title = {{A survey of transfer learning}},
volume = {3},
year = {2016}
}
@article{Gershman2012,
abstract = {Ambiguous images present a challenge to the visual system: How can uncertainty about the causes of visual inputs be represented when there are multiple equally plausible causes? A Bayesian ideal observer should represent uncertainty in the form of a posterior probability distribution over causes. However, in many real-world situations, computing this distribution is intractable and requires some form of approximation. We argue that the visual system approximates the posterior over underlying causes with a set of samples and that this approximation strategy produces perceptual multistability--stochastic alternation between percepts in consciousness. Under our analysis, multistability arises from a dynamic sample-generating process that explores the posterior through stochastic diffusion, implementing a rational form of approximate Bayesian inference known as Markov chain Monte Carlo (MCMC). We examine in detail the most extensively studied form of multistability, binocular rivalry, showing how a variety of experimental phenomena--gamma-like stochastic switching, patchy percepts, fusion, and traveling waves--can be understood in terms of MCMC sampling over simple graphical models of the underlying perceptual tasks. We conjecture that the stochastic nature of spiking neurons may lend itself to implementing sample-based posterior approximations in the brain.},
address = {Department of Psychology and Neuroscience Institute, Princeton University, Princeton, NJ, USA. sjgershm@princeton.edu},
author = {Gershman, Samuel J and Vul, Edward and Tenenbaum, Joshua B},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neural Computation 2012 Gershman.pdf:pdf},
journal = {Neural Comput.},
month = {jan},
number = {1},
pages = {1--24},
title = {{Multistability and perceptual inference.}},
volume = {24},
year = {2012}
}
@article{Srihasam2014,
abstract = {Primate inferotemporal cortex is subdivided into domains for biologically important categories, such as faces, bodies and scenes, as well as domains for culturally entrained categories, such as text or buildings. These domains are in stereotyped locations in most humans and monkeys. To ask what determines the locations of such domains, we intensively trained seven juvenile monkeys to recognize three distinct sets of shapes. After training, the monkeys developed regions that were selectively responsive to each trained set. The location of each specialization was similar across monkeys, despite differences in training order. This indicates that the location of training effects does not depend on function or expertise, but rather on some kind of proto-organization. We explore the possibility that this proto-organization is retinotopic or shape-based.},
author = {Srihasam, Krishna and Vincent, Justin L. and Livingstone, Margaret S.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2014_Srihasam_Vincent_Livingstone, Novel domain formation reveals proto-architecture in IT, Nat Neuro.pdf:pdf},
journal = {Nat. Neurosci.},
number = {12},
pages = {1776--1783},
title = {{Novel domain formation reveals proto-architecture in inferotemporal cortex}},
volume = {17},
year = {2014}
}
@article{Coates2011,
abstract = {Abstract Recent deep learning and unsupervised feature learning systems that learn from unlabeled data have achieved high performance in benchmarks by using extremely large architectures with many features (hidden units) at each layer. Unfortunately, for such large ... 
},
author = {Coates, A and Ng, A Y},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Advances in neural information processing systems 2011 Coates.pdf:pdf},
journal = {Adv. Neural Inf. Process. Syst.},
month = {jan},
title = {{Selecting receptive fields in deep networks}},
year = {2011}
}
@article{Atencio2009,
abstract = {Sensory cortical anatomy has identified a canonical microcircuit underlying computations between and within layers. This feed-forward circuit processes information serially from granular to supragranular and to infragranular layers. How this substrate correlates with an auditory cortical processing hierarchy is unclear. We recorded simultaneously from all layers in cat primary auditory cortex (AI) and estimated spectrotemporal receptive fields (STRFs) and associated nonlinearities. Spike-triggered averaged STRFs revealed that temporal precision, spectrotemporal separability, and feature selectivity varied with layer according to a hierarchical processing model. STRFs from maximally informative dimension (MID) analysis confirmed hierarchical processing. Of two cooperative MIDs identified for each neuron, the first comprised the majority of stimulus information in granular layers. Second MID contributions and nonlinear cooperativity increased in supragranular and infragranular layers. The AI microcircuit provides a valid template for three independent hierarchical computation principles. Increases in processing complexity, STRF cooperativity, and nonlinearity correlate with the synaptic distance from granular layers.},
address = {University of California, San Francisco/University of California, Berkeley Bioengineering Graduate Group, W. M. Keck Foundation Center for Integrative Neuroscience, and Coleman Memorial Laboratory, Department of Otolaryngology-Head and Neck Surgery, Unive},
author = {Atencio, Craig A and Sharpee, Tatyana O and Schreiner, Christoph E},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proceedings of the National Academy of Sciences 2009 Atencio.pdf:pdf},
journal = {Proc. Natl. Acad. Sci.},
month = {dec},
number = {51},
pages = {21894--21899},
title = {{Hierarchical computation in the canonical auditory cortical circuit.}},
volume = {106},
year = {2009}
}
@article{Gutmann2013,
author = {Gutmann, Michael U and Hyv{\"{a}}rinen, Aapo},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Physiology-Paris 2013 Gutmann.pdf:pdf},
journal = {J. Physiol.},
month = {jan},
number = {5},
pages = {369--398},
title = {{A three-layer model of natural image statistics}},
volume = {107},
year = {2013}
}
@article{Weng2003,
abstract = {Abstract—Appearance-based image analysis techniques require fast computation of principal components of high-dimensional image vectors. We introduce a fast incremental principal component analysis (IPCA) algorithm, called candid covariance - free IPCA ( ...},
author = {Weng, J and Zhang, Y and Hwang, W S},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Pattern Analysis and Machine {\ldots} 2003 Weng.pdf:pdf},
journal = {Pattern Anal. Mach.  {\ldots}},
month = {jan},
title = {{Candid covariance-free incremental principal component analysis}},
year = {2003}
}
@article{Haynes2006,
author = {Haynes, John-Dylan and Rees, Geraint},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nat Rev Neurosci 2006 Haynes.pdf:pdf},
journal = {Nat. Rev. Neurosci.},
month = {jul},
number = {7},
pages = {523--534},
title = {{Decoding mental states from brain activity in humans}},
volume = {7},
year = {2006}
}
@article{Ma2006,
author = {Ma, Wei Ji and Beck, Jeffrey M and Latham, Peter E and Pouget, Alexandre},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature neuroscience 2006 Ma-1.pdf:pdf},
journal = {Nat. Neurosci.},
month = {oct},
number = {11},
pages = {1432--1438},
title = {{Bayesian inference with probabilistic population codes}},
volume = {9},
year = {2006}
}
@article{DeValois1982a,
abstract = {We measured the spatial frequency contrast sensitivity of cells in the primate striate cortex at two different eccentricities to provide quantitative statistics from a large population of cells. Distributions of the peak frequencies and bandwidths are presented and examined in relationship to (a) each other, (b) absolute contrast sensitivity, (c) orientation tuning, (d)retinal eccentricity, and (e) cell type. Simple and complex cells are examined in relationship to linear/nonlinear (that is, X/Y) properties; a procedure is described which provides a simple, reliable and quantitative method for classifying and describing striate cells. Among other things, it is shown that (a) many stirate cells have quite narrow spatial bandwidths and (b) at a given retinal eccentricity, the distribution of peak frequency covers a wide range of frequencies; these findings support the basic multiple channel notion. The orientation tuning and spatial frequency tuning which occurs at the level of striate cortex (in a positively correlated fashion) suggests that the cells might best be considered as two-dimensional spatial filters.},
author = {{De Valois}, R L and Albrecht, D G and Thorell, L G},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Vision Research 1982 De Valois.pdf:pdf},
journal = {Vision Res.},
month = {jan},
number = {5},
pages = {545--559},
title = {{Spatial frequency selectivity of cells in macaque visual cortex.}},
volume = {22},
year = {1982}
}
@article{Gawne2002,
author = {Gawne, T J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neurophysiology 2002 Gawne.pdf:pdf},
journal = {J. Neurophysiol.},
month = {nov},
number = {5},
pages = {2178--2186},
title = {{Responses of Primate Visual Cortical Neurons to Stimuli Presented by Flash, Saccade, Blink, and External Darkening}},
volume = {88},
year = {2002}
}
@article{Angelucci2002,
abstract = {Contrast-dependent changes in spatial summation and contextual modulation of primary visual cortex (V1) neuron responses to stimulation of their receptive field reveal long-distance integration of visual signals within V1, well beyond the classical receptive field (cRF) of single neurons. To identify the cortical circuits mediating these long-distance computations, we have used a combination of anatomical and physiological recording methods to determine the spatial scale and retinotopic logic of intra-areal V1 horizontal connections and inter-areal feedback connections to V1. We have then compared the spatial scales of these connectional systems to the spatial dimensions of the cRF, spatial summation field (SF), and modulatory surround field of macaque V1 neurons. We find that monosynaptic horizontal connections within area V1 are of an appropriate spatial scale to mediate interactions within the SF of V1 neurons and to underlie contrast-dependent changes in SF size. Contrary to common beliefs, these connections cannot fully account for the dimensions of the surround field. The spatial scale of feedback circuits from extrastriate cortex to V1 is, instead, commensurate with the full spatial range of center-surround interactions. Thus these connections could represent an anatomical substrate for contextual modulation and global-to-local integration of visual signals. Feedback projections connect corresponding and equal-sized regions of the visual field in striate and extrastriate cortices and cover anisotropic parts of visual space, unlike V1 horizontal connections that are isotropic in the macaque. V1 isotropic connectivity demonstrates that anisotropic horizontal connections are not necessary to generate orientation selectivity. Anisotropic feedback connections may play a role in contour completion.},
address = {Department of Ophthalmology and Visual Science, Moran Eye Center, University of Utah, Salt Lake City, Utah 84132, USA. alessandra.angelucci@hsc.utah.edu},
author = {Angelucci, Alessandra and Levitt, Jonathan B and Walton, Emma J S and Hupe, Jean-Michel and Bullier, Jean and Lund, Jennifer S},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2002 Angelucci.pdf:pdf},
journal = {J. Neurosci.},
month = {oct},
number = {19},
pages = {8633--8646},
title = {{Circuits for local and global signal integration in primary visual cortex.}},
volume = {22},
year = {2002}
}
@article{Kornblith2013,
author = {Kornblith, Simon and Cheng, Xueqi and Ohayon, Shay and Tsao, Doris Y},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neuron 2013 Kornblith.pdf:pdf},
journal = {Neuron},
month = {jan},
number = {4},
pages = {766--781},
title = {{A Network for Scene Processing in the Macaque Temporal Lobe}},
volume = {79},
year = {2013}
}
@article{Froudarakis2014,
abstract = {Neural codes are believed to have adapted to the statistical properties of the natural environment. However, the principles that govern the organization of ensemble activity in the visual cortex during natural visual input are unknown. We recorded populations of up to 500 neurons in the mouse primary visual cortex and characterized the structure of their activity, comparing responses to natural movies with those to control stimuli. We found that higher order correlations in natural scenes induced a sparser code, in which information is encoded by reliable activation of a smaller set of neurons and can be read out more easily. This computationally advantageous encoding for natural scenes was state-dependent and apparent only in anesthetized and active awake animals, but not during quiet wakefulness. Our results argue for a functional benefit of sparsification that could be a general principle governing the structure of the population activity throughout cortical microcircuits.},
address = {Department of Neuroscience, Baylor College of Medicine, Houston, Texas, USA.},
author = {Froudarakis, Emmanouil and Berens, Philipp and Ecker, Alexander S and Cotton, R James and Sinz, Fabian H and Yatsenko, Dimitri and Saggau, Peter and Bethge, Matthias and Tolias, Andreas S},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature Publishing Group 2014 Froudarakis.pdf:pdf},
journal = {Nat. Neurosci.},
month = {jun},
number = {6},
pages = {851--857},
title = {{Population code in mouse V1 facilitates readout of natural scenes through increased sparseness.}},
volume = {17},
year = {2014}
}
@article{Peters2015,
abstract = {Nature 510, 263 (2014). doi:10.1038/nature13235},
author = {Peters, Andrew J and Chen, Simon X and Komiyama, Takaki},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature 2015 Peters.pdf:pdf},
journal = {Nature},
month = {apr},
number = {7504},
pages = {263--267},
title = {{Emergence of reproducible spatiotemporal activity during motor learning}},
volume = {510},
year = {2015}
}
@article{Makino2015,
author = {Makino, Hiroshi and Komiyama, Takaki},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature Publishing Group 2015 Makino.pdf:pdf},
journal = {Nat. Neurosci.},
month = {jul},
number = {8},
pages = {1116--1122},
title = {{Learning enhances the relative impact of top-down processing in the visual cortex}},
volume = {18},
year = {2015}
}
@article{Brincat2004,
author = {Brincat, Scott L and Connor, Charles E},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature neuroscience 2004 Brincat-1.pdf:pdf},
journal = {Nat. Neurosci.},
month = {jul},
number = {8},
pages = {880--886},
title = {{Underlying principles of visual shape selectivity in posterior inferotemporal cortex}},
volume = {7},
year = {2004}
}
@article{Locatello2020b,
abstract = {The idea behind the \emph{unsupervised} learning of \emph{disentangled} representations is that real-world data is generated by a few explanatory factors of variation which can be recovered by unsupervised learning algorithms. In this paper, we provide a sober look at recent progress in the field and challenge some common assumptions. We first theoretically show that the unsupervised learning of disentangled representations is fundamentally impossible without inductive biases on both the models and the data. Then, we train over $14000$ models covering most prominent methods and evaluation metrics in a reproducible large-scale experimental study on eight data sets. We observe that while the different methods successfully enforce properties "encouraged" by the corresponding losses, well-disentangled models seemingly cannot be identified without supervision. Furthermore, different evaluation metrics do not always agree on what should be considered "disentangled" and exhibit systematic differences in the estimation. Finally, increased disentanglement does not seem to necessarily lead to a decreased sample complexity of learning for downstream tasks. Our results suggest that future work on disentanglement learning should be explicit about the role of inductive biases and (implicit) supervision, investigate concrete benefits of enforcing disentanglement of the learned representations, and consider a reproducible experimental setup covering several data sets.},
archivePrefix = {arXiv},
arxivId = {2010.14766},
author = {Locatello, Francesco and Bauer, Stefan and Lucic, Mario and R{\"{a}}tsch, Gunnar and Gelly, Sylvain and Sch{\"{o}}lkopf, Bernhard and Bachem, Olivier},
eprint = {2010.14766},
file = {:Users/hahosoya/Documents/Mendeley Desktop/cdd70eac1d715d30553d79c2072f69277a4aacb7.pdf:pdf},
pages = {1--62},
title = {{A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation}},
volume = {21},
year = {2020}
}
@article{Felleman1991a,
abstract = {In recent years, many new cortical areas have been identified in the macaque monkey. The number of identified connections between areas has increased even more dramatically. We report here on (1) a summary of the layout of cortical areas associated with vision and with other modalities, (2) a computerized database for storing and representing large amounts of information on connectivity patterns, and (3) the application of these data to the analysis of hierarchical organization of the cerebral cortex. Our analysis concentrates on the visual system, which includes 25 neocortical areas that are predominantly or exclusively visual in function, plus an additional 7 areas that we regard as visual-association areas on the basis of their extensive visual inputs. A total of 305 connections among these 32 visual and visual-association areas have been reported. This represents 31X of the possible number of pathways if each area were connected with all others. The actual degree of connectivity is likely to be closer to 40%. The great majority of pathways involve reciprocal connections between areas. There are also extensive connections with cortical areas outside the visual system proper, including the somatosensory cortex, as well as neocortical, transitional, and archicortical regions in the temporal and frontal lobes. In the somatosensory/motor system, there are 62 identified pathways linking 13 cortical areas, suggesting an overall connectivity of about 40%. Based on the laminar patterns of connections between areas, we propose a hierarchy of visual areas and of somatosensory/motor areas that is more comprehensive than those suggested in other recent studies. The current version of the visual hierarchy includes 10 levels of cortical processing. Altogether, it contains 14 levels if one includes the retina and lateral geniculate nucleus at the bottom as well as the entorhinal cortex and hippocampus at the top. Within this hierarchy, there are multiple, intertwined processing streams, which, at a low level, are related to the compartmental organization of areas VI and V2 and, at a high level, are related to the distinction between processing centers in the temporal and parietal lobes. However, there are some pathways and relationships (about 10% of the total) whose descriptions do not fit cleanly into this hierarchical scheme for one reason or another. In most instances, though, it is unclear whether these represent genuine exceptions to a strict hierarchy rather than inaccuracies or uncertainties in the reported assignment. {\textcopyright} 1991 Oxford University Press.},
author = {Felleman, Daniel J. and {Van Essen}, David C.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/bc2145cf437ccafe1056745c8c05d94eb08fef01.pdf:pdf},
journal = {Cereb. Cortex},
number = {1},
pages = {1--47},
pmid = {1822724},
title = {{Distributed hierarchical processing in the primate cerebral cortex}},
volume = {1},
year = {1991}
}
@article{Dearden1998,
author = {Dearden, R and Friedman, N and Russell, S},
file = {:Users/hahosoya/Documents/Mendeley Desktop/1998 Dearden.pdf:pdf},
journal = {Proc. Natl. Conf. Artif. Intell.},
month = {jan},
pages = {761--768},
title = {{Bayesian Q-learning}},
year = {1998}
}
@article{Hupe1998,
abstract = {A single visual stimulus activates neurons in many different cortical areas. A major challenge in cortical physiology is to understand how the neural activity in these numerous active zones leads to a unified percept of the visual scene. The anatomical basis for these interactions is the dense network of connections that link the visual areas. Within this network, feedforward connections transmit signals from lower-order areas such as V1 or V2 to higher-order areas. In addition, there is a dense web of feedback connections which, despite their anatomical prominence, remain functionally mysterious. Here we show, using reversible inactivation of a higher-order area (monkey area V5/MT), that feedback connections serve to amplify and focus activity of neurons in lower-order areas, and that they are important in the differentiation of figure from ground, particularly in the case of stimuli of low visibility. More specifically, we show that feedback connections facilitate responses to objects moving within the classical receptive field; enhance suppression evoked by background stimuli in the surrounding region; and have the strongest effects for stimuli of low salience.},
address = {Cerveau et Vision INSERM 371, Bron, France.},
author = {Hup{\'{e}}, J M and James, A C and Payne, B R and Lomber, S G and Girard, P and Bullier, J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature 1998 Hup{\'{e}}.pdf:pdf},
journal = {Nature},
month = {aug},
number = {6695},
pages = {784--787},
title = {{Cortical feedback improves discrimination between figure and background by V1, V2 and V3 neurons.}},
volume = {394},
year = {1998}
}
@article{Rich2014,
abstract = {Abstract The rules governing the formation of spatial maps in the hippocampus have not been determined. We investigated the large -scale structure of place field activity by recording hippocampal neurons in rats exploring a previously unencountered 48-meter- ...},
author = {Rich, P D and Liaw, H P and Lee, A K},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Science 2014 Rich.pdf:pdf},
journal = {Science},
month = {aug},
number = {6198},
pages = {812--814},
title = {{Large environments reveal the statistical structure governing hippocampal representations}},
volume = {345},
year = {2014}
}
@article{Kanwisher2006,
author = {Kanwisher, N and Yovel, G},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Philosophical Transactions of the Royal Society B Biological Sciences 2006 Kanwisher.pdf:pdf},
journal = {Philos. Trans. R. Soc. B Biol. Sci.},
month = {dec},
number = {1476},
pages = {2109--2128},
title = {{The fusiform face area: a cortical region specialized for the perception of faces}},
volume = {361},
year = {2006}
}
@article{Nestor2016,
author = {Nestor, Adrian and Plaut, David C and Behrmann, Marlene},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proceedings of the National Academy of Sciences 2016 Nestor.pdf:pdf},
journal = {Proc. Natl. Acad. Sci.},
month = {jan},
number = {2},
pages = {416--421},
title = {{Feature-based face representations and image reconstruction from behavioral and neural data}},
volume = {113},
year = {2016}
}
@article{Fisher2015a,
author = {Fisher, Clark and Freiwald, Winrich A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/C663D267-079B-4457-AEB9-F0CDDDFF0FD9.pdf:pdf},
journal = {Proc. Natl. Acad. Sci. U. S. A.},
month = {nov},
number = {47},
pages = {14717--14722},
title = {{Whole-agent selectivity within the macaque face-processing system}},
volume = {112},
year = {2015}
}
@article{Hyvarinen1999a,
abstract = {Abstract A common problem encountered in such disciplines as statistics, data analysis, signal processing, and neural network research, is finding a suitable representation of multivariate data. For computational and conceptual simplicity, such a representation is  ...},
author = {Hyv{\"{a}}rinen, A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neural computing surveys 1999 Hyv{\"{a}}rinen.pdf:pdf},
journal = {Neural Comput. Surv.},
month = {jan},
title = {{Survey on independent component analysis}},
year = {1999}
}
@inproceedings{pmlr-v119-loynd20a,
abstract = {Transformers have increasingly outperformed gated RNNs in obtaining new state-of-the-art results on supervised tasks involving text sequences. Inspired by this trend, we study the question of how Transformer-based models can improve the performance of sequential decision-making agents. We present the Working Memory Graph (WMG), an agent that employs multi-head self-attention to reason over a dynamic set of vectors representing observed and recurrent state. We evaluate WMG in three environments featuring factored observation spaces: a Pathfinding environment that requires complex reasoning over past observations, BabyAI gridworld levels that involve variable goals, and Sokoban which emphasizes future planning. We find that the combination of WMG's Transformer-based architecture with factored observation spaces leads to significant gains in learning efficiency compared to baseline architectures across all tasks. WMG demonstrates how Transformer-based models can dramatically boost sample efficiency in RL environments for which observations can be factored.},
archivePrefix = {arXiv},
arxivId = {1911.07141},
author = {Loynd, Ricky and Fernandez, Roland and Celikyilmaz, Asli and Swaminathan, Adith and Hausknecht, Matthew},
booktitle = {Proc. 37th Int. Conf. Mach. Learn.},
editor = {III, Hal Daum{\'{e}} and Singh, Aarti},
eprint = {1911.07141},
file = {:Users/hahosoya/Documents/Mendeley Desktop/581a23e0f8a014ed6723f672068baefd8fd55a05.pdf:pdf},
month = {nov},
pages = {6404--6414},
series = {Proceedings of Machine Learning Research},
title = {{Working Memory Graphs}},
volume = {119},
year = {2020}
}
@article{Field1987,
abstract = {Received May 15, 1987; accepted August 14, 1987 The relative efficiency of any particular 
image-coding scheme should be defined only in relation to the class of images that the code
is likely to encounter. To understand the representation of images by the mammalian  ...},
author = {Field, D J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/J. Opt. Soc. Am. A 1987 Field.pdf:pdf},
journal = {J. Opt. Soc. Am. A},
month = {jan},
title = {{Relations between the statistics of natural images and the response properties of cortical cells}},
year = {1987}
}
@article{Hennequin2014,
author = {Hennequin, Guillaume and Aitchison, Laurence and Lengyel, M{\'{a}}t{\'{e}}},
file = {:Users/hahosoya/Documents/Mendeley Desktop/E5911D64-91DA-4763-84FE-C2536C7D99C4.pdf:pdf},
journal = {NIPS},
month = {jan},
title = {{Fast Sampling-Based Inference in Balanced Neuronal Networks.}},
year = {2014}
}
@article{Teeters2008,
author = {Teeters, Jeffrey L and Harris, Kenneth D and Millman, K Jarrod and Olshausen, Bruno A and Sommer, Friedrich T},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neuroinform 2008 Teeters.pdf:pdf},
journal = {Neuroinformatics},
month = {feb},
number = {1},
pages = {47--55},
title = {{Data Sharing for Computational Neuroscience}},
volume = {6},
year = {2008}
}
@article{Bandyopadhyay2010,
abstract = {Nature Neuroscience 13, 361 (2010). doi:10.1038/nn.2490},
author = {Bandyopadhyay, Sharba and Shamma, Shihab A and Kanold, Patrick O},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature Publishing Group 2010 Bandyopadhyay.pdf:pdf},
journal = {Nat. Neurosci.},
month = {jan},
number = {3},
pages = {361--368},
title = {{Dichotomy of functional organization in the mouse auditory cortex}},
volume = {13},
year = {2010}
}
@article{Lewicki2002,
abstract = {The auditory system encodes sound by decomposing the amplitude signal arriving at the ear into multiple frequency bands whose center frequencies and bandwidths are approximately exponential functions of the distance from the stapes. This organization is thought to result from the adaptation of cochlear mechanisms to the animal's auditory environment. Here we report that several basic auditory nerve fiber tuning properties can be accounted for by adapting a population of filter shapes to encode natural sounds efficiently. The form of the code depends on sound class, resembling a Fourier transformation when optimized for animal vocalizations and a wavelet transformation when optimized for non-biological environmental sounds. Only for the combined set does the optimal code follow scaling characteristics of physiological data. These results suggest that auditory nerve fibers encode a broad set of natural sounds in a manner consistent with information theoretic principles.},
address = {Computer Science Department and Center for the Neural Basis of Cognition, Carnegie Mellon University, 4400 Fifth Avenue, Pittsburgh, Pennsylvania 15213, USA. lewicki@cnbc.cmu.edu},
author = {Lewicki, Michael S},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature neuroscience 2002 Lewicki-1.pdf:pdf},
journal = {Nat. Neurosci.},
month = {apr},
number = {4},
pages = {356--363},
title = {{Efficient coding of natural sounds.}},
volume = {5},
year = {2002}
}
@inproceedings{Krull2015,
abstract = {Abstract Analysis-by-synthesis has been a successful approach for many tasks in computer vision, such as 6D pose estimation of an object in an RGB-D image which is the topic of this work. The idea is to compare the observation with the output of a forward process, such as ...
},
author = {Krull, A and Brachmann, E and Michel, F},
booktitle = {IEEE Int. Conf. Comput. Vis.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/The IEEE International Conference on Computer Vision (ICCV) 2015 Krull.pdf:pdf},
month = {jan},
title = {{Learning Analysis-by-Synthesis for 6D Pose Estimation in RGB-D Images}},
year = {2015}
}
@article{Tononi2008,
abstract = {The integrated information theory (IIT) starts from phenomenology and makes use of thought experiments to claim that consciousness is integrated information. Specifically: (i) the quantity of consciousness corresponds to the amount of integrated information generated by a complex of elements; (ii) the quality of experience is specified by the set of informational relationships generated within that complex. Integrated information (Phi) is defined as the amount of information generated by a complex of elements, above and beyond the information generated by its parts. Qualia space (Q) is a space where each axis represents a possible state of the complex, each point is a probability distribution of its states, and arrows between points represent the informational relationships among its elements generated by causal mechanisms (connections). Together, the set of informational relationships within a complex constitute a shape in Q that completely and univocally specifies a particular experience. Several observations concerning the neural substrate of consciousness fall naturally into place within the IIT framework. Among them are the association of consciousness with certain neural systems rather than with others; the fact that neural processes underlying consciousness can influence or be influenced by neural processes that remain unconscious; the reduction of consciousness during dreamless sleep and generalized seizures; and the distinct role of different cortical architectures in affecting the quality of experience. Equating consciousness with integrated information carries several implications for our view of nature.},
address = {Department of Psychiatry, University of Wisconsin, Madison, Wisconsin, USA. gtononi@wisc.edu},
author = {Tononi, Giulio},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Biol. Bull. 2008 Tononi.pdf:pdf},
journal = {Biol. Bull.},
month = {dec},
number = {3},
pages = {216--242},
title = {{Consciousness as integrated information: a provisional manifesto.}},
volume = {215},
year = {2008}
}
@article{Hochreiter1997,
abstract = {Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, 
},
author = {Hochreiter, S and Schmidhuber, J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neural Computation 1997 Hochreiter.pdf:pdf},
journal = {Neural Comput.},
month = {jan},
title = {{Long short-term memory}},
year = {1997}
}
@article{Nemeth2020,
abstract = {We consider the disentanglement of the representations of the relevant attributes of the data (content) from all other factors of variations (style) using Variational Autoencoders. Some recent works addressed this problem by utilizing grouped observations, where the content attributes are assumed to be common within each group, while there is no any supervised information on the style factors. In many cases, however, these methods fail to prevent the models from using the style variables to encode content related features as well. This work supplements these algorithms with a method that eliminates the content information in the style representations. For that purpose the training objective is augmented to minimize an appropriately defined mutual information term in an adversarial way. Experimental results and comparisons on image datasets show that the resulting method can efficiently separate the content and style related attributes and generalizes to unseen data.},
archivePrefix = {arXiv},
arxivId = {2001.04761},
author = {Nemeth, Jozsef},
eprint = {2001.04761},
file = {:Users/hahosoya/Documents/Mendeley Desktop/91526e2ba8400fdd6429124b2e6542a28c7c54c1.pdf:pdf},
journal = {arXiv},
title = {{Adversarial disentanglement with grouped observations}},
year = {2020}
}
@article{Kupers2019,
abstract = {Visual performance depends on polar angle, even when eccentricity is held constant; on many psychophysical tasks observers perform best when stimuli are presented on the horizontal meridian, worst on the upper vertical, and intermediate on the lower vertical meridian. This variation in performance ‘around' the visual field can be as pronounced as that of doubling the stimulus eccentricity. The causes of these asymmetries in performance are largely unknown. Some factors in the eye, e.g. cone density, are positively correlated with the reported variations in visual performance with polar angle. However, the question remains whether these correlations can quantitatively explain the perceptual differences observed ‘around' the visual field. To investigate the extent to which the earliest stages of vision–opti-cal quality and cone density–contribute to performance differences with polar angle, we created a computational observer model. The model uses the open-source software package ISETBIO to simulate an orientation discrimination task for which visual performance differs with polar angle. The model starts from the photons emitted by a display, which pass through simulated human optics with fixational eye movements, followed by cone isomeriza-tions in the retina. Finally, we classify stimulus orientation using a support vector machine to learn a linear classifier on the photon absorptions. To account for the 30% increase in contrast thresholds for upper vertical compared to horizontal meridian, as observed psycho-physically on the same task, our computational observer model would require either an increase of $\sim$7 diopters of defocus or a reduction of 500% in cone density. These values far exceed the actual variations as a function of polar angle observed in human eyes. Therefore, we conclude that these factors in the eye only account for a small fraction of differences in visual performance with polar angle. Substantial additional asymmetries must arise in later retinal and/or cortical processing.},
author = {Kupers, Eline R. and Carrasco, Marisa and Winawer, Jonathan},
file = {:Users/hahosoya/Documents/Mendeley Desktop/journal.pcbi.1007063.pdf:pdf},
isbn = {1111111111},
journal = {PLoS Comput. Biol.},
number = {5},
pages = {1--29},
title = {{Modeling visual performance differences ‘around' the visual field: A computational observer approach}},
volume = {15},
year = {2019}
}
@article{Sharpee2006,
abstract = {Abstract Sensory neuroscience seeks to understand how the brain encodes natural environments. However, neural coding has largely been studied using simplified stimuli. In order to assess whether the brain's coding strategy depends on the stimulus ensemble, ... 
},
author = {Sharpee, Tatyana O and Sugihara, Hiroki and Kurgansky, Andrei V and Rebrik, Sergei P and Stryker, Michael P and Miller, Kenneth D},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature 2006 Sharpee.pdf:pdf},
journal = {Nature},
month = {feb},
number = {7079},
pages = {936--942},
title = {{Adaptive filtering enhances information transmission in visual cortex}},
volume = {439},
year = {2006}
}
@article{Hinton1995,
abstract = {An unsupervised learning algorithm for a multilayer network of stochastic neurons is described. Bottom-up "recognition" connections convert the input into representations in successive hidden layers, and top-down "generative" connections reconstruct the representation in one layer from the representation in the layer above. In the "wake" phase, neurons are driven by recognition connections, and generative connections are adapted to increase the probability that they would reconstruct the correct activity vector in the layer below. In the "sleep" phase, neurons are driven by generative connections, and recognition connections are adapted to increase the probability that they would produce the correct activity vector in the layer above.},
author = {Hinton, Geoffrey E. and Dayan, Peter and Frey, Brendan J. and Neal, Radford M.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/6dd01cd9c17d1491ead8c9f97597fbc61dead8ea.pdf:pdf},
journal = {Science},
number = {5214},
pages = {1158--1161},
pmid = {7761831},
title = {{The "wake-sleep" algorithm for unsupervised neural networks}},
volume = {268},
year = {1995}
}
@article{Ringach2002a,
abstract = {I present measurements of the spatial structure of simple-cell receptive fields in macaque primary visual cortex (area V1). Similar to previous findings in cat area 17, the spatial profile of simple-cell receptive fields in the macaque is well described by two-dimensional Gabor functions. A population analysis reveals that the distribution of spatial profiles in primary visual cortex lies approximately on a one-parameter family of filter shapes. Surprisingly, the receptive fields cluster into even- and odd-symmetry classes with a tendency for neurons that are well tuned in orientation and spatial frequency to have odd-symmetric receptive fields. The filter shapes predicted by two recent theories of simple-cell receptive field function, independent component analysis and sparse coding, are compared with the data. Both theories predict receptive fields with a larger number of subfields than observed in the experimental data. In addition, these theories do not generate receptive fields that are broadly tuned in orientation and low-pass in spatial frequency, which are commonly seen in monkey V1. The implications of these results for our understanding of image coding and representation in primary visual cortex are discussed.},
address = {Department of Neurobiology, Psychology, and Brain Research Institute, University of California, Los Angeles, California 90095, USA. dario@ucla.edu},
author = {Ringach, Dario L},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neurophysiology 2002 Ringach.pdf:pdf},
journal = {J. Neurophysiol.},
month = {jul},
number = {1},
pages = {455--463},
title = {{Spatial structure and symmetry of simple-cell receptive fields in macaque primary visual cortex.}},
volume = {88},
year = {2002}
}
@article{Xu2014,
author = {Xu, X and Biederman, I and Shah, M P},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Vision 2014 Xu.pdf:pdf},
journal = {J. Vis.},
month = {jul},
number = {8},
pages = {9},
title = {{A neurocomputational account of the face configural effect}},
volume = {14},
year = {2014}
}
@article{Urbanczik2014,
abstract = {Neuron, 81 (2014) 521-528. doi:10.1016/j.neuron.2013.11.030},
author = {Urbanczik, Robert and Senn, Walter},
file = {:Users/hahosoya/Documents/Mendeley Desktop/9E539711-B434-4B37-A0F1-6A4D86CD48AA.pdf:pdf},
journal = {Neuron},
month = {feb},
number = {3},
pages = {521--528},
title = {{Learning by the Dendritic Prediction of Somatic Spiking}},
volume = {81},
year = {2014}
}
@article{Yu2013,
author = {Yu, J and Ferster, D},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2013 Yu.pdf:pdf},
journal = {J. Neurosci.},
month = {nov},
number = {48},
pages = {18855--18866},
title = {{Functional Coupling from Simple to Complex Cells in the Visually Driven Cortical Circuit}},
volume = {33},
year = {2013}
}
@article{Anzai1999,
abstract = {The visual system integrates information from the left and right eyes and constructs a visual world that is perceived as single and three dimensional. To understand neural mechanisms underlying this process, it is important to learn about how signals from the two eyes interact at the level of single neurons. Using a sophisticated receptive field (RF) mapping technique that employs binary m-sequences, we have determined the rules of binocular interactions exhibited by simple cells in the cat's striate cortex in relation to the structure of their monocular RFs. We find that binocular interaction RFs of most simple cells are well described as the product of left and right eye RFs. Therefore the binocular interactions depend not only on binocular disparity but also on monocular stimulus position or phase. The binocular interaction RF is consistent with that predicted by a model of a linear binocular filter followed by a static nonlinearity. The static nonlinearity is shown to have a shape of a half-power function with an average exponent of approximately 2. Although the initial binocular convergence of signals is linear, the static nonlinearity makes binocular interaction multiplicative at the output of simple cells. This multiplicative binocular interaction is a key ingredient for the computation of interocular cross-correlation, an algorithm for solving the stereo correspondence problem. Therefore simple cells may perform initial computations necessary to solve this problem.},
address = {Group in Vision Science, School of Optometry, University of California, Berkeley, California 94720-2020, USA.},
author = {Anzai, A and Ohzawa, I and Freeman, R D},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neurophysiology 1999 Anzai.pdf:pdf},
journal = {J. Neurophysiol.},
month = {aug},
number = {2},
pages = {891--908},
title = {{Neural mechanisms for processing binocular information I. Simple cells.}},
volume = {82},
year = {1999}
}
@article{Lochy2018,
author = {Lochy, Aliette and Jacques, Corentin and Maillard, Louis and Colnat-Coulbois, Sophie and Rossion, Bruno and Jonas, Jacques},
file = {:Users/hahosoya/Documents/Mendeley Desktop/AFEDCD67-F483-45D6-A581-68113CAB6F6B.pdf:pdf},
journal = {Proc. Natl. Acad. Sci. U. S. A.},
month = {aug},
number = {32},
pages = {E7595--E7604},
title = {{Selective visual representation of letters and words in the left ventral occipito-temporal cortex with intracerebral recordings}},
volume = {115},
year = {2018}
}
@article{Beck2011a,
author = {Beck, J M and Latham, P E and Pouget, A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2011 Beck.pdf:pdf},
journal = {J. Neurosci.},
month = {oct},
number = {43},
pages = {15310--15319},
title = {{Marginalization in Neural Circuits with Divisive Normalization}},
volume = {31},
year = {2011}
}
@article{Asuncion2007,
abstract = {Very large data sets, such as collections of images, text, and related data, are becoming increasingly 
common, with examples ranging from digitized collections of books by companies such as Google
and Amazon, to large collections of images at Web sites such as Flickr, to the recent  ...},
author = {Asuncion, A and Smyth, P and Welling, M},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Advances in neural {\ldots} 2007 Asuncion.pdf:pdf},
journal = {Adv. neural  {\ldots}},
month = {jan},
title = {{Distributed inference for latent dirichlet allocation}},
year = {2007}
}
@article{Najemnik2005,
author = {Najemnik, J and Geisler, W S},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature 2005 Najemnik.pdf:pdf},
journal = {Nature},
month = {jan},
number = {7031},
pages = {387--391},
title = {{Optimal eye movement strategies in visual search}},
volume = {434},
year = {2005}
}
@article{Jensen1995,
abstract = {Page 1. Blocking  Gibbs  Sampling in Very Large Probabilistic Expert Systems by ... d Page 2. Blocking Gibbs  Sampling in Very Large Probabilistic Expert Systems Claus Skaanning Jensen Aalborg 
University Fredrik Bajers Vej 7E DK-9220 Aalborg Denmark claus@iesd.auc.dk ...
},
author = {Jensen, C S and et Al},
file = {:Users/hahosoya/Documents/Mendeley Desktop/International Journal of Human {\ldots} 1995 Jensen.pdf:pdf},
journal = {Int. J. Hum.  {\ldots}},
month = {jan},
title = {{Blocking Gibbs sampling in very large probabilistic expert systems}},
year = {1995}
}
@article{Ruckert2012,
abstract = {Biological movement generation combines three interesting aspects: its modular organization in movement primitives (MPs), its characteristics of stochastic optimality under perturbations, and its efficiency in terms of learning.},
author = {R{\"{u}}ckert, Elmar A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2012 R{\"{u}}ckert.pdf:pdf},
month = {dec},
pages = {1--20},
title = {{Learned graphical models for probabilistic planning provide a new class of movement primitives}},
year = {2012}
}
@article{Tao2012,
author = {Tao, X and Zhang, B and Smith, E L and Nishimoto, S and Ohzawa, I and Chino, Y M},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neurophysiology 2012 Tao.pdf:pdf},
journal = {J. Neurophysiol.},
month = {feb},
number = {4},
pages = {1094--1110},
title = {{Local sensitivity to stimulus orientation and spatial frequency within the receptive fields of neurons in visual area 2 of macaque monkeys}},
volume = {107},
year = {2012}
}
@article{Schwartz2001,
author = {Schwartz, Odelia and Simoncelli, Eero P},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature neuroscience 2001 Schwartz-1.pdf:pdf},
journal = {Nat. Neurosci.},
month = {jan},
number = {8},
pages = {819--825},
title = {{Natural signal statistics and sensory gain control}},
volume = {4},
year = {2001}
}
@article{Sun2020,
abstract = {The brain codes continuous spatial, temporal and sensory changes in daily experience. Recent studies suggest that the brain also tracks experience as segmented subdivisions (events), but the neural basis for encoding events remains unclear. Here, we designed a maze for mice, composed of four materially indistinguishable lap events, and identify hippocampal CA1 neurons whose activity are modulated not only by spatial location but also lap number. These ‘event-specific rate remapping' (ESR) cells remain lap-specific even when the maze length is unpredictably altered within trials, which suggests that ESR cells treat lap events as fundamental units. The activity pattern of ESR cells is reused to represent lap events when the maze geometry is altered from square to circle, which suggests that it helps transfer knowledge between experiences. ESR activity is separately manipulable from spatial activity, and may therefore constitute an independent hippocampal code: an ‘event code' dedicated to organizing experience by events as discrete and transferable units.},
author = {Sun, Chen and Yang, Wannan and Martin, Jared and Tonegawa, Susumu},
file = {:Users/hahosoya/Documents/Mendeley Desktop/s41593-020-0614-x.pdf:pdf},
journal = {Nat. Neurosci.},
number = {5},
pages = {651--663},
pmid = {32251386},
title = {{Hippocampal neurons represent events as transferable units of experience}},
volume = {23},
year = {2020}
}
@article{David2006,
author = {David, S V and Hayden, B Y and Gallant, J L},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neurophysiology 2006 David.pdf:pdf},
journal = {J. Neurophysiol.},
month = {aug},
number = {6},
pages = {3492--3505},
title = {{Spectral Receptive Field Properties Explain Shape Selectivity in Area V4}},
volume = {96},
year = {2006}
}
@article{Kim2018a,
author = {Kim, Hyunjik and Mnih, Andriy},
file = {:Users/hahosoya/Documents/Mendeley Desktop/35430010-F1A9-44C0-9F54-860EAE4E36AD.pdf:pdf},
journal = {ICML},
month = {jan},
title = {{Disentangling by Factorising.}},
year = {2018}
}
@article{Marlin2004,
author = {Marlin, B and Zemel, R S},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2004 Marlin.pdf:pdf},
journal = {Proc. twenty-first Int. Conf. Mach. Learn.},
month = {jan},
pages = {73},
title = {{The multiple multiplicative factor model for collaborative filtering}},
year = {2004}
}
@article{Lumer1998,
abstract = {Abstract When dissimilar images are presented to the two eyes, perception alternates spontaneously between each monocular view, a phenomenon called binocular rivalry. Functional brain imaging in humans was used to study the neural basis of these  ...},
author = {Lumer, E D and Friston, K J and Rees, G},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Science 1998 Lumer.pdf:pdf},
journal = {Science},
month = {jan},
title = {{Neural correlates of perceptual rivalry in the human brain}},
year = {1998}
}
@article{Hughes2010,
abstract = {... We present a novel technique for the quantification of artistic style that utilizes a sparse ... space 
by maximizing the kurtosis of a representation of an arbitrarily selected image from that ... involves
a direct comparison based on a single relevant statistic, offers a natural and potentially ...
},
author = {Hughes, J M and et Al},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proceedings of the National Academy of Sciences USA 2010 Hughes.pdf:pdf},
journal = {Proc. Natl. Acad. Sci. USA},
month = {jan},
title = {{Quantification of artistic style through sparse coding analysis in the drawings of Pieter Bruegel the Elder}},
year = {2010}
}
@article{Srihasam2012,
abstract = {Distinct brain regions, reproducible from one person to the next, are specialized for processing different kinds of human expertise, such as face recognitionand reading. Here, we explore the relationship between age of learning, learning ability, and specialized brain structures. Specifically, we ask whether the existence of reproducible cortical domains necessarily means that certain abilities are innate, or innately easily learned, or whether reproducible domains can be formed, or refined, by interactions between genetic programs and common early experience. Functional MRI showed that intensive early, but not late, experience caused the formation of category-selective regions in macaque temporal lobe for stimuli never naturally encountered by monkeys. And behaviorally, early training produced more fluent processing of these stimuli than the same training in adults. One explanation for these results is that in higher cortical areas, as in early sensory areas, experience drives functional clustering and functional clustering determines how that information is processed. {\textcopyright} 2012 Elsevier Inc.},
author = {Srihasam, Krishna and Mandeville, Joseph B. and Morocz, Istvan A. and Sullivan, Kevin J. and Livingstone, Margaret S.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/25ed95545627b0f1744f013a8e46c58257d8cd81.pdf:pdf},
journal = {Neuron},
number = {3},
pages = {608--619},
title = {{Behavioral and anatomical consequences of early versus late symbol training in macaques}},
volume = {73},
year = {2012}
}
@article{Osindero2008,
abstract = {We describe an efficient learning procedure for multilayer generative models that combine the 
best aspects of Markov random fields and deep, directed belief nets. The generative models
can be learned one layer at a time and when learning is complete they have a very fast  ...},
author = {Osindero, Simon and Hinton, Geoffrey E},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Advances in neural information processing {\ldots} 2008 Osindero.pdf:pdf},
month = {jan},
title = {{Modeling image patches with a directed hierarchy of Markov random fields}},
year = {2008}
}
@inproceedings{pmlr-v119-locatello20a,
abstract = {Intelligent agents should be able to learn useful representations by observing changes in their environment. We model such observations as pairs of non-i.i.d. images sharing at least one of the underlying factors of variation. First, we theoretically show that only knowing how many factors have changed, but not which ones, is sufficient to learn disentangled representations. Second, we provide practical algorithms that learn disentangled representations from pairs of images without requiring annotation of groups, individual factors, or the number of factors that have changed. Third, we perform a large-scale empirical study and show that such pairs of observations are sufficient to reliably learn disentangled representations on several benchmark data sets. Finally, we evaluate our learned representations and find that they are simultaneously useful on a diverse suite of tasks, including generalization under covariate shifts, fairness, and abstract reasoning. Overall, our results demonstrate that weak supervision enables learning of useful disentangled representations in realistic scenarios.},
author = {Locatello, Francesco and Poole, Ben and Raetsch, Gunnar and Sch{\"{o}}lkopf, Bernhard and Bachem, Olivier and Tschannen, Michael},
booktitle = {Proc. 37th Int. Conf. Mach. Learn.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/locatello20a.pdf:pdf},
pages = {6348--6359},
title = {{Weakly-Supervised Disentanglement Without Compromises}},
volume = {119},
year = {2020}
}
@article{Dumoulin2007,
author = {Dumoulin, Serge O and Hess, Robert F},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Vision Research 2007 Dumoulin.pdf:pdf},
journal = {Vision Res.},
month = {jun},
number = {12},
pages = {1608--1613},
title = {{Cortical specialization for concentric shape processing}},
volume = {47},
year = {2007}
}
@article{Gregor2010,
abstract = {Abstract In Sparse Coding (SC), input vectors are reconstructed using a sparse linear combination of basis vectors. SC has become a popular method for extracting features from data. For a given input, SC minimizes a quadratic reconstruction error with an L1 penalty  ...},
author = {Gregor, K and LeCun, Y},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proceedings of the 27th {\ldots} 2010 Gregor.pdf:pdf},
journal = {Proc. 27th  {\ldots}},
month = {jan},
title = {{Learning fast approximations of sparse coding}},
year = {2010}
}
@article{Priebe2003,
abstract = {Tuning for speed is one key feature of motion-selective neurons in the middle temporal visual area of the macaque cortex (MT, or V5). The present paper asks whether speed is coded in a way that is invariant to the shape of the moving stimulus, and if so, how. When tested with single sine-wave gratings of different spatial and temporal frequencies, MT neurons show a continuum in the degree to which preferred speed depends on spatial frequency. There is some dependence in 75% of MT neurons, and the other 25% maintain speed tuning despite changes in spatial frequency. When tested with stimuli constructed by adding two superimposed sine-wave gratings, the preferred speed of MT neurons becomes less dependent on spatial frequency. Analysis of these responses reveals a speed-tuning nonlinearity that selectively enhances the responses of the neuron when multiple spatial frequencies are present and moving at the same speed. Consistent with the presence of the nonlinearity, MT neurons show speed tuning that is close to form-invariant when the moving stimuli comprise square-wave gratings, which contain multiple spatial frequencies moving at the same speed. We conclude that the neural circuitry in and before MT makes no explicit attempt to render MT neurons speed-tuned for sine-wave gratings, which do not occur in natural scenes. Instead, MT neurons derive form-invariant speed tuning in a way that takes advantage of the multiple spatial frequencies that comprise moving objects in natural scenes.},
address = {Howard Hughes Medical Institute, Department of Physiology, W. M. Keck Foundation Center for Integrative Neuroscience, University of California, San Francisco, California 94143, USA. nico@northwestern.edu},
author = {Priebe, Nicholas J and Cassanello, Carlos R and Lisberger, Stephen G},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2003 Priebe.pdf:pdf},
journal = {J. Neurosci.},
month = {jul},
number = {13},
pages = {5650--5661},
title = {{The neural representation of speed in macaque area MT/V5.}},
volume = {23},
year = {2003}
}
@article{Hubert1985,
abstract = {The problem of comparing two different partitions of a finite set of objects reappears continually in the clustering literature. We begin by reviewing a well-known measure of partition correspondence often attributed to Rand (1971), discuss the issue of correcting this index for chance, and note that a recent normalization strategy developed by Morey and Agresti (1984) and adopted by others (e.g., Miligan and Cooper 1985) is based on an incorrect assumption. Then, the general problem of comparing partitions is approached indirectly by assessing the congruence of two proximity matrices using a simple cross-product measure. They are generated from corresponding partitions using various scoring rules. Special cases derivable include traditionally familiar statistics and/or ones tailored to weight certain object pairs differentially. Finally, we propose a measure based on the comparison of object triples having the advantage of a probabilistic interpretation in addition to being corrected for chance (i.e., assuming a constant value under a reasonable null hypothesis) and bounded between ±1. {\textcopyright} 1985 Springer-Verlag New York Inc.},
author = {Hubert, Lawrence and Arabie, Phipps},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Hubert-Arabie1985_Article_ComparingPartitions.pdf:pdf},
journal = {J. Classif.},
keywords = {Consensus indices,Measures of agreement,Measures of association},
number = {1},
pages = {193--218},
title = {{Comparing partitions}},
volume = {2},
year = {1985}
}
@article{Zipser1988,
abstract = {Neurons in area 7a of the posterior parietal cortex of monkeys respond to both the retinal location of a visual stimulus and the position of the eyes and by combining these signals represent the spatial location of external objects. A neural network model, programmed using back-propagation learning, can decode this spatial information from area 7a neurons and accounts for their observed response properties.},
address = {Institute for Cognitive Science, University of California, San Diego, La Jolla 92093.},
author = {Zipser, D and Andersen, R A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/6E78DB31-E124-4C83-BA5D-BB5516CF7A16.pdf:pdf},
journal = {Nature},
month = {feb},
number = {6158},
pages = {679--684},
title = {{A back-propagation programmed network that simulates response properties of a subset of posterior parietal neurons.}},
volume = {331},
year = {1988}
}
@article{Ponce2017,
author = {Ponce, Carlos R and Hartmann, Till S and Livingstone, Margaret S},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2017 Ponce.pdf:pdf},
journal = {J. Neurosci.},
month = {jan},
number = {3},
pages = {648--659},
title = {{End-Stopping Predicts Curvature Tuning along the Ventral Stream}},
volume = {37},
year = {2017}
}
@article{Lamme2010,
author = {Lamme, Victor A F},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Cognitive Neuroscience 2010 Lamme.pdf:pdf},
journal = {Cogn. Neurosci.},
month = {aug},
number = {3},
pages = {204--220},
title = {{How neuroscience will change our view on consciousness}},
volume = {1},
year = {2010}
}
@techreport{Huang2007,
abstract = {Abstract—Face recognition has benefitted greatly from the many databases that have been produced to study it. Most of these databases have been created under controlled conditions to facilitate the study of specific parameters on the face recognition problem. These ...
},
address = {University of Massachusetts, Amherst},
author = {Huang, G B and Ramesh, M and Berg, T and Learned-Miller, E},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2007 Huang.pdf:pdf},
month = {jan},
title = {{Labeled faces in the wild: A database for studying face recognition in unconstrained environments}},
year = {2007}
}
@article{Clancy2021,
abstract = {Intentional control over external objects is informed by our sensory experience of them. To study how causal relationships are learned and effected, we devised a brain machine interface (BMI) task using wide-field calcium signals. Mice learned to entrain activity patterns in arbitrary pairs of cortical regions to guide a visual cursor to a target location for reward. Brain areas that were normally correlated could be rapidly reconfigured to exert control over the cursor in a sensory-feedback-dependent manner. Higher visual cortex was more engaged when expert but not naive animals controlled the cursor. Individual neurons in higher visual cortex responded more strongly to the cursor when mice controlled it than when they passively viewed it, with the greatest response boosting as the cursor approached the target location. Thus, representations of causally controlled objects are sensitive to intention and proximity to the subject's goal, potentially strengthening sensory feedback to allow more fluent control.},
author = {Clancy, Kelly B. and Mrsic-Flogel, Thomas D.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/1-s2.0-S0896627320309600-main.pdf:pdf},
journal = {Neuron},
keywords = {brain computer interface,brain machine interface,calcium imaging,causal control,decoder,neuroprosthetics,parietal cortex,sensorimotor learning,visual cortex,widefield imaging},
number = {4},
pages = {677--689.e4},
pmid = {33357383},
title = {{The sensory representation of causally controlled objects}},
volume = {109},
year = {2021}
}
@article{Chang2017,
abstract = {Cell, 169 (2017) 1013-1034. doi:10.1016/j.cell.2017.05.011},
author = {Chang, Le and Tsao, Doris Y},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Cell 2017 Le Chang(2).pdf:pdf},
journal = {Cell},
month = {jun},
number = {6},
pages = {1013--1020.e14},
title = {{The Code for Facial Identity in the Primate Brain}},
volume = {169},
year = {2017}
}
@article{Chen2016,
author = {Chen, Xi and Duan, Yan and Houthooft, Rein and Schulman, John and Sutskever, Ilya and Abbeel, Pieter},
file = {:Users/hahosoya/Documents/Mendeley Desktop/DEC1A234-921F-4976-BBDE-1CD2DBDA9150.pdf:pdf},
journal = {Adv. Neural Inf. Process. Syst.},
month = {jan},
title = {{InfoGAN - Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets.}},
year = {2016}
}
@article{Askenfelt1987,
author = {Askenfelt, Anders and Jansson, Erik},
file = {:Users/hahosoya/Documents/Mendeley Desktop/J. Acoust. Soc. Am. 1987 Askenfelt.pdf:pdf},
journal = {J. Acoust. Soc. Am.},
month = {jan},
number = {S1},
pages = {S61--S61},
title = {{From touch to string vibrations—The initial course of the piano tone}},
volume = {81},
year = {1987}
}
@article{Hosoya2012a,
abstract = {We study the interplay of Bayesian inference and natural image learning in a hierarchical vision system, in relation to the response properties of early visual cortex. We particularly focus on a Bayesian network with multinomial variables that can represent discrete feature spaces similar to hypercolumns combining minicolumns, enforce sparsity of activation to learn efficient representations, and explain divisive normalization. We demonstrate that maximal-likelihood learning using sampling-based Bayesian inference gives rise to classical receptive field properties similar to V1 simple cells and V2 cells, while inference performed on the trained network yields nonclassical context-dependent response properties such as cross-orientation suppression and filling in. Comparison with known physiological properties reveals some qualitative and quantitative similarities.},
address = {Brain Science Institute, RIKEN, and PRESTO, Japan Science and Technology Agency, Wako-shi, Saitama 351-0198, Japan. hahosoya@brain.riken.jp},
author = {Hosoya, Haruo},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neural Computation 2012 Hosoya.pdf:pdf},
journal = {Neural Comput.},
month = {aug},
number = {8},
pages = {2119--2150},
title = {{Multinomial Bayesian learning for modeling classical and nonclassical receptive field properties.}},
volume = {24},
year = {2012}
}
@article{Yoshimura2008,
abstract = {Neocortical neuronal circuits are refined by experience during the critical period of early postnatal life. The shift of ocular dominance in the visual cortex following monocular deprivation has been intensively studied to unravel the mechanisms underlying the experience-dependent ... 
},
author = {Yoshimura, Yumiko and Inaba, Mie and Yamada, Kazumasa and Kurotani, Tohru and Begum, Tahamina and Reza, Faruque and Maruyama, Takuro and Komatsu, Yukio},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Eur J Neurosci 2008 Yoshimura.pdf:pdf},
journal = {Eur. J. Neurosci.},
month = {aug},
number = {4},
pages = {730--743},
title = {{Involvement of T-type Ca 2+channels in the potentiation of synaptic and visual responses during the critical period in rat visual cortex}},
volume = {28},
year = {2008}
}
@article{Tanaka2013,
abstract = {We propose models and a method to qualitatively explain the receptive field properties of complex cells in the primary visual cortex. We apply a learning method based on the information maximization principle in a feedforward network, which comprises an input layer of image patches, simple cell-like first-output-layer neurons, and second-output-layer neurons (Model 1). The information maximization results in the emergence of the complex cell-like receptive field properties in the second-output-layer neurons. After learning, second-output-layer neurons receive connection weights having the same size from two first-output-layer neurons with sign-inverted receptive fields. The second-output-layer neurons replicate the phase invariance and iso-orientation suppression. Furthermore, on the basis of these results, we examine a simplified model showing the emergence of complex cell-like receptive fields (Model 2). We show that after learning, the output neurons of this model exhibit iso-orientation suppression, cross-orientation facilitation, and end stopping, which are similar to those found in complex cells. These properties of model neurons suggest that complex cells in the primary visual cortex become selective to features composed of edges to increase the variability of the output.},
address = {Department of Computational Intelligence and Systems Science, Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology Yokohama, Japan.},
author = {Tanaka, Takuma and Nakamura, Kiyohiko},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Front Comput Neurosci 2013 Tanaka.pdf:pdf},
journal = {Front. Comput. Neurosci.},
month = {jan},
pages = {165},
title = {{Information maximization principle explains the emergence of complex cell-like neurons.}},
volume = {7},
year = {2013}
}
@article{Hesse2020,
abstract = {Objects constitute the fundamental currency of our consciousness: they are the things that we perceive, remember and think about. One of the most important objects for a primate is a face. Research on the macaque face patch system in recent years has given us a remarkable window into the detailed processes underlying object recognition. Here, we review the macaque face patch system, including its anatomical organization, coding principles, role in behaviour and interactions with other brain regions. We highlight not only how it constitutes an archetypal object recognition system but also how it may provide a key to understanding mechanisms for higher cognitive function.},
author = {Hesse, Janis K. and Tsao, Doris Y.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/s41583-020-00393-w.pdf:pdf},
journal = {Nat. Rev. Neurosci.},
number = {December},
title = {{The macaque face patch system: a turtle's underbelly for the brain}},
volume = {21},
year = {2020}
}
@article{Jaderberg2019,
abstract = {Reinforcement learning (RL) has shown great success in increasingly complex single-agent environments and two-player turn-based games. However, the real world contains multiple agents, each learning and acting independently to cooperate and compete with other agents. We used a tournament-style evaluation to demonstrate that an agent can achieve human-level performance in a three-dimensional multiplayer first-person video game, Quake III Arena in Capture the Flag mode, using only pixels and game points scored as input. We used a two-tier optimization process in which a population of independent RL agents are trained concurrently from thousands of parallel matches on randomly generated environments. Each agent learns its own internal reward signal and rich representation of the world. These results indicate the great potential of multiagent reinforcement learning for artificial intelligence research.},
archivePrefix = {arXiv},
arxivId = {1807.01281},
author = {Jaderberg, Max and Czarnecki, Wojciech M and Dunning, Iain and Marris, Luke and Lever, Guy and Casta{\~{n}}eda, Antonio Garcia and Beattie, Charles and Rabinowitz, Neil C and Morcos, Ari S and Ruderman, Avraham and Sonnerat, Nicolas and Green, Tim and Deason, Louise and Leibo, Joel Z and Silver, David and Hassabis, Demis and Kavukcuoglu, Koray and Graepel, Thore},
eprint = {1807.01281},
file = {:Users/hahosoya/Documents/Mendeley Desktop/859.full.pdf:pdf},
journal = {Science},
number = {6443},
pages = {859--865},
title = {{Human-level performance in 3D multiplayer games with population-based reinforcement learning}},
volume = {364},
year = {2019}
}
@article{Roy1992,
abstract = {We tested the disparity sensitivity of neurons from the medial superior temporal area (MST) in awake behaving monkeys. While the monkey looked at a fixation spot on a screen in front of it, random dot stimuli moved in the preferred direction of the cell under study, and the disparity of the dots made the stimuli appear to move in a frontoparallel plane in front of, on, or behind the screen. Over 90% of the 272 neurons studied were sensitive to the disparity of the visual stimulus. Of those disparity-sensitive cells, 95% were most responsive either to near stimuli (stimuli with crossed disparities appearing to move in front of the screen) or to far stimuli (stimuli with uncrossed disparities appearing to move behind the screen). In a smaller sample of the disparity-sensitive cells, we found cells whose preferred direction of stimulus motion reversed as the disparity of the stimulus reversed. For example, a cell that responded best to rightward motion for near stimuli responded best to leftward motion for far stimuli. We found that 40% of the disparity-sensitive cells had this disparity-dependent direction selectivity. This disparity-dependent direction selectivity was maintained over the entire range of speeds tested (6-56 degrees/sec). We tested whether the disparity sensitivity of the neurons indicated the distance of the stimulus from the screen where the monkey was fixating (relative depth) or the distance of the stimulus from the monkey (absolute depth) by having the monkey fixate at different depths in front of or behind the screen. For most MST neurons, the changes in vergence did not alter the disparity response, indicating that the disparity sensitivity of these neurons conveyed information on depth relative to the plane of fixation. We conclude that the disparity characteristics of cells in the dorsomedial MST are those expected of a system serving primarily coarse rather than fine stereopsis. The correlation between disparity selectivity and direction selectivity in these neurons, as well as their other properties, suggests a role in signaling the direction of self-motion of the observer through the environment.},
address = {Laboratory of Sensorimotor Research, National Eye Institute, Bethesda, Maryland 20892.},
author = {Roy, J P and Komatsu, H and Wurtz, R H},
file = {:Users/hahosoya/Documents/Mendeley Desktop/The Journal of neuroscience 1992 Roy.pdf:pdf},
journal = {J. Neurosci.},
month = {jul},
number = {7},
pages = {2478--2492},
title = {{Disparity sensitivity of neurons in monkey extrastriate area MST.}},
volume = {12},
year = {1992}
}
@inproceedings{Dobs2019,
abstract = {Why does the human brain contain cortical regions specialized for the perception of some stimulus categories (e.g., faces), but not others (e.g., cars)? And why might functional specialization be a good design strategy for brains in the first place? Here, we used deep convolutional neural networks (CNNs) to test whether models optimized to recognize faces and objects require functional segregation for each task. First, we trained two separate CNNs with the same architecture to categorize either faces or objects. Unsurprisingly, the face-trained CNN performed worse on object categorization than the object-trained CNN and vice versa, demonstrating that the features optimized for each task differ from one another. Second, following the method of Kell et al (2018), we trained a family of dual-task CNNs on both tasks, asking how many layers can be shared before performance declines. Somewhat surprisingly, even the dual-task CNN that shared all layers performed nearly as well as the separate networks. This result is consistent with two hypotheses: 1) face and object recognition may be performed well by using a shared pool of common features or 2) the shared network has learned "hidden" functional specialization. In ongoing work, we are seeking to disambiguate these two hypotheses.},
address = {Brentwood, Tennessee, USA},
author = {Dobs, Katharina and Kell, Alexander and Palmer, Ian and Cohen, Michael and Kanwisher, Nancy},
booktitle = {2019 Conf. Cogn. Comput. Neurosci.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/348e1fadd70d78dc53b4eb695135b4c205cba038.pdf:pdf},
keywords = {deep neural networks,dual-task training,face,functional specificity,object processing,processing},
pages = {570--573},
title = {{Why Are Face and Object Processing Segregated in the Human Brain? Testing Computational Hypotheses with Deep Convolutional Neural Networks}},
year = {2019}
}
@article{Barlow1994,
author = {Barlow, H B},
file = {:Users/hahosoya/Documents/Mendeley Desktop/1994 Barlow.pdf:pdf},
journal = {Large-scale neuronal Theor. brain},
month = {jan},
pages = {1--22},
title = {{What is the computational goal of the neocortex}},
year = {1994}
}
@article{Freiwald2009,
abstract = {Nature Neuroscience 12, 1187 (2009). doi:10.1038/nn.2363},
author = {Freiwald, Winrich A and Tsao, Doris Y and Livingstone, Margaret S},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature neuroscience 2009 Freiwald.pdf:pdf;:Users/hahosoya/Documents/Mendeley Desktop/41593_2009_BFnn2363_MOESM4_ESM.pdf:pdf},
journal = {Nat. Neurosci.},
month = {aug},
number = {9},
pages = {1187--1196},
title = {{A face feature space in the macaque temporal lobe}},
volume = {12},
year = {2009}
}
@article{Richler2012,
abstract = {Few concepts are more central to the study of face recognition than holistic processing.},
author = {Richler, Jennifer J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2012 Richler.pdf:pdf},
month = {dec},
pages = {1--6},
title = {{Meanings, mechanisms, and measures of holistic processing}},
year = {2012}
}
@article{Lund2003,
abstract = {In this review we re-examine the concept of a cortical column in macaque primary visual  cortex , and consider to what extent a functionally defined column reflects any sort of anatomical  
entity that subdivides cortical territory. Functional studies have shown that columns ...},
author = {Lund, J S and et Al},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Cerebral Cortex 2003 Lund.pdf:pdf},
journal = {Cereb. Cortex},
month = {jan},
title = {{Anatomical substrates for functional columns in macaque monkey primary visual cortex}},
year = {2003}
}
@article{LeCun2015,
author = {LeCun, Yann and Bengio, Yoshua and Hinton, Geoffrey},
file = {:Users/hahosoya/Documents/Mendeley Desktop/59B6F2F2-69BE-42A0-A8FC-0275230E168C.pdf:pdf},
journal = {Nature},
month = {may},
number = {7553},
pages = {436--444},
title = {{Deep learning}},
volume = {521},
year = {2015}
}
@article{Parkhi2015,
author = {Parkhi, Omkar M and Vedaldi, Andrea and Zisserman, Andrew},
file = {:Users/hahosoya/Documents/Mendeley Desktop/31C5CC86-4ABC-4272-94E8-0898E412E44C.pdf:pdf},
journal = {Br. Mach. Vis. Conf.},
month = {jan},
pages = {41.1--41.12},
title = {{Deep Face Recognition.}},
year = {2015}
}
@article{Schrouff2020,
abstract = {We measured the fast temporal dynamics of face processing simultaneously across the human temporal cortex (TC) using intracranial recordings in eight participants. We found sites with selective responses to faces clustered in the ventral TC, which responded increasingly strongly to marine animal, bird, mammal, and human faces. Both face-selective and face-active but non-selective sites showed a posterior to anterior gradient in response time and selectivity. A sparse model focusing on information from the human face-selective sites performed as well as, or better than, anatomically distributed models when discriminating faces from non-faces stimuli. Additionally, we identified the posterior fusiform site (pFUS) as causally the most relevant node for inducing distortion of conscious face processing by direct electrical stimulation. These findings support anatomically discrete but temporally distributed response profiles in the human brain and provide a new common ground for unifying the seemingly contradictory modular and distributed modes of face processing.},
author = {Schrouff, Jessica and Raccah, Omri and Baek, Sori and Rangarajan, Vinitha and Salehi, Sina and Mour{\~{a}}o-Miranda, Janaina and Helili, Zeinab and Daitch, Amy L. and Parvizi, Josef},
file = {:Users/hahosoya/Documents/Mendeley Desktop/e79abe88d4424e161401c3fea7e7650ace83d4b8.pdf:pdf},
journal = {Nat. Commun.},
number = {1},
pages = {1--13},
pmid = {32005819},
title = {{Fast temporal dynamics and causal relevance of face processing in the human temporal cortex}},
volume = {11},
year = {2020}
}
@article{Vogelstein2014,
abstract = {Abstract A single nervous system can generate many distinct motor patterns. Identifying which neurons and circuits control which behaviors has been a laborious piecemeal process, usually for one observer-defined behavior at a time. We present a fundamentally ... 
},
author = {Vogelstein, J T and Park, Y and Ohyama, T and Kerr, R A and Truman, J W},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Science 2014 Vogelstein.pdf:pdf},
journal = {Science},
month = {jan},
title = {{Discovery of Brainwide Neural-Behavioral Maps via Multiscale Unsupervised Structure Learning}},
year = {2014}
}
@article{Masaaki2012,
author = {Masaaki, Sato},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2012 Masaaki.pdf:pdf},
month = {sep},
pages = {1--12},
title = {{Boltzmann Machines Turorial}},
year = {2012}
}
@article{Hubel1968,
author = {Hubel, David H and Wiesel, Torsten N},
file = {:Users/hahosoya/Documents/Mendeley Desktop/The Journal of Physiology 1968 Hubel.pdf:pdf},
journal = {J. Physiol.},
month = {jan},
number = {1},
pages = {215--243},
title = {{Receptive fields and functional architecture of monkey striate cortex}},
volume = {195},
year = {1968}
}
@article{Salakhutdinov2012,
abstract = {Abstract We describe how the pretraining algorithm for Deep Boltzmann Machines (DBMs) is related to the pretraining algorithm for Deep Belief Networks and we show that under certain conditions, the pretraining procedure improves the variational lower bound of a two- ... 
},
author = {Salakhutdinov, R and Hinton, G},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Advances in neural information {\ldots} 2012 Salakhutdinov.pdf:pdf},
journal = {Adv. Neural Inf. {\ldots}},
month = {jan},
title = {{A Better Way to Pretrain Deep Boltzmann Machines}},
year = {2012}
}
@article{Wang,
author = {Wang, Chong},
file = {:Users/hahosoya/Documents/Mendeley Desktop/IEEE Trans. Neural Netw. Wang.pdf:pdf},
journal = {IEEE Trans. Neural Networks},
number = {3},
pages = {905--910},
title = {{Variational Bayesian Approach to Canonical Correlation Analysis}},
volume = {18}
}
@article{Chikkerur2010,
abstract = {In the theoretical framework of this paper, attention is part of the inference process that solves the visual recognition problem of what is where. The theory proposes a computational role for attention and leads to a model that predicts some of its main properties at the level of ...},
author = {Chikkerur, Sharat and Serre, Thomas and Tan, Cheston and Poggio, Tomaso},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Vision Research 2010 Chikkerur.pdf:pdf},
journal = {Vision Res.},
month = {oct},
number = {22},
pages = {2233--2247},
title = {{What and where: A Bayesian inference theory of attention}},
volume = {50},
year = {2010}
}
@article{Vinje2002,
abstract = {... Surrounding the CRF is the nonclassical  receptive field (nCRF), where stimuli can modulate the 
responses evoked by CRF stimulation (Allman et al., 1985). ...  Natural -vision movies are designed
to mimic the stimulation that occurs during saccadic vision of a static scene. ...
},
author = {Vinje, W E and Gallant, J L},
file = {:Users/hahosoya/Documents/Mendeley Desktop/The Journal of neuroscience 2002 Vinje.pdf:pdf},
journal = {J. Neurosci.},
month = {jan},
title = {{Natural stimulation of the nonclassical receptive field increases information transmission efficiency in V1}},
year = {2002}
}
@article{Mark2020,
abstract = {Relations between task elements often follow hidden underlying structural forms such as periodicities or hierarchies, whose inferences fosters performance. However, transferring structural knowledge to novel environments requires flexible representations that are generalizable over particularities of the current environment, such as its stimuli and size. We suggest that humans represent structural forms as abstract basis sets and that in novel tasks, the structural form is inferred and the relevant basis set is transferred. Using a computational model, we show that such representation allows inference of the underlying structural form, important task states, effective behavioural policies and the existence of unobserved state-trajectories. In two experiments, participants learned three abstract graphs during two successive days. We tested how structural knowledge acquired on Day-1 affected Day-2 performance. In line with our model, participants who had a correct structural prior were able to infer the existence of unobserved state-trajectories and appropriate behavioural policies.},
author = {Mark, Shirley and Moran, Rani and Parr, Thomas and Kennerley, Steve W. and Behrens, Timothy E.J.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/77a47f0682b3998e3bb3e32ed0412a02d0354e81.pdf:pdf},
journal = {Nat. Commun.},
number = {1},
pages = {1--12},
pmid = {32963219},
title = {{Transferring structural knowledge across cognitive maps in humans and models}},
volume = {11},
year = {2020}
}
@article{Berkes2005,
abstract = {In this study we investigate temporal slowness as a learning principle for receptive fields using slow feature analysis, a new algorithm to determine functions that extract slowly varying signals from the input data. We find a good qualitative and quantitative match between the set of learned functions trained on image sequences and the population of complex cells in the primary visual cortex (V1). The functions show many properties found also experimentally in complex cells, such as direction selectivity, non-orthogonal inhibition, end-inhibition, and side-inhibition. Our results demonstrate that a single unsupervised learning principle can account for such a rich repertoire of receptive field properties.},
address = {Institute for Theoretical Biology, Humboldt University, Berlin, Germany. p.berkes@biologie.hu-berlin.de},
author = {Berkes, Pietro and Wiskott, Laurenz},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Vision 2005 Berkes.pdf:pdf},
journal = {J. Vis.},
month = {jan},
number = {6},
pages = {579--602},
title = {{Slow feature analysis yields a rich repertoire of complex cell properties.}},
volume = {5},
year = {2005}
}
@article{Karklin2009,
abstract = {A fundamental function of the visual system is to encode the building blocks of natural scenes-edges, textures and shapes-that subserve visual tasks such as object recognition and scene understanding. Essential to this process is the formation of abstract representations that generalize from specific instances of visual input. A common view holds that neurons in the early visual system signal conjunctions of image features, but how these produce invariant representations is poorly understood. Here we propose that to generalize over similar images, higher-level visual neurons encode statistical variations that characterize local image regions. We present a model in which neural activity encodes the probability distribution most consistent with a given image. Trained on natural images, the model generalizes by learning a compact set of dictionary elements for image distributions typically encountered in natural scenes. Model neurons show a diverse range of properties observed in cortical cells. These results provide a new functional explanation for nonlinear effects in complex cells and offer insight into coding strategies in primary visual cortex (V1) and higher visual areas.},
address = {Computer Science Department & Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PA 15213, USA. yan.karklin@nyu.edu},
author = {Karklin, Yan and Lewicki, Michael S},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature 2009 Karklin.pdf:pdf;:Users/hahosoya/Documents/Mendeley Desktop/Nature 2008 Karklin.pdf:pdf},
journal = {Nature},
month = {jan},
number = {7225},
pages = {83--86},
title = {{Emergence of complex cell properties by learning to generalize in natural scenes.}},
volume = {457},
year = {2009}
}
@article{Isola2017,
author = {Isola, Phillip and Zhu, Jun-Yan and Zhou, Tinghui and Efros, Alexei A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/1C77A48F-17F5-4A44-A1D6-210D94F6B3E3.pdf:pdf},
journal = {Comput. Vis. Pattern Recognit.},
month = {jan},
pages = {5967--5976},
title = {{Image-to-Image Translation with Conditional Adversarial Networks.}},
year = {2017}
}
@article{Hubel1974,
author = {Hubel, D H and WIESEL, T N},
file = {:Users/hahosoya/Documents/Mendeley Desktop/J. Comp. Neurol. 1974 Hubel.pdf:pdf},
journal = {J. Comp. Neurol.},
month = {dec},
number = {3},
pages = {267--293},
title = {{Sequence regularity and geometry of orientation columns in the monkey striate cortex.}},
volume = {158},
year = {1974}
}
@article{Jarrett2009,
abstract = {What is the Best  Multi - Stage  Architecture for Object  Recognition  ... normalization is the single most 
important ingredient for good accuracy on object recogni- tion ... few years, considerable efforts
have been devoted to designing appropriate feature descriptors for ob - ject  recognition . ...
},
author = {Jarrett, K and Kavukcuoglu, K and et Al},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Computer Vision 2009 Jarrett.pdf:pdf},
journal = {Comput. Vis.},
month = {jan},
title = {{What is the best multi-stage architecture for object recognition?}},
year = {2009}
}
@article{Rosenberg2013,
author = {Rosenberg, A and Cowan, N J and Angelaki, D E},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2013 Rosenberg.pdf:pdf},
journal = {J. Neurosci.},
month = {dec},
number = {49},
pages = {19352--19361},
title = {{The Visual Representation of 3D Object Orientation in Parietal Cortex}},
volume = {33},
year = {2013}
}
@article{Hassabis2017,
abstract = {Neuron, 95 (2017) 245-258. doi:10.1016/j.neuron.2017.06.011},
author = {Hassabis, Demis and Kumaran, Dharshan and Summerfield, Christopher and Botvinick, Matthew},
file = {:Users/hahosoya/Documents/Mendeley Desktop/6B5F39D2-5E29-4C8E-8BF2-99A4121FF71D.pdf:pdf},
journal = {Neuron},
month = {jul},
number = {2},
pages = {245--258},
title = {{Neuroscience-Inspired Artificial Intelligence}},
volume = {95},
year = {2017}
}
@article{Inagaki2011,
abstract = {Social communication in nonhuman primates and humans is strongly affected by facial information from other individuals. Many cortical and subcortical brain areas are known to be involved in processing facial information. However, how the neural representation of faces differs across different brain areas remains unclear. Here, we demonstrate that the reference frame for spatial frequency (SF) tuning of face-responsive neurons differs in the temporal visual cortex and amygdala in monkeys. Consistent with psychophysical properties for face recognition, temporal cortex neurons were tuned to image-based SFs (cycles/image) and showed viewing distance invariant representation of face patterns. On the other hand, many amygdala neurons were influenced by retina-based SFs (cycles/ degree), a characteristic that is useful for social distance computation. The two brain areas also differed in the luminance contrast sensitivity of face-responsive neurons; amygdala neurons sharply reduced their responses to low luminance contrast images, while temporal cortex neurons maintained the level of their responses. From these results, we conclude that different types of visual processing in the temporal visual cortex and the amygdala contribute to the construction of the neural representations of faces. {\textcopyright}2011 the authors.},
author = {Inagaki, Mikio and Fujita, Ichiro},
file = {:Users/hahosoya/Documents/Mendeley Desktop/5b26bba6b20c15718fa20bb2f81a571d8f33192e.pdf:pdf},
journal = {J. Neurosci.},
number = {28},
pages = {10371--10379},
pmid = {21753014},
title = {{Reference frames for spatial frequency in face representation differ in the temporal visual cortex and amygdala}},
volume = {31},
year = {2011}
}
@article{Rust2006,
abstract = {Neurons in area MT (V5) are selective for the direction of visual motion . In addition, many are selective for the motion of complex patterns independent of the orientation of their components, a behavior not seen in earlier visual areas. We show that the responses of ... 
},
author = {Rust, Nicole C and Mante, Valerio and Simoncelli, Eero P and Movshon, J Anthony},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature neuroscience 2006 Rust-1.pdf:pdf},
journal = {Nat. Neurosci.},
month = {oct},
number = {11},
pages = {1421--1431},
title = {{How MT cells analyze the motion of visual patterns}},
volume = {9},
year = {2006}
}
@article{Liu2021,
abstract = {We learn and remember multiple new experiences throughout the day. The neural principles enabling continuous rapid learning and formation of distinct representations of numerous sequential experiences without major interference are not understood. To understand this process, here we interrogated ensembles of hippocampal place cells as rats explored 15 novel linear environments interleaved with sleep sessions over continuous 16 h periods. Remarkably, we found that a population of place cells were selective to environment orientation and topology. This orientation selectivity property biased the network-level discrimination and re/mapping between multiple environments. Novel environmental representations emerged rapidly as more generic, but repeated experience within the environments subsequently enhanced their discriminability. Generalization of prior experience with different environments consequently improved network predictability of future novel environmental representations via strengthened generative predictive codes. These coding schemes reveal a high-capacity, high-efficiency neuronal framework for rapid representation of numerous sequential experiences with optimal discrimination-generalization balance and reduced interference.},
author = {Liu, Kefei and Sibille, Jeremie and Dragoi, George},
file = {:Users/hahosoya/Documents/Mendeley Desktop/1-s2.0-S0896627321006103-main.pdf:pdf},
journal = {Neuron},
keywords = {capacity,discrimination,generalization,hippocampus,orientation selectivity,predictive codes,remapping,sequence learning},
number = {22},
pages = {3688--3698.e6},
pmid = {34506724},
title = {{Orientation selectivity enhances context generalization and generative predictive coding in the hippocampus}},
volume = {109},
year = {2021}
}
@article{Goodfellow2012,
abstract = {Page 1. Spike - and - Slab  Sparse  Coding for Unsupervised  Feature  Discovery Ian 
J. Goodfellow, Aaron Courville, Yoshua Bengio D{\'{e}}partement d'informatique et de
recherche op{\'{e}}rationnelle Universit{\'{e}} de Montr{\'{e}}al Montr{\'{e}}al, QC ...
},
author = {Goodfellow, I J and Courville, Aaron and Bengio, Y},
file = {:Users/hahosoya/Documents/Mendeley Desktop/arXiv preprint arXiv1201.3382 2012 Goodfellow.pdf:pdf},
journal = {arXiv Prepr. arXiv1201.3382},
month = {jan},
title = {{Spike-and-slab sparse coding for unsupervised feature discovery}},
year = {2012}
}
@article{Abbasi2020,
abstract = {Human visual cortex contains discrete areas that respond selectively to specific object categories such as faces, bodies, and places. A long-standing question is whether these areas are shaped by genetic or environmental factors. To address this question, here we analyzed functional MRI data from an unprecedented number (n = 424) of monozygotic (MZ) and dizygotic (DZ) twins. Category-selective maps were more identical in MZ than DZ twins. Within each category-selective area, distinct subregions showed significant genetic influence. Structural MRI analysis revealed that the ‘genetic voxels' were predominantly located in regions with higher cortical curvature (gyral crowns in face areas and sulcal fundi in place areas). Moreover, we found that cortex was thicker and more myelinated in genetic voxels of face areas, while it was thinner and less myelinated in genetic voxels of place areas. This double dissociation suggests a differential development of face and place areas in cerebral cortex.},
author = {Abbasi, Nooshin and Duncan, John and Rajimehr, Reza},
file = {:Users/hahosoya/Documents/Mendeley Desktop/50d19b1c41408ce6db68bf6c71c31fd56f9fbfbc.pdf:pdf},
isbn = {4146702014610},
journal = {Nat. Commun.},
number = {1},
pmid = {32024844},
title = {{Genetic influence is linked to cortical morphology in category-selective areas of visual cortex}},
volume = {11},
year = {2020}
}
@article{Reynolds2004,
author = {Reynolds, John H and Chelazzi, Leonardo},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Annu. Rev. Neurosci. 2004 Reynolds.pdf:pdf},
journal = {Annu. Rev. Neurosci.},
month = {jul},
number = {1},
pages = {611--647},
title = {{ATTENTIONAL MODULATION OF VISUAL PROCESSING}},
volume = {27},
year = {2004}
}
@article{Cox2005,
abstract = {Abstract While it is often assumed that objects can be recognized irrespective of where they fall on the retina, little is known about the mechanisms underlying this ability. By exposing human subjects to an altered world where some objects systematically changed identity ... 
},
author = {Cox, David D and Meier, Philip and Oertelt, Nadja and DiCarlo, James J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature neuroscience 2005 Cox-1.pdf:pdf},
journal = {Nat. Neurosci.},
month = {aug},
number = {9},
pages = {1145--1147},
title = {{'Breaking' position-invariant object recognition}},
volume = {8},
year = {2005}
}
@article{Hohwy2008,
author = {Hohwy, Jakob and Roepstorff, Andreas and Friston, Karl},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Cognition 2008 Hohwy.pdf:pdf},
journal = {Cognition},
month = {sep},
number = {3},
pages = {687--701},
title = {{Predictive coding explains binocular rivalry: An epistemological review}},
volume = {108},
year = {2008}
}
@article{Sunkara2016,
author = {Sunkara, Adhira and DeAngelis, Gregory C and Angelaki, Dora E},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proceedings of the National Academy of Sciences 2016 Sunkara.pdf:pdf},
journal = {Proc. Natl. Acad. Sci.},
month = {may},
number = {18},
pages = {5077--5082},
title = {{Joint representation of translational and rotational components of optic flow in parietal cortex}},
volume = {113},
year = {2016}
}
@article{Oizumi2014,
author = {Oizumi, Masafumi and Albantakis, Larissa and Tononi, Giulio},
editor = {Sporns, Olaf},
file = {:Users/hahosoya/Documents/Mendeley Desktop/PLoS Comp Biol 2014 Oizumi.pdf:pdf},
journal = {PLoS Comput. Biol.},
month = {may},
number = {5},
pages = {e1003588},
title = {{From the Phenomenology to the Mechanisms of Consciousness: Integrated Information Theory 3.0}},
volume = {10},
year = {2014}
}
@article{Taubert2015,
author = {Taubert, J and {Van Belle}, G and Vanduffel, W and Rossion, B and Vogels, R},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2015 Taubert.pdf:pdf},
journal = {J. Neurosci.},
month = {jul},
number = {27},
pages = {9872--9878},
title = {{Neural Correlate of the Thatcher Face Illusion in a Monkey Face-Selective Patch}},
volume = {35},
year = {2015}
}
@article{McClellan2020,
abstract = {Language is crucial for human intelligence, but what exactly is its role? We take language to be a part of a system for understanding and communicating about situations. In humans, these abilities emerge gradually from experience and depend on domain-general principles of biological neural networks: connection-based learning, distributed representation, and context-sensitive, mutual constraint satisfaction-based processing. Current artificial language processing systems rely on the same domain general principles, embodied in artificial neural networks. Indeed, recent progress in this field depends on query-based attention, which extends the ability of these systems to exploit context and has contributed to remarkable breakthroughs. Nevertheless, most current models focus exclusively on language-internal tasks, limiting their ability to perform tasks that depend on understanding situations. These systems also lack memory for the contents of prior situations outside of a fixed contextual span. We describe the organization of the brain's distributed understanding system, which includes a fast learning system that addresses the memory problem. We sketch a framework for future models of understanding drawing equally on cognitive neuroscience and artificial intelligence and exploiting query-based attention. We highlight relevant current directions and consider further developments needed to fully capture human-level language understanding in a computational system.},
author = {McClellan, James L. and Hill, Felix and Rudolph, Maja and Baldridge, Jason and Sch{\"{u}}tze, Hinrich},
file = {:Users/hahosoya/Documents/Mendeley Desktop/25966.full.pdf:pdf},
journal = {Proc. Natl. Acad. Sci. U. S. A.},
keywords = {Artificial intelligence,Cognitive neuroscience,Deep learning,Natural language understanding,Situation models},
number = {42},
pages = {25966--25974},
pmid = {32989131},
title = {{Placing language in an integrated understanding system: Next steps toward human-level performance in neural language models}},
volume = {117},
year = {2020}
}
@article{Jha2018,
abstract = {Generative models that learn disentangled representations for different factors of variation in an image can be very useful for targeted data augmentation. By sampling from the disentangled latent subspace of interest, we can efficiently generate new data necessary for a particular task. Learning disentangled representations is a challenging problem, especially when certain factors of variation are difficult to label. In this paper, we introduce a novel architecture that disentangles the latent space into two complementary subspaces by using only weak supervision in form of pairwise similarity labels. Inspired by the recent success of cycle-consistent adversarial architectures, we use cycle-consistency in a variational auto-encoder framework. Our non-adversarial approach is in contrast with the recent works that combine adversarial training with auto-encoders to disentangle representations. We show compelling results of disentangled latent subspaces on three datasets and compare with recent works that leverage adversarial training.},
archivePrefix = {arXiv},
arxivId = {1804.10469},
author = {Jha, Ananya Harsh and Anand, Saket and Singh, Maneesh and Veeravasarapu, Vsr},
eprint = {1804.10469},
file = {:Users/hahosoya/Documents/Mendeley Desktop/1804.10469.pdf:pdf},
isbn = {9783030012182},
journal = {Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics)},
keywords = {Cycle-consistent architecture,Disentangling factors of variation,Variational auto-encoders},
pages = {829--845},
title = {{Disentangling Factors of Variation with Cycle-Consistent Variational Auto-encoders}},
volume = {11207 LNCS},
year = {2018}
}
@inproceedings{Wang2017,
abstract = {In this paper, we propose a principled Tag Disentangled Generative Adversarial Networks (TD-GAN) for re-rendering new images for the object of interest from a single image of it by specifying multiple scene properties (such as viewpoint, illumination , expression, etc.). The whole framework consists of a disentangling network, a generative network, a tag mapping net, and a discriminative network, which are trained jointly based on a given set of images that are completely/partially tagged (i.e., supervised/semi-supervised setting). Given an input image, the disentangling network extracts disentangled and interpretable representations, which are then used to generate images by the generative network. In order to boost the quality of disentangled representations, the tag mapping net is integrated to explore the consistency between the image and its tags. Furthermore, the discriminative network is introduced to implement the adversar-ial training strategy for generating more realistic images. Experiments on two challenging datasets demonstrate the state-of-the-art performance of the proposed framework in the problem of interest.},
author = {Wang, Chaoyue and Wang, C and Wang, Chaohui and Xu, Chang and Tao, Dacheng},
booktitle = {Int. Jt. Conf. Artif. Intell.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/10C148C5-D0F2-49E7-A903-0D9988B8B544.pdf:pdf},
month = {aug},
title = {{Tag Disentangled Generative Adversarial Networks for Object Image Re-rendering}},
year = {2017}
}
@article{Li2014,
abstract = {A fundamental task of the visual system is to extract figure-ground boundaries between images of objects, which in natural scenes are often defined not only by luminance differences but also by "second-order" contrast or texture differences. Responses to contrast modulation (CM) and other second-order stimuli have been extensively studied in human psychophysics, but the neuronal substrates of second-order responses in nonhuman primates remain poorly understood. In this study, we have recorded single neurons in area V2 of macaque monkeys, using both CM patterns as well as conventional luminance modulation (LM) gratings. CM stimuli were constructed from stationary sine wave grating carrier patterns, which were modulated by drifting envelope gratings of a lower spatial frequency. We found approximately one-third of visually responsive V2 neurons responded to CM stimuli with a pronounced selectivity to carrier spatial frequencies, and often orientations, that were clearly outside the neurons' passbands for LM gratings. These neurons were "form-cue invariant" in that their tuning to CM envelope spatial frequency and orientation was very similar to that for LM gratings. Neurons were tuned to carrier spatial frequencies that were typically 2-4 octaves higher than their optimal envelope spatial frequencies, similar to results from human psychophysics. These results are distinct from CM responses arising from surround suppression, but could be understood in terms of a filter-rectify-filter model. Such neurons could provide a functionally useful and explicit representation of segmentation boundaries as well as a plausible neural substrate for human perception of second-order boundaries.},
address = {Chinese Academy of Sciences Key Laboratory of Brain Function and Disease, and School of Life Sciences, University of Science and Technology of China, Hefei 230027, China, McGill Vision Research Unit, Department of Ophthalmology, McGill University, Montrea},
author = {Li, Guangxing and Yao, Zhimo and Wang, Zhengchun and Yuan, Nini and Talebi, Vargha and Tan, Jiabo and Wang, Yongchang and Zhou, Yifeng and Baker, Curtis L},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2014 Li.pdf:pdf},
journal = {J. Neurosci.},
month = {sep},
number = {36},
pages = {12081--12092},
title = {{Form-cue invariant second-order neuronal responses to contrast modulation in primate area V2.}},
volume = {34},
year = {2014}
}
@article{Gilmore2021,
abstract = {The necessity of the human hippocampus for remote autobiographical recall remains fiercely debated. The standard model of consolidation predicts a time-limited role for the hippocampus, but the competing multiple trace/trace transformation theories posit indefinite involvement. Lesion evidence remains inconclusive, and the inferences one can draw from functional MRI (fMRI) have been limited by reliance on covert (silent) recall, which obscures dynamic, moment-to-moment content of retrieved memories. Here, we capitalized on advances in fMRI denoising to employ overtly spoken recall. Forty participants retrieved recent and remote memories, describing each for approximately 2 min. Details associated with each memory were identified and modeled in the fMRI time-series data using a variant of the Autobiographical Interview procedure, and activity associated with the recall of recent and remote memories was then compared. Posterior hippocampal regions exhibited temporally graded activity patterns (recent events > remote events), as did several regions of frontal and parietal cortex. Consistent with predictions of the standard model, recall-related hippocampal activity differed from a non-autobiographical control task only for recent, and not remote, events. Task-based connectivity between posterior hippocampal regions and others associated with mental scene construction also exhibited a temporal gradient, with greater connectivity accompanying the recall of recent events. These findings support predictions of the standard model of consolidation and demonstrate the potential benefits of overt recall in neuroimaging experiments.},
author = {Gilmore, Adrian W. and Quach, Alina and Kalinowski, Sarah E. and Gonz{\'{a}}lez-Araya, Estefan{\'{i}}a I. and Gotts, Stephen J. and Schacter, Daniel L. and Martin, Alex},
file = {:Users/hahosoya/Documents/Mendeley Desktop/5c8faeaebdcc793fdd12f7bbcbf910c6feb54541.pdf:pdf},
journal = {Proc. Natl. Acad. Sci. U. S. A.},
keywords = {Autobiographical memory | fMRI | hippocampus | par},
number = {12},
pmid = {33723070},
title = {{Evidence supporting a time-limited hippocampal role in retrieving autobiographical memories}},
volume = {118},
year = {2021}
}
@article{Stansbury2013,
abstract = {Neuron, 79 (2013) 1025-1034. doi:10.1016/j.neuron.2013.06.034},
author = {Stansbury, Dustin E and Naselaris, Thomas and Gallant, Jack L},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neuron 2013 Stansbury.pdf:pdf},
journal = {Neuron},
month = {sep},
number = {5},
pages = {1025--1034},
title = {{Natural Scene Statistics Account for the Representation of Scene Categories in Human Visual Cortex}},
volume = {79},
year = {2013}
}
@article{Lewicki1999,
author = {Lewicki, M S and Olshausen, B A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/J. Opt. Soc. Am. A 1999 Lewicki.pdf:pdf},
journal = {J. Opt. Soc. Am. A},
month = {jan},
number = {7},
pages = {1587--1601},
title = {{Probabilistic framework for the adaptation and comparison of image codes}},
volume = {16},
year = {1999}
}
@article{Yan2016,
author = {Yan, Xinchen and Yang, Jimei and Yumer, Ersin and Guo, Yijie and Lee, Honglak},
file = {:Users/hahosoya/Documents/Mendeley Desktop/NIPS 2016 Yan.pdf:pdf},
journal = {Adv. Neural Inf. Process. Syst.},
month = {jan},
title = {{Perspective Transformer Nets - Learning Single-View 3D Object Reconstruction without 3D Supervision.}},
year = {2016}
}
@article{Basu2021,
abstract = {Accurate navigation to a desired goal requires consecutive estimates of spatial relationships between the current position and future destination throughout the journey. Although neurons in the hippocampal formation can represent the position of an animal as well as its nearby trajectories1–7, their role in determining the destination of the animal has been questioned8,9. It is, thus, unclear whether the brain can possess a precise estimate of target location during active environmental exploration. Here we describe neurons in the rat orbitofrontal cortex (OFC) that form spatial representations persistently pointing to the subsequent goal destination of an animal throughout navigation. This destination coding emerges before the onset of navigation, without direct sensory access to a distal goal, and even predicts the incorrect destination of an animal at the beginning of an error trial. Goal representations in the OFC are maintained by destination-specific neural ensemble dynamics, and their brief perturbation at the onset of a journey led to a navigational error. These findings suggest that the OFC is part of the internal goal map of the brain, enabling animals to navigate precisely to a chosen destination that is beyond the range of sensory perception.},
author = {Basu, Raunak and Gebauer, Robert and Herfurth, Tim and Kolb, Simon and Golipour, Zahra and Tchumatchenko, Tatjana and Ito, Hiroshi T.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/s41586-021-04042-9.pdf:pdf},
journal = {Nature},
number = {7885},
pages = {449--452},
title = {{The orbitofrontal cortex maps future navigational goals}},
volume = {599},
year = {2021}
}
@article{Raman2019,
abstract = {A number of recent computational studies have emphasized quantitative similarity between convolutional neural networks (CNNs) and the visual ventral stream up to the inferotemporal (IT) cortex in the primate. However, whether such similarity holds for the face-selective areas, a subsystem of IT, is not clear. To address this question, we extensively investigate whether CNNs show facial tuning properties previously reported by four experimental studies on different macaque face areas. More specifically, while simulating the four experiments on a variety of CNN models optimized for classification, we attempted to make a correspondence between the model layers and the macaque face areas by quantitatively matching the tuning properties. As a result, we found that higher model layers explained well all properties of anterior areas, while no layer simultaneously explained multiple properties of middle areas. This observation was consistent across the different model instances with varied architecture and dataset settings. Thus, our results indicate that, although the near-goal representation of face-classifying CNNs has some similarity with the primate face processing system, the intermediate computational process might be rather different, thus calling for a more comprehensive model for better understanding of this system.},
address = {bioRxiv},
author = {Raman, Rajani and Hosoya, Haruo},
file = {:Users/hahosoya/Documents/Mendeley Desktop/B20AC1E0-CBD1-466D-B5DC-ECB2072C1244.pdf:pdf},
journal = {bioRxiv},
month = {jul},
pages = {1--33},
title = {{CNN explains tuning properties of anterior, but not middle, face-processing areas in macaque IT}},
year = {2019}
}
@article{Learned-Miller,
abstract = {Abstract In 2007, Labeled Faces in the Wild was released in an effort to spur research in face recognition, specifically for the problem of face verification with unconstrained images. Since that time, more than 50 papers have been published that improve upon this ...
},
author = {Learned-Miller, E and Huang, G and RoyChowdhury, A and Li, H},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Learned-Miller.pdf:pdf},
journal = {people.cs.umass.edu },
title = {{Labeled Faces in the Wild: A Survey}},
}
@article{Zhuang2021,
abstract = {Deep neural networks currently provide the best quantitative models of the response patterns of neurons throughout the primate ventral visual stream. However, such networks have remained implausible as a model of the development of the ventral stream, in part because they are trained with supervised methods requiring many more labels than are accessible to infants during development. Here, we report that recent rapid progress in unsupervised learning has largely closed this gap. We find that neural network models learned with deep unsupervised contrastive embedding methods achieve neural prediction accuracy in multiple ventral visual cortical areas that equals or exceeds that of models derived using today's best supervised methods and that the mapping of these neural network models' hidden layers is neuroanatomically consistent across the ventral stream. Strikingly, we find that these methods produce brain-like representations even when trained solely with real human child developmental data collected from head-mounted cameras, despite the fact that these datasets are noisy and limited. We also find that semisupervised deep contrastive embeddings can leverage small numbers of labeled examples to produce representations with substantially improved error-pattern consistency to human behavior. Taken together, these results illustrate a use of unsupervised learning to provide a quantitative model of a multiarea cortical brain system and present a strong candidate for a biologically plausible computational theory of primate sensory learning.},
author = {Zhuang, Chengxu and Yan, Siming and Nayebi, Aran and Schrimpf, Martin and Frank, Michael C. and DiCarlo, James J. and Yamins, Daniel L.K.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/e2014196118.full.pdf:pdf},
journal = {Proc. Natl. Acad. Sci. U. S. A.},
keywords = {Deep neural networks,Unsupervised algorithms,Ventral visual stream},
number = {3},
title = {{Unsupervised neural network models of the ventral visual stream}},
volume = {118},
year = {2021}
}
@article{Nomi2015,
author = {Nomi, Jason S. and Uddin, Lucina Q.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nomi_Uddin_2015_Face processing in autism spectrum disorders.pdf:pdf},
isbn = {2163684814},
journal = {Neuropsychologia},
keywords = {determination,protein crystallography,protein data bank,r -factor,resolution,restraints,structure,structure interpretation,structure quality,structure refinement,structure validation},
month = {may},
number = {1},
pages = {201--216},
title = {{Face processing in autism spectrum disorders: From brain regions to brain networks}},
volume = {71},
year = {2015}
}
@article{Wilson2003,
abstract = {Cortical-form vision comprises multiple, hierarchically arranged areas with feedforward and feedback interconnections. This complex architecture poses difficulties for attempts to link perceptual phenomena to activity at a particular level of the system. This difficulty has been especially salient in studies of binocular rivalry alternations, where there is seemingly conflicting evidence for a locus in primary visual cortex or alternatively in higher cortical areas devoted to object perception. Here, I use a competitive neural model to demonstrate that the data require at least two hierarchic rivalry stages for their explanation. This model demonstrates that competitive inhibition in the first rivalry stage can be eliminated by using suitable stimulus dynamics, thereby revealing properties of a later stage, a result obtained with both spike-rate and conductance-based model neurons. This result provides a synthesis of competing rivalry theories and suggests that neural competition may be a general characteristic throughout the form-vision hierarchy.},
address = {Centre for Vision Research and Department of Biology, York University, 4700 Keele Street, Toronto, ON, Canada M3J 1P3. hrwilson@yorku.ca},
author = {Wilson, Hugh R},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proc. Natl. Acad. Sci. U.S.A. 2003 Wilson.pdf:pdf},
journal = {Proc. Natl. Acad. Sci. U. S. A.},
month = {nov},
number = {24},
pages = {14499--14503},
title = {{Computational evidence for a rivalry hierarchy in vision.}},
volume = {100},
year = {2003}
}
@article{Ohki2005,
abstract = {Neurons in the cerebral cortex are organized into anatomical columns, with ensembles of cells arranged from the surface to the white matter. Within a column, neurons often share functional properties, such as selectivity for stimulus orientation; columns with distinct properties, such as different preferred orientations, tile the cortical surface in orderly patterns. This functional architecture was discovered with the relatively sparse sampling of microelectrode recordings. Optical imaging of membrane voltage or metabolic activity elucidated the overall geometry of functional maps, but is averaged over many cells (resolution >100 microm). Consequently, the purity of functional domains and the precision of the borders between them could not be resolved. Here, we labelled thousands of neurons of the visual cortex with a calcium-sensitive indicator in vivo. We then imaged the activity of neuronal populations at single-cell resolution with two-photon microscopy up to a depth of 400 microm. In rat primary visual cortex, neurons had robust orientation selectivity but there was no discernible local structure; neighbouring neurons often responded to different orientations. In area 18 of cat visual cortex, functional maps were organized at a fine scale. Neurons with opposite preferences for stimulus direction were segregated with extraordinary spatial precision in three dimensions, with columnar borders one to two cells wide. These results indicate that cortical maps can be built with single-cell precision.},
address = {Department of Neurobiology, Harvard Medical School, Boston, Massachusetts 02115, USA.},
author = {Ohki, Kenichi and Chung, Sooyoung and Ch'ng, Yeang H and Kara, Prakash and Reid, R Clay},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature 2005 Ohki.pdf:pdf},
journal = {Nature},
month = {feb},
number = {7026},
pages = {597--603},
title = {{Functional imaging with cellular resolution reveals precise micro-architecture in visual cortex.}},
volume = {433},
year = {2005}
}
@article{Wang2014,
abstract = {Nature Neuroscience 18, 97 (2014). doi:10.1038/nn.3878},
author = {Wang, Yushi and Jin, Jianzhong and Kremkow, Jens and Lashgari, Reza and Komban, Stanley J and Alonso, Jose M},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature Publishing Group 2014 Wang.pdf:pdf},
journal = {Nat. Neurosci.},
month = {nov},
number = {1},
pages = {97--103},
title = {{Columnar organization of spatial phase in visual cortex}},
volume = {18},
year = {2014}
}
@article{Salazar2012,
author = {Salazar, R F and Dotson, N M and Bressler, S L and Gray, C M},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Science 2012 Salazar.pdf:pdf},
journal = {Science},
month = {nov},
number = {6110},
pages = {1097--1100},
title = {{Content-Specific Fronto-Parietal Synchronization During Visual Working Memory}},
volume = {338},
year = {2012}
}
@article{Read2002,
author = {Read, Jenny C A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neural Computation 2002 Read.pdf:pdf},
journal = {Neural Comput.},
month = {jun},
number = {6},
pages = {1371--1392},
title = {{A Bayesian Approach to the Stereo Correspondence Problem}},
volume = {14},
year = {2002}
}
@article{Rezaabad2019,
abstract = {Variational autoencoders (VAEs) have ushered in a new era of unsupervised learning methods for complex distributions. Although these techniques are elegant in their approach, they are typically not useful for representation learning. In this work, we propose a simple yet powerful class of VAEs that simultaneously result in meaningful learned representations. Our solution is to combine traditional VAEs with mutual information maximization, with the goal to enhance amortized inference in VAEs using Information Theoretic techniques. We call this approach InfoMax-VAE, and such an approach can significantly boost the quality of learned high-level representations. We realize this through the explicit maximization of information measures associated with the representation. Using extensive experiments on varied datasets and setups, we show that InfoMax-VAE outperforms contemporary popular approaches, including Info-VAE and $\beta$-VAE.},
archivePrefix = {arXiv},
arxivId = {1912.13361},
author = {Rezaabad, Ali Lotfi and Vishwanath, Sriram},
eprint = {1912.13361},
file = {:Users/hahosoya/Documents/Mendeley Desktop/5e5c843c9b4016e34b6204dc1f1a4f2f09dbd20f.pdf:pdf},
number = {NeurIPS},
title = {{Learning Representations by Maximizing Mutual Information in Variational Autoencoders}},
year = {2019}
}
@article{Atkins1994,
abstract = {Abstract. Given a set of objects and a correlation function/reflecting the desire for two items to be near each other, find all sequences ж of the items so that correlation preferences are preserved; that is if ir (i)< 7r (j)< w (k) then f (i, j)>/(«, &) and f (j, k)> f (i, k). This seriation ...
},
author = {Atkins, J E and Boman, E G and Hendrickson, B},
file = {:Users/hahosoya/Documents/Mendeley Desktop/1994 Atkins.pdf:pdf},
month = {jan},
title = {{A spectral algorithm for the seriation problem}},
year = {1994}
}
@article{Sønderby2016a,
abstract = {Abstract Variational autoencoders are powerful models for unsupervised learning. However deep models with several layers of dependent stochastic variables are difficult to train which limits the improvements obtained using these highly expressive models. We propose a new 
},
author = {S{\o}nderby, C K and Raiko, T and Maal{\o}e, L},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Advances in neural {\ldots} 2016 S{\o}nderby.pdf:pdf},
journal = {Adv. neural  {\ldots}},
month = {jan},
title = {{Ladder variational autoencoders}},
year = {2016}
}
@article{Kummerer2015,
author = {K{\"{u}}mmerer, Matthias and Wallis, Thomas S A and Bethge, Matthias},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proceedings of the National Academy of Sciences 2015 K{\"{u}}mmerer.pdf:pdf},
journal = {Proc. Natl. Acad. Sci.},
month = {dec},
number = {52},
pages = {16054--16059},
title = {{Information-theoretic model comparison unifies saliency metrics}},
volume = {112},
year = {2015}
}
@article{Jordan1996,
author = {Jordan, M I and Jaakkola, T S and Saul, L K},
file = {:Users/hahosoya/Documents/Mendeley Desktop/1996 Jordan.pdf:pdf},
month = {jan},
title = {{Fast Learning by Bounding Likelihoods in Sigmoid Type Belief Networks}},
year = {1996}
}
@article{Neri2014,
author = {Neri, P},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2014 Neri.pdf:pdf},
journal = {J. Neurosci.},
month = {feb},
number = {6},
pages = {2374--2388},
title = {{Semantic Control of Feature Extraction from Natural Scenes}},
volume = {34},
year = {2014}
}
@article{Cootes2001,
author = {Cootes, Timothy F and Edwards, Gareth J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/40730BAE-BB58-488B-A5DC-5AF8B241D58D.pdf:pdf},
journal = {IEEE Trans. Pattern Anal. Mach. Intell.},
month = {jan},
number = {6},
pages = {681--685},
title = {{Active Appearance Models.}},
volume = {23},
year = {2001}
}
@article{Brown2019,
abstract = {In recent years there have been great strides in artificial intelligence (AI), with games often serving as challenge problems, benchmarks, and milestones for progress. Poker has served for decades as such a challenge problem. Past successes in such benchmarks, including poker, have been limited to two-player games. However, poker in particular is traditionally played with more than two players. Multiplayer games present fundamental additional issues beyond those in two-player games, and multiplayer poker is a recognized AI milestone. In this paper we present Pluribus, an AI that we show is stronger than top human professionals in six-player no-limit Texas hold'em poker, the most popular form of poker played by humans.},
author = {Brown, Noam and Sandholm, Tuomas},
file = {:Users/hahosoya/Documents/Mendeley Desktop/885.full.pdf:pdf},
journal = {Science},
number = {6456},
pages = {885--890},
title = {{Superhuman AI for multiplayer poker}},
volume = {365},
year = {2019}
}
@article{Rao2005,
abstract = {... E-mail: rao @cs.washington.edu. ... The responses of neurons in cortical areas V2 and V4 can be significantly modulated by attention to particular locations ... We show that such effects emerge naturally when perception is viewed as a probabilistic inference process governed by ...},
author = {Rao, Rajesh P N},
file = {:Users/hahosoya/Documents/Mendeley Desktop/NeuroReport 2005 Rao.pdf:pdf},
journal = {Neuroreport},
month = {nov},
number = {16},
pages = {1843},
title = {{Bayesian inference and attentional modulation in the visual cortex}},
volume = {16},
year = {2005}
}
@article{Poltoratski2021,
abstract = {Spatial processing by receptive fields is a core property of the visual system. However, it is unknown how spatial processing in high-level regions contributes to recognition behavior. As face inversion is thought to disrupt typical holistic processing of information in faces, we mapped population receptive fields (pRFs) with upright and inverted faces in the human visual system. Here we show that in face-selective regions, but not primary visual cortex, pRFs and overall visual field coverage are smaller and shifted downward in response to face inversion. From these measurements, we successfully predict the relative behavioral detriment of face inversion at different positions in the visual field. This correspondence between neural measurements and behavior demonstrates how spatial processing in face-selective regions may enable holistic perception. These results not only show that spatial processing in high-level visual regions is dynamically used towards recognition, but also suggest a powerful approach for bridging neural computations by receptive fields to behavior.},
author = {Poltoratski, Sonia and Kay, Kendrick and Finzi, Dawn and Grill-Spector, Kalanit},
file = {:Users/hahosoya/Documents/Mendeley Desktop/690d0aed1281d8ae3e17bffed877507a7f74b91e.pdf:pdf},
journal = {Nat. Commun.},
number = {1},
pages = {1--13},
pmid = {34362883},
title = {{Holistic face recognition is an emergent phenomenon of spatial processing in face-selective regions}},
volume = {12},
year = {2021}
}
@article{Zeiler2014,
abstract = {Abstract Large Convolutional Network models have recently demonstrated impressive classification performance on the ImageNet benchmark Krizhevsky et al.[18]. However there is no clear understanding of why they perform so well, or how they might be improved. In ...
},
author = {Zeiler, M D and Fergus, R},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2014 Zeiler.pdf:pdf},
journal = {Comput. vision–ECCV 2014},
month = {jan},
title = {{Visualizing and understanding convolutional networks}},
year = {2014}
}
@article{Kriegeskorte2018,
abstract = {Nature Neuroscience, doi:10.1038/s41593-018-0210-5},
author = {Kriegeskorte, Nikolaus and Douglas, Pamela K},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2F7C54D4-59B9-406A-A993-9A838BA6353F.pdf:pdf},
journal = {Nat. Publ. Gr.},
month = {aug},
number = {9},
pages = {1--13},
title = {{Cognitive computational neuroscience}},
volume = {21},
year = {2018}
}
@article{Chang2021,
abstract = {Understanding how the brain represents the identity of complex objects is a central challenge of visual neuroscience. The principles governing object processing have been extensively studied in the macaque face patch system, a sub-network of inferotemporal (IT) cortex specialized for face processing. A previous study reported that single face patch neurons encode axes of a generative model called the “active appearance” model, which transforms 50D feature vectors separately representing facial shape and facial texture into facial images. However, a systematic investigation comparing this model to other computational models, especially convolutional neural network models that have shown success in explaining neural responses in the ventral visual stream, has been lacking. Here, we recorded responses of cells in the most anterior face patch anterior medial (AM) to a large set of real face images and compared a large number of models for explaining neural responses. We found that the active appearance model better explained responses than any other model except CORnet-Z, a feedforward deep neural network trained on general object classification to classify non-face images, whose performance it tied on some face image sets and exceeded on others. Surprisingly, deep neural networks trained specifically on facial identification did not explain neural responses well. A major reason is that units in the network, unlike neurons, are less modulated by face-related factors unrelated to facial identification, such as illumination.},
author = {Chang, Le and Egger, Bernhard and Vetter, Thomas and Tsao, Doris Y.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/45b95279ff27ac71e058d5feff07505593ad0ce5.pdf:pdf},
journal = {Curr. Biol.},
keywords = {computational model,electrophysiology,face processing,inferotemporal cortex,neural coding,primate vision},
number = {13},
pages = {2785--2795.e4},
pmid = {33951457},
title = {{Explaining face representation in the primate brain using different computational models}},
volume = {31},
year = {2021}
}
@article{Tenenbaum2000,
abstract = {Perceptual systems routinely separate "content" from "style," classifying familiar words spoken in an unfamiliar accent, identifying a font or handwriting style across letters, or recognizing a familiar face or object seen under unfamiliar viewing conditions. Yet a general and tractable computational model of this ability to untangle the underlying factors of perceptual observations remains elusive (Hofstadter, 1985). Existing factor models (Mardia, Kent, & Bibby, 1979; Hinton & Zemel, 1994; Ghahramani, 1995; Bell & Sejnowski, 1995; Hinton, Dayan, Frey, & Neal, 1995; Dayan, Hinton, Neal, & Zemel, 1995; Hinton & Ghahramani, 1997) are either insufficiently rich to capture the complex interactions of perceptually meaningful factors such as phoneme and speaker accent or letter and font, or do not allow efficient learning algorithms. We present a general framework for learning to solve two-factor tasks using bilinear models, which provide sufficiently expressive representations of factor interactions but can nonetheless be fit to data using efficient algorithms based on the singular value decomposition and expectation-maximization. We report promising results on three different tasks in three different perceptual domains: spoken vowel classification with a benchmark multi-speaker database, extrapolation of fonts to unseen letters, and translation of faces to novel illuminants.},
address = {Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge 02139, USA.},
author = {Tenenbaum, J B and Freeman, W T},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neural Computation 2000 Tenenbaum.pdf:pdf},
journal = {Neural Comput.},
month = {jun},
number = {6},
pages = {1247--1283},
title = {{Separating style and content with bilinear models.}},
volume = {12},
year = {2000}
}
@article{Park2017,
abstract = {Neuron, 95 (2017) 971-986. doi:10.1016/j.neuron.2017.07.014},
author = {Park, Soo Hyun and Russ, Brian E and McMahon, David B T and Koyano, Kenji W and Berman, Rebecca A and Leopold, David A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/5B25C25B-7C92-4026-8247-7993CC367797.pdf:pdf},
journal = {Neuron},
month = {aug},
number = {4},
pages = {971--981.e5},
title = {{Functional Subpopulations of Neurons in a Macaque Face Patch Revealed by Single-Unit fMRI Mapping}},
volume = {95},
year = {2017}
}
@article{Schwartz2006,
abstract = {Gaussian scale mixture models offer a top-down description of signal generation that captures key bottom-up statistical characteristics of filter responses to images. However, the pattern of dependence among the filters for this class of models is prespecified. We propose a novel extension to the gaussian scale mixture model that learns the pattern of dependence from observed inputs and thereby induces a hierarchical representation of these inputs. Specifically, we propose that inputs are generated by gaussian variables (modeling local filter structure), multiplied by a mixer variable that is assigned probabilistically to each input from a set of possible mixers. We demonstrate inference of both components of the generative model, for synthesized data and for different classes of natural images, such as a generic ensemble and faces. For natural images, the mixer variable assignments show invariances resembling those of complex cells in visual cortex; the statistics of the gaussian components of the model are in accord with the outputs of divisive normalization models. We also show how our model helps interrelate a wide range of models of image statistics and cortical processing.},
address = {Howard Hughes Medical Institute, Computational Neurobiology Lab, Salk Institute for Biological Studies, La Jolla, CA 92037, USA. odelia@salk.edu},
author = {Schwartz, Odelia and Sejnowski, Terrence J and Dayan, Peter},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neural Computation 2006 Schwartz.pdf:pdf},
journal = {Neural Comput.},
month = {nov},
number = {11},
pages = {2680--2718},
title = {{Soft mixer assignment in a hierarchical generative model of natural scene statistics.}},
volume = {18},
year = {2006}
}
@article{Rezende2014,
abstract = {Abstract: We marry ideas from deep neural networks and approximate Bayesian inference to derive a generalised class of deep, directed generative models, endowed with a new algorithm for scalable inference and learning. Our algorithm introduces a recognition model to represent approximate posterior distributions, and that acts as a stochastic encoder of the data. We develop stochastic back-propagation--rules for back-propagation through ...
},
author = {Rezende, D J and Mohamed, S and Wierstra, D},
file = {:Users/hahosoya/Documents/Mendeley Desktop/arXiv 2014 Rezende.pdf:pdf},
journal = {arXiv.org},
month = {jan},
title = {{Stochastic backpropagation and approximate inference in deep generative models}},
year = {2014}
}
@article{Goodale1992,
abstract = {Abstract Accumulating neuropsychological, electrophysiological and behavioural evidence suggests that the neural substrates of visual perception may be quite distinct from those underlying the visual control of actions. In other words, the set of object descriptions that ...
},
author = {Goodale, M A and Milner, A D},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Trends in Neurosciences 1992 Goodale.pdf:pdf},
journal = {Trends Neurosci.},
month = {jan},
title = {{Separate visual pathways for perception and action}},
year = {1992}
}
@inproceedings{pmlr-v70-munkhdalai17a,
abstract = {Neural networks have been successfully applied in applications with a large amount of labeled data. However, the task of rapid generalization on new concepts with small training data while preserving performances on previously learned ones still presents a significant challenge to neural network models. In this work, we introduce a novel meta learning method, Meta Networks (MetaNet), that learns a meta-level knowledge across tasks and shifts its inductive biases via fast parameterization for rapid generalization. When evaluated on Omniglot and Mini-ImageNet benchmarks, our MetaNet models achieve a near human-level performance and outperform the baseline approaches by up to 6\% accuracy. We demonstrate several appealing properties of MetaNet relating to generalization and continual learning.},
address = {International Convention Centre, Sydney, Australia},
author = {Munkhdalai, Tsendsuren and Yu, Hong},
booktitle = {Proc. 34th Int. Conf. Mach. Learn.},
editor = {Precup, Doina and Teh, Yee Whye},
file = {:Users/hahosoya/Documents/Mendeley Desktop/9aca853a4f7da3b2b521f2be42cf4834a16107aa.pdf:pdf},
pages = {2554--2563},
series = {Proceedings of Machine Learning Research},
title = {{Meta Networks}},
volume = {70},
year = {2017}
}
@article{Kreutz-Delgado2007,
abstract = {We present a hierarchical architecture and learning algorithm for visual recognition and other visual inference tasks such as imagination, re- construction of occluded images, and expectation-driven segmentation. Using properties of biological vision for guidance, we ...},
author = {Kreutz-Delgado, K},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neural Computation 2007 Kreutz-Delgado.pdf:pdf},
journal = {Neural Comput.},
month = {jan},
title = {{Visual recognition and inference using dynamic overcomplete sparse learning}},
year = {2007}
}
@article{Perrett1982,
abstract = {Of 497 single neurones recorded in the cortex in the fundus of the superior temporal sulcus (STS) of three alert rhesus monkeys, a population of at least 48 cells which were selectively responsive to faces had the following response properties: (1) The cells' responses to faces (real or projected, human or rhesus monkey) were two to ten times as large as those to gratings, simple geometrical stimuli or complex 3-D objects. (2) Neuronal responses to faces were excitatory, sustained and were time-locked to the stimulus presentation with a latency of between 80 and 160 ms. (3) The cells were unresponsive to auditory or tactile stimuli and to the sight of arousing or aversive stimuli. (4) The magnitude of the responses of 28 cells tested was relatively constant despite transformations, such as rotation, so that the face was inverted or horizontal, and alterations of colour, size or distance. (5) Rotation to profile substantially reduced the responses of 21 cells (31 tested). (6) Masking out or presenting parts of the face (i.e. eyes, mouth or hair) in isolation revealed that different cells responded to different features or subsets of features. (7) For several cells, responses to the normal organisation of cut-out or line-drawn facial features were significantly larger than to jumbled controls. These findings indicate that explanations in terms of arousal, emotional or motor reactions, simple visual feature sensitivity or receptive fields are insufficient to account for the selective responses to faces and face features observed in this population of STS neurones. It appears that these neurones are part of a system specialised to code for faces or features present in faces, and it is suggested that damage to this system is related to prosopagnosia, or difficulty in face recognition, in man and to the tameness and social disturbances which follow temporal lobe damage and are part of the Kl{\"{u}}ver-Bucy syndrome in the monkey.},
author = {Perrett, D I and Rolls, E T and Caan, W},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Exp Brain Res 1982 Perrett.pdf:pdf},
journal = {Exp. brain Res.},
month = {jan},
number = {3},
pages = {329--342},
title = {{Visual neurones responsive to faces in the monkey temporal cortex.}},
volume = {47},
year = {1982}
}
@article{VonMelchner2000,
abstract = {Abstract An unresolved issue in cortical development concerns the relative contributions of intrinsic and extrinsic factors to the functional specification of different cortical areas 1, 2, 3, 4. Ferrets in which retinal projections are redirected neonatally to the auditory thalamus 5  ...},
author = {{Von Melchner}, L and Pallas, S L and Sur, M},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature 2000 Von Melchner.pdf:pdf},
journal = {Nature},
month = {jan},
title = {{Visual behaviour mediated by retinal projections directed to the auditory pathway}},
year = {2000}
}
@article{Kim2015,
author = {Kim, Soyun and Dede, Adam J O and Hopkins, Ramona O and Squire, Larry R},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proceedings of the National Academy of Sciences 2015 Kim.pdf:pdf},
journal = {Proc. Natl. Acad. Sci.},
month = {apr},
number = {15},
pages = {4767--4772},
title = {{Memory, scene construction, and the human hippocampus}},
volume = {112},
year = {2015}
}
@article{Ravassard2013,
author = {Ravassard, P and Kees, A and Willers, B and Ho, D and Aharoni, D and Cushman, J and Aghajan, Z M and Mehta, M R},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Science 2013 Ravassard.pdf:pdf},
journal = {Science},
month = {jun},
number = {6138},
pages = {1342--1346},
title = {{Multisensory Control of Hippocampal Spatiotemporal Selectivity}},
volume = {340},
year = {2013}
}
@article{Yildirim2020,
abstract = {Vision not only detects and recognizes objects, but performs rich inferences about the underlying scene structure that causes the patterns of light we see. Inverting generative models, or “analysis-by-synthesis”, presents a possible solution, but its mechanistic implementations have typically been too slow for online perception, and their mapping to neural circuits remains unclear. Here we present a neurally plausible efficient inverse graphics model and test it in the domain of face recognition. The model is based on a deep neural network that learns to invert a three-dimensional face graphics program in a single fast feedforward pass. It explains human behavior qualitatively and quantitatively, including the classic “hollow face” illusion, and it maps directly onto a specialized face-processing circuit in the primate brain. The model fits both behavioral and neural data better than state-of-the-art computer vision models, and suggests an interpretable reverse-engineering account of how the brain transforms images into percepts.},
author = {Yildirim, Ilker and Belledonne, Mario and Freiwald, Winrich and Tenenbaum, Josh},
file = {:Users/hahosoya/Documents/Mendeley Desktop/d27c28b632753bfb72887cf95e1551ed73a5dd37.pdf:pdf},
journal = {Sci. Adv.},
number = {10},
pmid = {32181338},
title = {{Efficient inverse graphics in biological face processing}},
volume = {6},
year = {2020}
}
@article{Issa2018,
abstract = {Ventral visual stream neural responses are dynamic, even for static image presentations. However, dynamical neural models of visual cortex are lacking as most progress has been made modeling static, time-averaged responses. Here, we studied population neural dynamics during face detection across three cortical processing stages. Remarkably,$\sim$30 milliseconds after the initially evoked response, we found that neurons in intermediate level areas decreased their responses to typical configurations of their preferred face parts relative to their response for atypical configurations even while neurons in higher areas achieved and maintained a preference for typical configurations. These hierarchical neural dynamics were inconsistent with standard feedforward circuits. Rather, recurrent models computing prediction errors between stages captured the observed temporal signatures. This model of neural dynamics, which simply augments the standard feedforward model of online vision, suggests that neural responses to static images may encode top-down prediction errors in addition to bottom-up feature estimates.},
author = {Issa, Elias B. and Cadieu, Charles F. and Dicarlo, James J.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/elife-42870-v2.pdf:pdf},
journal = {Elife},
number = {1},
pages = {1--24},
title = {{Neural dynamics at successive stages of the ventral visual stream are consistent with hierarchical error signals}},
volume = {7},
year = {2018}
}
@article{Aitchison2017,
abstract = {Two theoretical ideas have emerged recently with the ambition to provide a unifying functional explanation of neural population coding and dynamics: predictive coding and Bayesian inference. Here, we describe the two theories and their combination into a single framework: Bayesian predictive coding. We clarify how the two theories can be distinguished, despite sharing core computational concepts and addressing an overlapping set of empirical phenomena. We argue that predictive coding is an algorithmic/representational motif that can serve several different computational goals of which Bayesian inference is but one. Conversely, while Bayesian inference can utilize predictive coding, it can also be realized by a variety of other representations. We critically evaluate the experimental evidence supporting Bayesian predictive coding and discuss how to test it more directly.},
address = {Computational & Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, United Kingdom.},
author = {Aitchison, Laurence and Lengyel, M{\'{a}}t{\'{e}}},
file = {:Users/hahosoya/Documents/Mendeley Desktop/D49FE868-B44D-45CA-AABF-688A2973770F.pdf:pdf},
journal = {Curr. Opin. Neurobiol.},
month = {oct},
pages = {219--227},
title = {{With or without you: predictive coding and Bayesian inference in the brain.}},
volume = {46},
year = {2017}
}
@article{Ban2013,
author = {Ban, H and Yamamoto, H and Hanakawa, T and Urayama, S i and Aso, T and Fukuyama, H and Ejima, Y},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2013 Ban.pdf:pdf},
journal = {J. Neurosci.},
month = {oct},
number = {43},
pages = {16992--17007},
title = {{Topographic Representation of an Occluded Object and the Effects of Spatiotemporal Context in Human Early Visual Areas}},
volume = {33},
year = {2013}
}
@article{Edlund2011,
author = {Edlund, Jeffrey A and Chaumont, Nicolas and Hintze, Arend and Koch, Christof and Tononi, Giulio and Adami, Christoph},
editor = {Graham, Lyle J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/PLoS Comp Biol 2011 Edlund.pdf:pdf},
journal = {PLoS Comput. Biol.},
month = {oct},
number = {10},
pages = {e1002236},
title = {{Integrated Information Increases with Fitness in the Evolution of Animats}},
volume = {7},
year = {2011}
}
@article{Eickenberg2012,
abstract = {The human visual system is capable of recognizing complex objects even when their appearances change drastically under various viewing conditions. Especially in the higher cortical areas, the sensory neurons reflect such functional capacity in their selectivity for complex visual features and invariance to certain object transformations, such as image translation. Due to the strong nonlinearities necessary to achieve both the selectivity and invariance, characterizing and predicting the response patterns of these neurons represents a formidable computational challenge. A related problem is that such neurons are poorly driven by randomized inputs, such as white noise, and respond strongly only to stimuli with complex high-order correlations, such as natural stimuli. Here we describe a novel two-step optimization technique that can characterize both the shape selectivity and the range and coarseness of position invariance from neural responses to natural stimuli. One step in the optimization is finding the template as the maximally informative dimension given the estimated spatial location where the response could have been triggered within each image. The estimates of the locations that triggered the response are updated in the next step. Under the assumption of a monotonic relationship between the firing rate and stimulus projections on the template at a given position, the most likely location is the one that has the largest projection on the estimate of the template. The algorithm shows quick convergence during optimization, and the estimation results are reliable even in the regime of small signal-to-noise ratios. When we apply the algorithm to responses of complex cells in the primary visual cortex (V1) to natural movies, we find that responses of the majority of cells were significantly better described by translation-invariant models based on one template compared with position-specific models with several relevant features.},
address = {Computational Neurobiology Laboratory and Crick-Jacobs Center for Theoretical and Computational Biology, Salk Institute for Biological Studies, La Jolla, CA 92037, U.S.A., and Center for Theoretical Biological Physics, University of California, San Diego,},
author = {Eickenberg, Michael and Rowekamp, Ryan J and Kouh, Minjoon and Sharpee, Tatyana O},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neural Computation 2012 Eickenberg.pdf:pdf},
journal = {Neural Comput.},
month = {sep},
number = {9},
pages = {2384--2421},
title = {{Characterizing responses of translation-invariant neurons to natural stimuli: maximally informative invariant dimensions.}},
volume = {24},
year = {2012}
}
@article{Burkhalter1997,
abstract = {Abstract Feedback connections from extrastriate cortex to primary visual cortex (V1) in the primate may provide “top-down” information that plays a role in visual attention and object recognition. Our work in a rodent model of corticocortical circuitry demonstrates that  ...},
author = {Burkhalter, A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/The Journal of neuroscience 1997 Burkhalter.pdf:pdf},
journal = {J. Neurosci.},
month = {jan},
title = {{A polysynaptic feedback circuit in rat visual cortex}},
year = {1997}
}
@article{Golledge2004,
abstract = {Cognitive maps are mental representations of spatial and conceptual relationships in an environment, and are critical for flexible behavior. To form these abstract maps, the hippocampus has to learn to separate or merge aliased observations appropriately in different contexts in a manner that enables generalization and efficient planning. Here we propose a specific higher-order graph structure, clone-structured cognitive graph (CSCG), which forms clones of an observation for different contexts as a representation that addresses these problems. CSCGs can be learned efficiently using a probabilistic sequence model that is inherently robust to uncertainty. We show that CSCGs can explain a variety of cognitive map phenomena such as discovering spatial relations from aliased sensations, transitive inference between disjoint episodes, and formation of transferable schemas. Learning different clones for different contexts explains the emergence of splitter cells observed in maze navigation and event-specific responses in lap-running experiments. Moreover, learning and inference dynamics of CSCGs offer a coherent explanation for disparate place cell remapping phenomena. By lifting aliased observations into a hidden space, CSCGs reveal latent modularity useful for hierarchical abstraction and planning. Altogether, CSCG provides a simple unifying framework for understanding hippocampal function, and could be a pathway for forming relational abstractions in artificial intelligence.},
author = {George, Dileep and Rikhye, Rajeev V. and Gothoskar, Nishad and Guntupalli, J. Swaroop and Dedieu, Antoine and L{\'{a}}zaro-Gredilla, Miguel},
file = {:Users/hahosoya/Documents/Mendeley Desktop/73f45488d8d17a23d9ca0bf764cf9496720b4455.pdf:pdf},
isbn = {9780123693983},
journal = {Nat. Commun.},
month = {dec},
number = {1},
pages = {2392},
title = {{Clone-structured graph representations enable flexible learning and vicarious evaluation of cognitive maps}},
volume = {12},
year = {2021}
}
@article{Frey1996,
author = {Frey, B J and Hinton, G E and Dayan, P},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Advances in neural information processing systems 1996 Frey.pdf:pdf},
journal = {Adv. Neural Inf. Process. Syst.},
month = {jan},
pages = {661--670},
title = {{Does the wake-sleep algorithm produce good density estimators?}},
year = {1996}
}
@article{DeHaas2016,
author = {de Haas, B and Schwarzkopf, D S and Alvarez, I and Lawson, R P and Henriksson, L and Kriegeskorte, N and Rees, G},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2016 de Haas.pdf:pdf},
journal = {J. Neurosci.},
month = {sep},
number = {36},
pages = {9289--9302},
title = {{Perception and Processing of Faces in the Human Brain Is Tuned to Typical Feature Locations}},
volume = {36},
year = {2016}
}
@article{Adams2018,
abstract = {Subjects with a diagnosis of schizophrenia (Scz) overweight unexpected evidence in probabilistic inference: such evidence becomes “aberrantly salient.” A neurobiological explanation for this effect is that diminished synaptic gain (e.g., hypofunction of cortical NMDARs) in Scz destabilizes quasi-stable neuronal network states (or “attractors”). This attractor instability account predicts that (1) Scz would overweight unexpected evidence but underweight consistent evidence, (2) belief updating would be more vulnerable to stochastic fluctuations in neural activity, and (3) these effects would correlate. Hierarchical Bayesian belief updating models were tested in two independent datasets (n = 80 male and n = 167 female) comprising human subjects with Scz, and both clinical and nonclinical controls (some tested when unwell and on recovery) performing the “probability estimates” version of the beads task (a probabilistic inference task). Models with a standard learning rate, or including a parameter increasing updating to “disconfirmatory evidence,” or a parameter encoding belief instability were formally compared. The “belief instability” model (based on the principles of attractor dynamics) had most evidence in all groups in both datasets. Two of four parameters differed between Scz and nonclinical controls in each dataset: belief instability and response stochasticity. These parameters correlated in both datasets. Furthermore, the clinical controls showed similar parameter distributions to Scz when unwell, but were no different from controls once recovered. These findings are consistent with the hypothesis that attractor network instability contributes to belief updating abnormalities in Scz, and suggest that similar changes may exist during acute illness in other psychiatric conditions.},
author = {Adams, Rick A. and Napier, Gary and Roiser, Jonathan P. and Mathys, Christoph and Gilleen, James},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Adams et al. - 2018 - Attractor-like Dynamics in Belief Updating in Schi.pdf:pdf},
journal = {J. Neurosci.},
keywords = {Attractor model,Bayesian,Beads task,Disconfirmatory bias,Psychosis,Schizophrenia},
number = {44},
pages = {9471--9485},
title = {{Attractor-like dynamics in belief updating in schizophrenia}},
volume = {38},
year = {2018}
}
@article{Fukushima1980,
abstract = {A neural network model for a mechanism of visual pattern recognition is proposed in this paper. The network is self-organized by "learning without a teacher", and acquires an ability to recognize stimulus patterns based on the geometrical similarity (Gestalt) of their shapes without affected by their positions. This network is given a nickname "neocognitron". After completion of self-organization, the network has a structure similar to the hierarchy model of the visual nervous system proposed by Hubel and Wiesel. The network consists of an input layer (photoreceptor array) followed by a cascade connection of a number of modular structures, each of which is composed of two layers of cells connected in a cascade. The first layer of each module consists of "S-cells", which show characteristics similar to simple cells or lower order hypercomplex cells, and the second layer consists of "C-cells" similar to complex cells or higher order hypercomplex cells. The afferent synapses to each S-cell have plasticity and are modifiable. The network has an ability of unsupervised learning: We do not need any "teacher" during the process of self-organization, and it is only needed to present a set of stimulus patterns repeatedly to the input layer of the network. The network has been simulated on a digital computer. After repetitive presentation of a set of stimulus patterns, each stimulus pattern has become to elicit an output only from one of the C-cells of the last layer, and conversely, this C-cell has become selectively responsive only to that stimulus pattern. That is, none of the C-cells of the last layer responds to more than one stimulus pattern. The response of the C-cells of the last layer is not affected by the pattern's position at all. Neither is it affected by a small change in shape nor in size of the stimulus pattern.},
author = {Fukushima, K},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Biological Cybernetics 1980 Fukushima.pdf:pdf},
journal = {Biol. Cybern.},
month = {jan},
number = {4},
pages = {193--202},
title = {{Neocognitron: a self organizing neural network model for a mechanism of pattern recognition unaffected by shift in position.}},
volume = {36},
year = {1980}
}
@article{Mutch2008,
abstract = {Abstract We investigate the role of sparsity and localized features in a biologically-inspired model of visual object classification. As in the model of Serre, Wolf, and Poggio, we first apply Gabor filters at all positions and scales; feature complexity and position/scale ... 
},
author = {Mutch, Jim and Lowe, David G},
file = {:Users/hahosoya/Documents/Mendeley Desktop/International journal on machine learning 2008 Mutch.pdf:pdf},
journal = {Int. J. Comput. Vis.},
month = {jan},
number = {1},
pages = {45--57},
title = {{Object Class Recognition and Localization Using Sparse Features with Limited Receptive Fields}},
volume = {80},
year = {2008}
}
@article{Liu2016,
author = {Liu, Lu and She, Liang and Chen, Ming and Liu, Tianyi and Lu, Haidong D and Dan, Yang and Poo, Mu-ming},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proceedings of the National Academy of Sciences 2016 Liu.pdf:pdf},
journal = {Proc. Natl. Acad. Sci.},
month = {feb},
number = {7},
pages = {1913--1918},
title = {{Spatial structure of neuronal receptive field in awake monkey secondary visual cortex (V2)}},
volume = {113},
year = {2016}
}
@article{Rainer2004,
abstract = {One of the most remarkable capabilities of the adult brain is its ability to learn and continuously 
adapt to an ever-changing environment. While many studies have documented how learning
improves the perception and identification of visual stimuli, relatively little is known about  ...},
author = {Rainer, G and et Al},
file = {:Users/hahosoya/Documents/Mendeley Desktop/PLoS Biology 2004 Rainer.pdf:pdf},
journal = {PLoS Biol.},
month = {jan},
title = {{The effect of learning on the function of monkey extrastriate visual cortex}},
year = {2004}
}
@article{Stettler2002,
abstract = {Two components of cortical circuits could mediate contour integration in primary visual cortex (V1): intrinsic horizontal connections and feedback from higher cortical areas. To distinguish between these, we combined functional mapping with a new technique for labeling axons, a recombinant adenovirus bearing the gene for green fluorescent protein (GFP), to determine the extent, density, and orientation specificity of V1 intrinsic connections and V2 to V1 feedback. Both connections cover portions of V1 representing regions of visual space up to eight times larger than receptive fields as classically defined, though the intrinsic connections are an order of magnitude denser than the feedback. Whereas the intrinsic connections link similarly oriented domains in V1, V2 to V1 feedback displays no such specificity. These findings suggest that V1 intrinsic horizontal connections provide a more likely substrate for contour integration.},
address = {The Rockefeller University, 1230 York Avenue, New York, NY 10021, USA.},
author = {Stettler, Dan D and Das, Aniruddha and Bennett, Jean and Gilbert, Charles D},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neuron 2002 Stettler.pdf:pdf},
journal = {Neuron},
month = {nov},
number = {4},
pages = {739--750},
title = {{Lateral connectivity and contextual interactions in macaque primary visual cortex.}},
volume = {36},
year = {2002}
}
@article{Barlow1961,
author = {Barlow, Horace B},
file = {:Users/hahosoya/Documents/Mendeley Desktop/1961 Barlow.pdf:pdf},
journal = {Sens. Commun.},
month = {jan},
pages = {217--234},
title = {{Possible principles underlying the transformation of sensory messages}},
year = {1961}
}
@article{Baek2021,
abstract = {Face-selective neurons are observed in the primate visual pathway and are considered as the basis of face detection in the brain. However, it has been debated as to whether this neuronal selectivity can arise innately or whether it requires training from visual experience. Here, using a hierarchical deep neural network model of the ventral visual stream, we suggest a mechanism in which face-selectivity arises in the complete absence of training. We found that units selective to faces emerge robustly in randomly initialized networks and that these units reproduce many characteristics observed in monkeys. This innate selectivity also enables the untrained network to perform face-detection tasks. Intriguingly, we observed that units selective to various non-face objects can also arise innately in untrained networks. Our results imply that the random feedforward connections in early, untrained deep neural networks may be sufficient for initializing primitive visual selectivity.},
author = {Baek, Seungdae and Song, Min and Jang, Jaeson and Kim, Gwangsu and Paik, Se-Bum},
file = {:Users/hahosoya/Documents/Mendeley Desktop/a620fa046323f4598d647ee079dff1966b53ab8e.pdf:pdf},
isbn = {4146702127},
journal = {Nat. Commun.},
number = {1},
title = {{Face detection in untrained deep neural networks}},
volume = {12},
year = {2021}
}
@article{VanHooser2005,
annote = {Table 1 shows which species have orientation maps},
author = {{Van Hooser}, S D},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2005 Van Hooser.pdf:pdf},
journal = {J. Neurosci.},
month = {jan},
number = {1},
pages = {19--28},
title = {{Orientation Selectivity without Orientation Maps in Visual Cortex of a Highly Visual Mammal}},
volume = {25},
year = {2005}
}
@article{Serre2007,
abstract = {... A feedforward  architecture  accounts for rapid  categorization . ... Here we show that a specific implementation of a class of feedforward theories of object recognition (that extend the Hubel and Wiesel simple-to-complex cell hierarchy and account for many anatomical ... 
},
author = {Serre, T and Oliva, A and Poggio, T},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proceedings of the National Academy of Sciences 2007 Serre.pdf:pdf},
journal = {Proc. Natl. Acad. Sci.},
month = {apr},
number = {15},
pages = {6424--6429},
title = {{A feedforward architecture accounts for rapid categorization}},
volume = {104},
year = {2007}
}
@article{Dobbins1998,
author = {Dobbins, A C},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Science 1998 Dobbins.pdf:pdf},
journal = {Science},
month = {jul},
number = {5376},
pages = {552--555},
title = {{Distance Modulation of Neural Activity in the Visual Cortex}},
volume = {281},
year = {1998}
}
@article{Breveglieri2012,
abstract = {Eye position signals are pivotal in the visuomotor transformations performed by the posterior parietal cortex (PPC), but to date there are few studies addressing the influence of vergence angle upon single PPC neurons. In the present study, we investigated the influence on single neurons of the medial PPC area V6A of vergence and version signals. Single-unit activity was recorded from V6A in two Macaca fascicularis fixating real targets in darkness. The fixation targets were placed at eye level and at different vergence and version angles within the peripersonal space. Few neurons were modulated by version or vergence only, while the majority of cells were affected by both signals. We advance here the hypothesis that gaze-modulated V6A cells are able to encode gazed positions in the three-dimensional space. In single cells, version and vergence influenced the discharge with variable time course. In several cases, the two gaze variables influence neural discharges during only a part of the fixation time, but, more often, their influence persisted through large parts of it. Cells discharging for the first 400-500 ms of fixation could signal the arrival of gaze (and/or of spotlight of attention) in a new position in the peripersonal space. Cells showing a more sustained activity during the fixation period could better signal the location in space of the gazed objects. Both signals are critical for the control of upcoming or ongoing arm movements, such as those needed to reach and grasp objects located in the peripersonal space.},
address = {Department of Human and General Physiology, University of Bologna, 40126 Bologna, Italy.},
author = {Breveglieri, Rossella and Hadjidimitrakis, Kostas and Bosco, Annalisa and Sabatini, Silvio P and Galletti, Claudio and Fattori, Patrizia},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2012 Breveglieri.pdf:pdf},
journal = {J. Neurosci.},
month = {jan},
number = {1},
pages = {159--169},
title = {{Eye position encoding in three-dimensional space: integration of version and vergence signals in the medial posterior parietal cortex.}},
volume = {32},
year = {2012}
}
@article{Handwerker2012,
abstract = {NeuroImage, 63 (2012) 1712-1719. doi:10.1016/j.neuroimage.2012.06.078},
author = {Handwerker, Daniel A and Roopchansingh, Vinai and Gonzalez-Castillo, Javier and Bandettini, Peter A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/NeuroImage 2012 Handwerker.pdf:pdf},
journal = {Neuroimage},
month = {nov},
number = {3},
pages = {1712--1719},
title = {{Periodic changes in fMRI connectivity}},
volume = {63},
year = {2012}
}
@article{Goyal2019,
abstract = {Learning modular structures which reflect the dynamics of the environment can lead to better generalization and robustness to changes which only affect a few of the underlying causes. We propose Recurrent Independent Mechanisms (RIMs), a new recurrent architecture in which multiple groups of recurrent cells operate with nearly independent transition dynamics, communicate only sparingly through the bottleneck of attention, and are only updated at time steps where they are most relevant. We show that this leads to specialization amongst the RIMs, which in turn allows for dramatically improved generalization on tasks where some factors of variation differ systematically between training and evaluation.},
archivePrefix = {arXiv},
arxivId = {1909.10893},
author = {Goyal, Anirudh and Lamb, Alex and Hoffmann, Jordan and Sodhani, Shagun and Levine, Sergey and Bengio, Yoshua and Sch{\"{o}}lkopf, Bernhard},
eprint = {1909.10893},
file = {:Users/hahosoya/Documents/Mendeley Desktop/af99d60ec6ead325ad25b88eb4f4496d6acf9961.pdf:pdf},
journal = {arXiv},
pages = {1--35},
title = {{Recurrent independent mechanisms}},
year = {2019}
}
@article{Aparicio2016,
author = {Aparicio, Paul L and Issa, Elias B and DiCarlo, James J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2016 Aparicio.pdf:pdf},
journal = {J. Neurosci.},
month = {dec},
number = {50},
pages = {12729--12745},
title = {{Neurophysiological Organization of the Middle Face Patch in Macaque Inferior Temporal Cortex}},
volume = {36},
year = {2016}
}
@article{Kietzmann2019,
abstract = {The human visual system is an intricate network of brain regions that enables us to recognize the world around us. Despite its abundant lateral and feedback connections, object processing is commonly viewed and studied as a feedforward process. Here, we measure and model the rapid representational dynamics across multiple stages of the human ventral stream using time-resolved brain imaging and deep learning. We observe substantial representational transformations during the first 300 ms of processing within and across ventral-stream regions. Categorical divisions emerge in sequence, cascading forward and in reverse across regions, and Granger causality analysis suggests bidirectional information flow between regions. Finally, recurrent deep neural network models clearly outperform parameter-matched feedforward models in terms of their ability to capture the multiregion cortical dynamics. Targeted virtual cooling experiments on the recurrent deep network models further substantiate the importance of their lateral and top-down connections. These results establish that recurrent models are required to understand information processing in the human ventral stream.},
author = {Kietzmann, Tim C. and Spoerer, Courtney J. and S{\"{o}}rensen, Lynn K.A. and Cichy, Radoslaw M. and Hauk, Olaf and Kriegeskorte, Nikolaus},
file = {:Users/hahosoya/Documents/Mendeley Desktop/21854.full.pdf:pdf},
journal = {Proc. Natl. Acad. Sci. U. S. A.},
keywords = {Deep recurrent neural networks,Dynamics,Magnetoencephalography,Object recognition,Representational,Virtual cooling},
number = {43},
pages = {21854--21863},
title = {{Recurrence is required to capture the representational dynamics of the human visual system}},
volume = {116},
year = {2019}
}
@article{Atick1993,
abstract = {An unsupervised developmental algorithm for linear maps is derived which reduces the pixel-entropy (using the measure introduced in previous work) at every update and thus removes pairwise correlations between pixels. Since the measure of pixel-entropy has only a global minimum the algorithm is guaranteed to converge to the minimum entropy map. Such optimal maps have recently been shown to possess cognitively desirable properties and are likely to be used by the nervous system to organize sensory information. The algorithm derived here turns out to be one proposed by Goodall for pairwise decorrelation. It is biologically plausible since in a neural network implementation it requires only data available locally to a neuron. In training over ensembles of two-dimensional input signals with the same spatial power spectrum as natural scenes, networks develop output neurons with center-surround receptive fields similar to those of ganglion cells in the retina. Some technical issues pertinent to developmental al...},
author = {Atick, Joseph J and Redlich, A Norman},
journal = {Neural Comput.},
month = {jan},
number = {1},
pages = {45--60},
title = {{Convergent Algorithm for Sensory Receptive Field Development}},
volume = {5},
year = {1993}
}
@article{Barranca2014,
author = {Barranca, Victor J and Kova{\v{c}}i{\v{c}}, Gregor and Zhou, Douglas and Cai, David},
editor = {Sporns, Olaf},
file = {:Users/hahosoya/Documents/Mendeley Desktop/PLoS Comp Biol 2014 Barranca.pdf:pdf},
journal = {PLoS Comput. Biol.},
month = {aug},
number = {8},
pages = {e1003793},
title = {{Sparsity and Compressed Coding in Sensory Systems}},
volume = {10},
year = {2014}
}
@article{Crair1998,
abstract = {The role of experience in the development of the cerebral cortex has long been controversial. Patterned visual experience in the cat begins when the eyes open about a week after birth. Cortical maps for orientation and ocular dominance in the primary visual cortex of cats were found to be present by 2 weeks. Early pattern vision appeared unimportant because these cortical maps developed identically until nearly 3 weeks of age, whether or not the eyes were open. The na{\"{i}}ve maps were powerfully dominated by the contralateral eye, and experience was needed for responses to the other eye to become strong, a process unlikely to be strictly Hebbian. With continued visual deprivation, responses to both eyes deteriorated, with a time course parallel to the well-known critical period of cortical plasticity. The basic structure of cortical maps is therefore innate, but experience is essential for specific features of these maps, as well as for maintaining the responsiveness and selectivity of cortical neurons.},
address = {W. M. Keck Foundation Center for Integrative Neuroscience, Department of Physiology, University of California, San Francisco, CA 94143, USA.},
author = {Crair, M C and Gillespie, D C and Stryker, M P},
journal = {Science},
month = {jan},
number = {5350},
pages = {566--570},
title = {{The role of visual experience in the development of columns in cat visual cortex.}},
volume = {279},
year = {1998}
}
@article{OSullivan2015,
author = {O'Sullivan, J A and Shamma, S A and Lalor, E C},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2015 O'Sullivan.pdf:pdf},
journal = {J. Neurosci.},
month = {may},
number = {18},
pages = {7256--7263},
title = {{Evidence for Neural Computations of Temporal Coherence in an Auditory Scene and Their Enhancement during Active Listening}},
volume = {35},
year = {2015}
}
@article{Pinto2009,
abstract = {While many models of biological object recognition share a common set of "broad-stroke" properties, the performance of any one model depends strongly on the choice of parameters in a particular instantiation of that model--e.g., the number of units per layer, the size of pooling kernels, exponents in normalization operations, etc. Since the number of such parameters (explicit or implicit) is typically large and the computational cost of evaluating one particular parameter set is high, the space of possible model instantiations goes largely unexplored. Thus, when a model fails to approach the abilities of biological visual systems, we are left uncertain whether this failure is because we are missing a fundamental idea or because the correct "parts" have not been tuned correctly, assembled at sufficient scale, or provided with enough training. Here, we present a high-throughput approach to the exploration of such parameter sets, leveraging recent advances in stream processing hardware (high-end NVIDIA graphic cards and the PlayStation 3's IBM Cell Processor). In analogy to high-throughput screening approaches in molecular biology and genetics, we explored thousands of potential network architectures and parameter instantiations, screening those that show promising object recognition performance for further analysis. We show that this approach can yield significant, reproducible gains in performance across an array of basic object recognition tasks, consistently outperforming a variety of state-of-the-art purpose-built vision systems from the literature. As the scale of available computational power continues to expand, we argue that this approach has the potential to greatly accelerate progress in both artificial vision and our understanding of the computational underpinning of biological vision.},
address = {McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachussetts, USA. pinto@mit.edu},
author = {Pinto, Nicolas and Doukhan, David and DiCarlo, James J and Cox, David D},
file = {:Users/hahosoya/Documents/Mendeley Desktop/PLoS Comp Biol 2009 Pinto.pdf:pdf},
journal = {PLoS Comput. Biol.},
month = {nov},
number = {11},
pages = {e1000579},
title = {{A high-throughput screening approach to discovering good forms of biologically inspired visual representation.}},
volume = {5},
year = {2009}
}
@article{Rajalingham2018,
author = {Rajalingham, Rishi and Issa, Elias B and Bashivan, Pouya and Kar, Kohitij and Schmidt, Kailyn and DiCarlo, James J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/4E36F293-1F42-4B03-B56A-1C927EE0A417.pdf:pdf},
journal = {J. Neurosci.},
month = {aug},
number = {33},
pages = {7255--7269},
title = {{Large-Scale, High-Resolution Comparison of the Core Visual Object Recognition Behavior of Humans, Monkeys, and State-of-the-Art Deep Artificial Neural Networks}},
volume = {38},
year = {2018}
}
@article{Khemakhem2019,
abstract = {The framework of variational autoencoders allows us to efficiently learn deep latent-variable models, such that the model's marginal distribution over observed variables fits the data. Often, we're interested in going a step further, and want to approximate the true joint distribution over observed and latent variables, including the true prior and posterior distributions over latent variables. This is known to be generally impossible due to unidentifiability of the model. We address this issue by showing that for a broad family of deep latent-variable models, identification of the true joint distribution over observed and latent variables is actually possible up to very simple transformations, thus achieving a principled and powerful form of disentanglement. Our result requires a factorized prior distribution over the latent variables that is conditioned on an additionally observed variable, such as a class label or almost any other observation. We build on recent developments in nonlinear ICA, which we extend to the case with noisy, undercomplete or discrete observations, integrated in a maximum likelihood framework. The result also trivially contains identifiable flow-based generative models as a special case.},
archivePrefix = {arXiv},
arxivId = {1907.04809},
author = {Khemakhem, Ilyes and Kingma, Diederik P. and Monti, Ricardo Pio and Hyv{\"{a}}rinen, Aapo},
eprint = {1907.04809},
file = {:Users/hahosoya/Documents/Mendeley Desktop/f445a0ccf8700db1748f2c309493906c21491cea.pdf:pdf},
number = {2015},
title = {{Variational Autoencoders and Nonlinear ICA: A Unifying Framework}},
volume = {108},
year = {2019}
}
@article{Kingma2016,
abstract = {Abstract The framework of normalizing flows provides a general strategy for flexible variational inference of posteriors over latent variables. We propose a new type of normalizing flow, inverse autoregressive flow (IAF), that, in contrast to earlier published flows, scales well to high-dimensional latent spaces. The proposed flow consists of a chain of invertible transformations, where each transformation is based on an autoregressive ...
},
author = {Kingma, D P and Salimans, T and Jozefowicz, R},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Advances in neural {\ldots} 2016 Kingma.pdf:pdf},
journal = {Adv. neural  {\ldots}},
month = {jan},
title = {{Improved variational inference with inverse autoregressive flow}},
year = {2016}
}
@article{Tootell1998,
abstract = {We used high-field (3T) functional magnetic resonance imaging (fMRI) to label cortical activity due to visual spatial attention, relative to flattened cortical maps of the retinotopy and visual areas from the same human subjects. In the main task, the visual stimulus remained constant, but covert visual spatial attention was varied in both location and load. In each of the extrastriate retinotopic areas, we found MR increases at the representations of the ...
},
author = {Tootell, R B H and Hadjikhani, N and Hall, E K and Marrett, S},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neuron 1998 Tootell.pdf:pdf},
journal = {Neuron},
month = {jan},
title = {{The retinotopy of visual spatial attention}},
year = {1998}
}
@article{Tian2019,
abstract = {Humans view the world through many sensory channels, e.g., the long-wavelength light channel, viewed by the left eye, or the high-frequency vibrations channel, heard by the right ear. Each view is noisy and incomplete, but important factors, such as physics, geometry, and semantics, tend to be shared between all views (e.g., a "dog" can be seen, heard, and felt). We investigate the classic hypothesis that a powerful representation is one that models view-invariant factors. We study this hypothesis under the framework of multiview contrastive learning, where we learn a representation that aims to maximize mutual information between different views of the same scene but is otherwise compact. Our approach scales to any number of views, and is view-agnostic. We analyze key properties of the approach that make it work, finding that the contrastive loss outperforms a popular alternative based on cross-view prediction, and that the more views we learn from, the better the resulting representation captures underlying scene semantics. Our approach achieves state-of-the-art results on image and video unsupervised learning benchmarks. Code is released at: http://github.com/HobbitLong/CMC/.},
archivePrefix = {arXiv},
arxivId = {1906.05849},
author = {Tian, Yonglong and Krishnan, Dilip and Isola, Phillip},
eprint = {1906.05849},
file = {:Users/hahosoya/Documents/Mendeley Desktop/196a4d52caeb8c151b69287b0e8acda8060e4685.pdf:pdf},
title = {{Contrastive Multiview Coding}},
year = {2019}
}
@article{Baylis2001,
abstract = {We assessed how the visual shape preferences of neurons in the inferior temporal cortex of awake, behaving monkeys generalized across three different stimulus transformations. Stimulus-preferences of particular cells among different polygon displays were correlated across reversed contrast polarity or mirror reversal, but not across figure-ground reversal. This corresponds with psychological findings on human shape judgments. Our results imply that neurons in inferior temporal cortex respond to components of visual shape derived only after figure-ground assignment of contours, not to the contours themselves.},
address = {University of Plymouth, Plymouth Institute of Neuroscience, 12 Kirkby Place, Plymouth, PL4 8AA, UK. gordon@pion.ac.uk},
author = {Baylis, G C and Driver, J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature neuroscience 2001 Baylis-1.pdf:pdf},
journal = {Nat. Neurosci.},
month = {sep},
number = {9},
pages = {937--942},
title = {{Shape-coding in IT cells generalizes over contrast and mirror reversal, but not figure-ground reversal.}},
volume = {4},
year = {2001}
}
@article{Schiltz2006,
author = {Schiltz, Christine and Rossion, Bruno},
file = {:Users/hahosoya/Documents/Mendeley Desktop/NeuroImage 2006 Schiltz.pdf:pdf},
journal = {Neuroimage},
month = {sep},
number = {3},
pages = {1385--1394},
title = {{Faces are represented holistically in the human occipito-temporal cortex}},
volume = {32},
year = {2006}
}
@article{Macke2010,
author = {Macke, J H and Opper, M and Bethge, M},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2010 Macke.pdf:pdf},
journal = {arXiv Prepr. arXiv1009.2855},
month = {jan},
title = {{An analytically tractable model of neural population activity in the presence of common input explains higher-order correlations and entropy}},
year = {2010}
}
@article{Mobahi2009,
abstract = {... This work proposes a learning method for deep architectures that takes advantage of sequential 
data, in ... from the tem- poral coherence that naturally exists in un- labeled video recordings. ... We
demonstrate the effectiveness of this method on some pose invariant object and face ...
},
author = {Mobahi, H and et Al},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proceedings of the 26th Annual {\ldots} 2009 Mobahi.pdf:pdf},
journal = {Proc. 26th Annu.  {\ldots}},
month = {jan},
title = {{Deep learning from temporal coherence in video}},
year = {2009}
}
@article{Kay2008,
abstract = {A challenging goal in neuroscience is to be able to read out, or decode, mental content from brain activity. Recent functional magnetic resonance imaging (fMRI) studies have decoded orientation, position and object category from activity in visual cortex. However, these studies typically used relatively simple stimuli (for example, gratings) or images drawn from fixed categories (for example, faces, houses), and decoding was based on previous measurements of brain activity evoked by those same stimuli or categories. To overcome these limitations, here we develop a decoding method based on quantitative receptive-field models that characterize the relationship between visual stimuli and fMRI activity in early visual areas. These models describe the tuning of individual voxels for space, orientation and spatial frequency, and are estimated directly from responses evoked by natural images. We show that these receptive-field models make it possible to identify, from a large set of completely novel natural images, which specific image was seen by an observer. Identification is not a mere consequence of the retinotopic organization of visual areas; simpler receptive-field models that describe only spatial tuning yield much poorer identification performance. Our results suggest that it may soon be possible to reconstruct a picture of a person's visual experience from measurements of brain activity alone.},
address = {Department of Psychology, University of California, Berkeley, California 94720, USA.},
author = {Kay, Kendrick N and Naselaris, Thomas and Prenger, Ryan J and Gallant, Jack L},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature 2008 Kay.pdf:pdf},
journal = {Nature},
month = {mar},
number = {7185},
pages = {352--355},
title = {{Identifying natural images from human brain activity.}},
volume = {452},
year = {2008}
}
@article{Cross2021,
abstract = {Humans possess an exceptional aptitude to efficiently make decisions from high-dimensional sensory observations. However, it is unknown how the brain compactly represents the current state of the environment to guide this process. The deep Q-network (DQN) achieves this by capturing highly nonlinear mappings from multivariate inputs to the values of potential actions. We deployed DQN as a model of brain activity and behavior in participants playing three Atari video games during fMRI. Hidden layers of DQN exhibited a striking resemblance to voxel activity in a distributed sensorimotor network, extending throughout the dorsal visual pathway into posterior parietal cortex. Neural state-space representations emerged from nonlinear transformations of the pixel space bridging perception to action and reward. These transformations reshape axes to reflect relevant high-level features and strip away information about task-irrelevant sensory features. Our findings shed light on the neural encoding of task representations for decision-making in real-world situations.},
author = {Cross, Logan and Cockburn, Jeff and Yue, Yisong and O'Doherty, John P.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/1-s2.0-S0896627320308990-main.pdf:pdf},
journal = {Neuron},
keywords = {computational neuroscience,decision-making,deep reinforcement learning,fMRI,naturalistic task},
number = {4},
pages = {724--738.e7},
pmid = {33326755},
title = {{Using deep reinforcement learning to reveal how the brain encodes abstract state-space representations in high-dimensional environments}},
volume = {109},
year = {2021}
}
@article{Darlington2018,
abstract = {Nature Neuroscience, doi:10.1038/s41593-018-0233-y},
author = {Darlington, Timothy R and Beck, Jeffrey M and Lisberger, Stephen G},
file = {:Users/hahosoya/Documents/Mendeley Desktop/F2696C35-50CB-4C0D-9168-08C25AB6762D.pdf:pdf},
journal = {Nat. Neurosci.},
month = {sep},
number = {10},
pages = {1--14},
title = {{Neural implementation of Bayesian inference in a sensorimotor behavior}},
volume = {21},
year = {2018}
}
@article{Olshausen2008,
author = {Olshausen, Bruno A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Advances in neural information processing systems 2008 Olshausen.pdf:pdf},
journal = {Adv. Neural Inf. Process. Syst.},
month = {jan},
pages = {505--512},
title = {{Learning horizontal connections in a sparse coding model of natural images}},
year = {2008}
}
@article{Spratling2010,
abstract = {A simple model is shown to account for a large range of V1 classical, and nonclassical, receptive field properties including orientation tuning, spatial and temporal frequency tuning, cross-orientation suppression, surround suppression, and facilitation and inhibition by flankers and textured surrounds. The model is an implementation of the predictive coding theory of cortical function and thus provides a single computational explanation for a diverse range of neurophysiological findings. Furthermore, since predictive coding can be related to the biased competition theory and is a specific example of more general theories of hierarchical perceptual inference, the current results relate V1 response properties to a wider, more unified, framework for understanding cortical function.},
address = {Division of Engineering, King's College London, London, United Kingdom. michael.spratling@kcl.ac.uk},
author = {Spratling, Michael W},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2010 Spratling.pdf:pdf},
journal = {J. Neurosci.},
month = {mar},
number = {9},
pages = {3531--3543},
title = {{Predictive coding as a model of response properties in cortical area V1.}},
volume = {30},
year = {2010}
}
@article{Cadieu2012,
abstract = {We present a model of intermediate-level visual representation that is based on learning invariances from movies of the natural environment. The model is composed of two stages of processing: an early feature representation layer and a second layer in which invariances are explicitly represented. Invariances are learned as the result of factoring apart the temporally stable and dynamic components embedded in the early feature representation. The structure contained in these components is made explicit in the activities of second-layer units that capture invariances in both form and motion. When trained on natural movies, the first layer produces a factorization, or separation, of image content into a temporally persistent part representing local edge structure and a dynamic part representing local motion structure, consistent with known response properties in early visual cortex (area V1). This factorization linearizes statistical dependencies among the first-layer units, making them learnable by the second layer. The second-layer units are split into two populations according to the factorization in the first layer. The form-selective units receive their input from the temporally persistent part (local edge structure) and after training result in a diverse set of higher-order shape features consisting of extended contours, multiscale edges, textures, and texture boundaries. The motion-selective units receive their input from the dynamic part (local motion structure) and after training result in a representation of image translation over different spatial scales and directions, in addition to more complex deformations. These representations provide a rich description of dynamic natural images and testable hypotheses regarding intermediate-level representation in visual cortex.},
address = {Redwood Center for Theoretical Neuroscience, Helen Wills Neuroscience Institute, and School of Optometry, University of California, Berkeley, Berkeley, CA 94720, USA. cadieu@berkeley.edu},
author = {Cadieu, Charles F and Olshausen, Bruno A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neural Computation 2012 Cadieu.pdf:pdf},
journal = {Neural Comput.},
month = {apr},
number = {4},
pages = {827--866},
title = {{Learning intermediate-level representations of form and motion from natural movies.}},
volume = {24},
year = {2012}
}
@article{Leibo2015,
author = {Leibo, Joel Z and Liao, Qianli and Anselmi, Fabio and Poggio, Tomaso},
editor = {Kriegeskorte, Nikolaus},
file = {:Users/hahosoya/Documents/Mendeley Desktop/PLoS Comp Biol 2015 Leibo.pdf:pdf},
journal = {PLoS Comput. Biol.},
month = {oct},
number = {10},
pages = {e1004390},
title = {{The Invariance Hypothesis Implies Domain-Specific Regions in Visual Cortex}},
volume = {11},
year = {2015}
}
@article{Hoyer2003,
abstract = {Interpreting Neural Response Variability as Monte Carlo Sampling of the Posterior Patrik O. 
Hover* and Aapo Hyvarinen Neural Networks Research Centre Helsinki University of Technology
PO Box 9800. FIN-02015 HUT. Finland http://www. cis. hut. fi/phoyer/ patrik. hoyer@ hut. fi  ...},
author = {Hoyer, Patrik O and Hyv{\"{a}}rinen, Aapo},
file = {:Users/hahosoya/Documents/Mendeley Desktop/{\ldots} Systems 15 Proceedings of the 2002 {\ldots} 2003 Hoyer.pdf:pdf},
journal = {{\ldots}  Syst. 15 Proc. 2002  {\ldots}},
month = {jan},
title = {{Interpreting neural response variability as Monte Carlo sampling of the posterior}},
year = {2003}
}
@article{Schwiedrzik2017,
abstract = {Neuron, 96 (2017) 89-102. doi:10.1016/j.neuron.2017.09.007},
author = {Schwiedrzik, Caspar M and Freiwald, Winrich A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/566084C8-3D53-45BC-A6E2-41C083FEDA86.pdf:pdf},
journal = {Neuron},
month = {sep},
number = {1},
pages = {89--97.e5},
title = {{High-Level Prediction Signals in a Low-Level Area of the Macaque Face-Processing Hierarchy}},
volume = {96},
year = {2017}
}
@article{Erdogan2015,
abstract = {The world is multimodal. 1 We sense our environments using inputs from multiple sensory modalities. Similarly, digital information is increasingly available through multiple media. In this extended abstract, we present a general computational framework for understanding multimodal learning and perception that builds on the analysis-by-synthesis approach [2, 3]. The analysis-by-synthesis approach makes use of generative models describing how ...
},
author = {Erdogan, G and Yildirim, I and Jacobs, R A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2015 Erdogan.pdf:pdf},
month = {jan},
title = {{An Analysis-By-Synthesis Approach to Multisensory Object Shape Perception}},
year = {2015}
}
@article{Koyano2021,
abstract = {The visual perception of identity in humans and other primates is thought to draw upon cortical areas specialized for the analysis of facial structure. A prominent theory of face recognition holds that the brain computes and stores average facial structure, which it then uses to efficiently determine individual identity, though the neural mechanisms underlying this process are controversial. Here, we demonstrate that the dynamic suppression of average facial structure plays a prominent role in the responses of neurons in three fMRI-defined face patches of the macaque. Using photorealistic face stimuli that systematically varied in identity level according to a psychophysically based face space, we found that single units in the AF, AM, and ML face patches exhibited robust tuning around average facial structure. This tuning emerged after the initial excitatory response to the face and was expressed as the selective suppression of sustained responses to low-identity faces. The coincidence of this suppression with increased spike timing synchrony across the population suggests a mechanism of active inhibition underlying this effect. Control experiments confirmed that the diminished responses to low-identity faces were not due to short-term adaptation processes. We propose that the brain's neural suppression of average facial structure facilitates recognition by promoting the extraction of distinctive facial characteristics and suppressing redundant or irrelevant responses across the population.},
author = {Koyano, Kenji W. and Jones, Adam P. and McMahon, David B.T. and Waidmann, Elena N. and Russ, Brian E. and Leopold, David A.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/IdentityTuning_Dynamics_CurrBiol2021.pdf:pdf;:Users/hahosoya/Documents/Mendeley Desktop/mmc1(2).pdf:pdf},
journal = {Curr. Biol.},
keywords = {delayed response,face,normalization,primate,vision},
number = {1},
pages = {1--12.e5},
pmid = {33065012},
title = {{Dynamic Suppression of Average Facial Structure Shapes Neural Tuning in Three Macaque Face Patches}},
volume = {31},
year = {2021}
}
@article{Lamme1998,
abstract = {The cortical visual system consists of many richly interconnected areas. Each area is characterized by more or less specific receptive field tuning properties. However, these tuning properties reflect only a subset of the interactions that occur within and between ...
},
author = {Lamme, V A F and Super, H and Spekreijse, H},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Curr. Opin. Neurobiol. 1998 Lamme.pdf:pdf},
journal = {Curr. Opin. Neurobiol.},
month = {jan},
title = {{Feedforward, horizontal, and feedback processing in the visual cortex}},
year = {1998}
}
@article{Saremi2013a,
author = {Saremi, S and Sejnowski, T J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proceedings of the National Academy of Sciences 2013 Saremi.pdf:pdf},
journal = {Proc. Natl. Acad. Sci.},
month = {feb},
title = {{Hierarchical model of natural images and the origin of scale invariance}},
year = {2013}
}
@article{Belhumeur1996,
author = {Belhumeur, Peter N},
file = {:Users/hahosoya/Documents/Mendeley Desktop/International journal on machine learning 1996 Belhumeur.pdf:pdf},
journal = {Int. J. Comput. Vis.},
month = {jan},
number = {3},
pages = {237--260},
title = {{A Bayesian approach to binocular steropsis}},
volume = {19},
year = {1996}
}
@article{Goris2014,
abstract = {Responses of sensory neurons differ across repeated measurements. This variability is usually treated as stochasticity arising within neurons or neural circuits. However, some portion of the variability arises from fluctuations in excitability due to factors that are not purely sensory, such as arousal, attention and adaptation. To isolate these fluctuations, we developed a model in which spikes are generated by a Poisson process whose rate is the product of a drive that is sensory in origin and a gain summarizing stimulus-independent modulatory influences on excitability. This model provides an accurate account of response distributions of visual neurons in macaque lateral geniculate nucleus and cortical areas V1, V2 and MT, revealing that variability originates in large part from excitability fluctuations that are correlated over time and between neurons, and that increase in strength along the visual pathway. The model provides a parsimonious explanation for observed systematic dependencies of response variability and covariability on firing rate.},
address = {Center for Neural Science, New York University, New York, New York, USA.},
author = {Goris, Robbe L T and Movshon, J Anthony and Simoncelli, Eero P},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature Publishing Group 2014 Goris.pdf:pdf},
journal = {Nat. Neurosci.},
month = {jun},
number = {6},
pages = {858--865},
title = {{Partitioning neuronal variability.}},
volume = {17},
year = {2014}
}
@article{Graham2010,
author = {Graham, D J and Redies, C},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Vision Research 2010 Graham.pdf:pdf},
journal = {Vision Res.},
month = {jan},
number = {16},
pages = {1503--1509},
title = {{Statistical regularities in art: Relations with visual coding and perception}},
volume = {50},
year = {2010}
}
@article{Cadieu2014,
author = {Cadieu, Charles F and Hong, Ha and Yamins, Daniel L K and Pinto, Nicolas and Ardila, Diego and Solomon, Ethan A and Majaj, Najib J and DiCarlo, James J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/PLoS Comp Biol 2014 Cadieu.PDF:PDF},
journal = {PLoS Comput. Biol.},
month = {dec},
number = {12},
pages = {e1003963},
title = {{Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition}},
volume = {10},
year = {2014}
}
@article{Butko,
author = {Butko, Nicholas J and Movellan, Javier R},
file = {:Users/hahosoya/Documents/Mendeley Desktop/IEEE Trans. Auton. Mental Dev. Butko.pdf:pdf},
journal = {IEEE Trans. Auton. Ment. Dev.},
number = {2},
pages = {91--107},
title = {{Infomax Control of Eye Movements}},
volume = {2}
}
@article{Baker2016,
author = {Baker, P M and Bair, W},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2016 Baker.pdf:pdf},
journal = {J. Neurosci.},
month = {jun},
number = {24},
pages = {6563--6582},
title = {{A Model of Binocular Motion Integration in MT Neurons}},
volume = {36},
year = {2016}
}
@article{Liu2020,
abstract = {The perception of color is an internal label for the inferred spectral reflectance of visible surfaces. To study how spectral representation is transformed through modular subsystems of successive cortical areas, we undertook simultaneous optical imaging of intrinsic signals in macaque V1, V2, and V4, supplemented by higher-resolution electrophysiology and two-photon imaging in awake macaques. We find a progressive evolution in the scale and precision of chromotopic maps, expressed by a uniform blob-like architecture of hue responses within each area. Two-photon imaging reveals enhanced hue-specific cell clustering in V2 compared with V1. A phenomenon of endspectral (red and blue) responses that is clear in V1, recedes in V2, and is virtually absent in V4. The increase in mid- and extra-spectral hue representations through V2 and V4 reflects the nature of hierarchical processing as higher areas read out locations in chromatic space from progressive integration of signals relayed by V1.},
author = {Liu, Ye and Li, Ming and Zhang, Xian and Lu, Yiliang and Gong, Hongliang and Yin, Jiapeng and Chen, Zheyuan and Qian, Liling and Yang, Yupeng and Andolina, Ian Max and Shipp, Stewart and Mcloughlin, Niall and Tang, Shiming and Wang, Wei},
file = {:Users/hahosoya/Documents/Mendeley Desktop/1-s2.0-S089662732030581X-main.pdf:pdf},
journal = {Neuron},
keywords = {2-photon calcium imaging,V1,V2,V4,color perception,cortical hue representation,intrinsic-signal optical imaging,macaque,visual cortex},
number = {3},
pages = {538--550.e5},
pmid = {32853551},
title = {{Hierarchical Representation for Chromatic Processing across Macaque V1, V2, and V4}},
volume = {108},
year = {2020}
}
@article{Wiskott2002,
author = {Wiskott, L and Sejnowski, T J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neural Computation 2002 Wiskott-2.pdf:pdf},
journal = {Neural Comput.},
month = {jan},
number = {4},
pages = {715--770},
title = {{Slow feature analysis: Unsupervised learning of invariances}},
volume = {14},
year = {2002}
}
@article{Schwartz2019,
abstract = {Place cells in the hippocampus (HC) are active when an animal visits a certain location (referred to as a place field) within an environment. Grid cells in the medial entorhinal cortex (MEC) respond at multiple locations, with firing fields that form a periodic and hexagonal tiling of the environment. The joint activity of grid and place cell populations, as a function of location, forms a neural code for space. In this article, we develop an understanding of the relationships between coding theoretically relevant properties of the combined activity of these populations and how these properties limit the robustness of this representation to noise-induced interference. These relationships are revisited by measuring the performances of biologically realizable algorithms implemented by networks of place and grid cell populations, as well as constraint neurons, which perform denoising operations. Contributions of this work include the investigation of coding theoretic limitations of the mammalian neural code for location and how communication between grid and place cell networks may improve the accuracy of each population's representation. Simulations demonstrate that denoising mechanisms analyzed here can significantly improve the fidelity of this neural representation of space. Furthermore, patterns observed in connectivity of each population of simulated cells predict that anti-Hebbian learning drives decreases in inter-HC-MEC connectivity along the dorsoventral axis.},
archivePrefix = {arXiv},
arxivId = {NIHMS150003},
author = {Schwartz, David M. and Koyluoglu, O. Ozan},
eprint = {NIHMS150003},
file = {:Users/hahosoya/Documents/Mendeley Desktop/neco_a_01208.pdf:pdf},
isbn = {0924-6703},
journal = {Neural Comput.},
month = {aug},
number = {8},
pages = {1519--1550},
pmid = {15772874},
title = {{On the Organization of Grid and Place Cells: Neural Denoising via Subspace Learning}},
volume = {31},
year = {2019}
}
@article{Desimone1984,
abstract = {Abstract Previous studies have reported that some neurons in the inferior temporal (IT) cortex respond selectively to highly specific complex objects. In the present study, we conducted the first systematic survey of the responses of IT neurons to both simple stimuli, ... 
},
author = {Desimone, R and Albright, T D and Gross, C G},
file = {:Users/hahosoya/Documents/Mendeley Desktop/The Journal of neuroscience 1984 Desimone.pdf:pdf},
journal = {J. Neurosci.},
month = {jan},
number = {8},
pages = {2051--2062},
title = {{Stimulus-selective properties of inferior temporal neurons in the macaque}},
volume = {4},
year = {1984}
}
@article{Song2014,
author = {Song, H Francis and Kennedy, Henry and Wang, Xiao-Jing},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proceedings of the National Academy of Sciences 2014 Song.pdf:pdf},
journal = {Proc. Natl. Acad. Sci.},
month = {nov},
number = {46},
pages = {16580--16585},
title = {{Spatial embedding of structural similarity in the cerebral cortex}},
volume = {111},
year = {2014}
}
@article{Fawcett2006,
abstract = {Receiver operating characteristics (ROC) graphs are useful for organizing classifiers and visualizing their performance. ROC graphs are commonly used in medical decision making, and in recent years have been used increasingly in machine learning and data mining research. Although ROC graphs are apparently simple, there are some common misconceptions and pitfalls when using them in practice. The purpose of this article is to serve as an introduction to ROC graphs and as a guide for using them in research. {\textcopyright} 2005 Elsevier B.V. All rights reserved.},
author = {Fawcett, Tom},
file = {:Users/hahosoya/Documents/Mendeley Desktop/745782902e97be8fbacd1e05d283f11104e2fec6.pdf:pdf},
journal = {Pattern Recognit. Lett.},
keywords = {Classifier evaluation,Evaluation metrics,ROC analysis},
number = {8},
pages = {861--874},
title = {{An introduction to ROC analysis}},
volume = {27},
year = {2006}
}
@inproceedings{Kingma2014,
abstract = {Abstract: How can we perform efficient inference and learning in directed probabilistic models, in the presence of continuous latent variables with intractable posterior distributions, and large datasets? We introduce a stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case. Our contributions is two-fold. First, we show that a reparameterization ...
},
author = {Kingma, D P and Welling, M},
booktitle = {Int. Conf. Learn. Represent.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proceedings of the 2nd International Conference on Learning Representations (ICLR) 2014 Kingma.pdf:pdf},
month = {jan},
title = {{Auto-encoding variational bayes}},
year = {2014}
}
@article{Nishimoto2006,
abstract = {Abstract We introduce a novel class of white-noise analyses, named local spectral reverse correlation (LSRC), which is capable of revealing various aspects of visual receptive field profiles that were undetectable previously in a single simple measurement. The method is  ...},
author = {Nishimoto, S and Ishida, T and Ohzawa, I},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2006 Nishimoto.pdf:pdf},
journal = {J. Neurosci.},
month = {mar},
number = {12},
pages = {3269--3280},
title = {{Receptive field properties of neurons in the early visual cortex revealed by local spectral reverse correlation}},
volume = {26},
year = {2006}
}
@article{Dotson2021,
abstract = {Navigation occurs through a continuum of space and time. The hippocampus is known to encode the immediate position of moving animals. However, active navigation, especially at high speeds, may require representing navigational information beyond the present moment. Using wireless electrophysiological recordings in freely flying bats, we demonstrate that neural activity in area CA1 predominantly encodes nonlocal spatial information up to meters away from the bat's present position. This spatiotemporal representation extends both forward and backward in time, with an emphasis on future locations, and is found during both random exploration and goal-directed navigation. The representation of position thus extends along a continuum, with each moment containing information about past, present, and future, and may provide a key mechanism for navigating along self-selected and remembered paths.},
author = {Dotson, Nicholas M. and Yartsev, Michael M.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/242.full.pdf:pdf},
journal = {Science},
number = {6551},
pages = {242--247},
pmid = {34244418},
title = {{Nonlocal spatiotemporal representation in the hippocampus of freely flying bats}},
volume = {373},
year = {2021}
}
@article{Movshon1985,
author = {Movshon, J Anthony and Adelson, Edward H and Gizzi, Matin S and Newsome, William T},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Pattern Recognition Mechanisms 1985 Movshon.pdf:pdf},
journal = {Pattern Recognit. Mech.},
month = {jun},
pages = {117--151},
title = {{The analysis of visual moving patterns}},
year = {1985}
}
@article{Rice2014,
author = {Rice, G E and Watson, D M and Hartley, T and Andrews, T J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2014 Rice.pdf:pdf},
journal = {J. Neurosci.},
month = {jun},
number = {26},
pages = {8837--8844},
title = {{Low-Level Image Properties of Visual Objects Predict Patterns of Neural Response across Category-Selective Regions of the Ventral Visual Pathway}},
volume = {34},
year = {2014}
}
@article{Balduzzi2009,
author = {Balduzzi, David and Tononi, Giulio},
editor = {Friston, Karl J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/PLoS Comp Biol 2009 Balduzzi.pdf:pdf},
journal = {PLoS Comput. Biol.},
month = {aug},
number = {8},
pages = {e1000462},
title = {{Qualia: The Geometry of Integrated Information}},
volume = {5},
year = {2009}
}
@article{Lieber2019,
author = {Lieber, Justin D and Bensmaia, Sliman J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/53177313-D45C-4152-9CF9-F378F78F124E.pdf:pdf},
journal = {Proc. Natl. Acad. Sci. U. S. A.},
month = {feb},
number = {8},
pages = {3268--3277},
title = {{High-dimensional representation of texture in somatosensory cortex of primates}},
volume = {116},
year = {2019}
}
@article{Ito1995,
abstract = {1. Object vision is largely invariant to changes of retinal images of objects in size and position. To reveal neuronal mechanisms of this invariance, we recorded activities from single cells in the anterior part of the inferotemporal cortex (anterior IT), determined the critical features for the activation of individual cells, and examined the effects of changes in stimulus size and position on the responses. 2. Twenty-one percent of the anterior IT cells studied here responded to ranges of size > 4 octaves, whereas 43% responded to size ranges < 2 octaves. The optimal stimulus size, measured by the distance between the outer edges along the longest axis of the stimulus, ranged from 1.7 to 30 degrees. 3. The selectivity for shape was mostly preserved over the entire range of effective size and over the receptive field, whereas some subtle but statistically significant changes were observed in one half of the cells studied here. 4. The size-specific responses observed in 43% of the cells are consistent with recent psychophysical data that suggest that images of objects are stored in a size-specific manner in the long-term memory. Both size-dependent and -independent processing of images may occur in anterior IT.},
address = {Laboratory for Neural Information Processing, Research Development Corporation of Japan, Saitama.},
author = {Ito, M and Tamura, H and Fujita, I and Tanaka, K},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neurophysiology 1995 Ito.pdf:pdf},
journal = {J. Neurophysiol.},
month = {jan},
number = {1},
pages = {218--226},
title = {{Size and position invariance of neuronal responses in monkey inferotemporal cortex.}},
volume = {73},
year = {1995}
}
@article{Laje2013,
abstract = {Nature Neuroscience 16, 925 (2013). doi:10.1038/nn.3405},
author = {Laje, Rodrigo and Buonomano, Dean V},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature Publishing Group 2013 Laje.pdf:pdf},
journal = {Nat. Neurosci.},
month = {may},
number = {7},
pages = {925--933},
title = {{Robust timing and motor patterns by taming chaos in recurrent neural networks}},
volume = {16},
year = {2013}
}
@article{Orban2008,
abstract = {Efficient and versatile processing of any hierarchically structured information requires a learning mechanism that combines lower-level features into higher-level chunks. We investigated this chunking mechanism in humans with a visual pattern-learning paradigm. We developed an ideal learner based on Bayesian model comparison that extracts and stores only those chunks of information that are minimally sufficient to encode a set of visual scenes. Our ideal Bayesian chunk learner not only reproduced the results of a large set of previous empirical findings in the domain of human pattern learning but also made a key prediction that we confirmed experimentally. In accordance with Bayesian learning but contrary to associative learning, human performance was well above chance when pair-wise statistics in the exemplars contained no relevant information. Thus, humans extract chunks from complex visual patterns by generating accurate yet economical representations and not by encoding the full correlational structure of the input.},
address = {Collegium Budapest Institute for Advanced Study, 2 Szenth{\'{a}}roms{\'{a}}g utca, Budapest H-1014, Hungary.},
author = {Orb{\'{a}}n, Gergo and Fiser, J{\'{o}}zsef and Aslin, Richard N and Lengyel, M{\'{a}}t{\'{e}}},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proceedings of the National Academy of Sciences 2008 Orb{\'{a}}n.pdf:pdf},
journal = {Proc. Natl. Acad. Sci.},
month = {feb},
number = {7},
pages = {2745--2750},
title = {{Bayesian learning of visual chunks by human observers.}},
volume = {105},
year = {2008}
}
@article{Gutmann2018,
author = {Gutmann, Michael U and Dutta, Ritabrata and Kaski, Samuel and Corander, Jukka},
file = {:Users/hahosoya/Documents/Mendeley Desktop/255AD394-41A0-41B9-8577-B83E98A6C369.pdf:pdf},
journal = {Stat. Comput.},
month = {jan},
number = {2},
pages = {411--425},
title = {{Likelihood-free inference via classification.}},
volume = {28},
year = {2018}
}
@article{Gawne2002a,
abstract = {We report here results from 45 primate V4 visual cortical neurons to the preattentive presentations of seven different patterns located in two separate areas of the same receptive field and to combinations of the patterns in the two locations. For many neurons, we could not determine any clear relationship for the responses to two simultaneous stimuli. However, for a substantial fraction of the neurons we found that the firing rate was well modeled as the maximum firing rate of each stimulus presented separately. It has previously been proposed that taking the maximum of the inputs ("MAX" operator) could be a useful operation for neurons in visual cortex, although there has until now been little direct physiological evidence for this hypothesis. Our results here provide direct support for the hypothesis that the MAX operator plays a significant (although certainly not exclusive) role in generating the receptive field properties of visual cortical neurons.},
address = {University of Alabama, Department of Physiological Optics, Birmingham, Alabama 35294, USA. Tgawne@icare.OPT.UAB.EDU},
author = {Gawne, Timothy J and Martin, Julie M},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neurophysiology 2002 Gawne-3.pdf:pdf},
journal = {J. Neurophysiol.},
month = {sep},
number = {3},
pages = {1128--1135},
title = {{Responses of primate visual cortical V4 neurons to simultaneously presented stimuli.}},
volume = {88},
year = {2002}
}
@article{Kiani2007,
author = {Kiani, R and Esteky, H and Mirpour, K and Tanaka, K},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neurophysiology 2007 Kiani.pdf:pdf},
journal = {J. Neurophysiol.},
month = {apr},
number = {6},
pages = {4296--4309},
title = {{Object Category Structure in Response Patterns of Neuronal Population in Monkey Inferior Temporal Cortex}},
volume = {97},
year = {2007}
}
@article{Li2010,
abstract = {...  Unsupervised  Natural  Visual  Experience  Rapidly Reshapes Size Invariant  Object Representation in Inferior Temporal Cortex . Nuo Li and James J. DiCarlo *. McGovern Institute for Brain Research, Department of Brain and Cognitive ... 
},
author = {Li, Nuo and DiCarlo, James J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neuron 2010 Li.pdf:pdf},
journal = {Neuron},
month = {sep},
number = {6},
pages = {1062--1075},
title = {{Unsupervised Natural Visual Experience Rapidly Reshapes Size-Invariant Object Representation in Inferior Temporal Cortex}},
volume = {67},
year = {2010}
}
@article{Pruszynski2014,
abstract = {Nature Neuroscience 17, 1404 (2014). doi:10.1038/nn.3804},
author = {Pruszynski, J Andrew and Johansson, Roland S},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature Publishing Group 2014 Pruszynski.pdf:pdf},
journal = {Nat. Neurosci.},
month = {aug},
number = {10},
pages = {1404--1409},
title = {{Edge-orientation processing in first-order tactile neurons}},
volume = {17},
year = {2014}
}
@article{Frith1999,
abstract = {Demonstrating that neural activity 'represents' physical properties of the world such as the orientation of a line in the receptive field of a nerve cell is a standard procedure in neuroscience. However, not all such neural activity will be associated with the mental representations that form the contents of consciousness. In some cases, such as when patients with blindsight correctly 'guess' the location of a stimulus, neural activity is associated with physical stimulation and with appropriate behaviour, but not with awareness. To identify the neural correlates of conscious experience we need to identify patterns of neural activity that are specifically associated with awareness. Experiments aimed at making such identifications require that subjects report some aspect of their conscious experience either verbally or through some pre-arranged non-verbal report while neural activity is measured. If there is some characteristic neural signature of consciousness, then this should be distinguishable from the kinds of neural activity associated with stimulation and/or behaviour in the absence of awareness. It remains to be seen whether the neural signature of consciousness relates to the location of the neural activity, the temporal properties of the neural activity or the form of the interaction between activity in different brain regions.},
address = {Wellcome Department of Cognitive Neurology, Institute of Neurology, 12 Queen Square, London, UK WC1N 3BG.},
author = {Frith, C and Perry, R and Lumer, E},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Trends in cognitive Sciences 1999 Frith.pdf:pdf},
journal = {Trends Cogn. Sci.},
month = {mar},
number = {3},
pages = {105--114},
title = {{The neural correlates of conscious experience: an experimental framework.}},
volume = {3},
year = {1999}
}
@article{Bogadhi2021,
abstract = {Recent fMRI experiments identified an attention-related region in the macaque temporal cortex, here called the floor of the superior temporal sulcus (fSTS), as the primary cortical target of superior colliculus (SC) activity. However, it remains unclear which aspects of attention are processed by fSTS neurons and how or why these might depend on SC activity. Here, we show that SC inactivation decreases attentional modulations in fSTS neurons by increasing their activity for ignored stimuli in addition to decreasing their activity for attended stimuli. Neurons in the fSTS also exhibit event-related activity during attention tasks linked to detection performance, and this link is eliminated during SC inactivation. Finally, fSTS neurons respond selectively to particular visual objects, and this selectivity is reduced markedly during SC inactivation. These diverse, high-level properties of fSTS neurons all involve visual signals that carry behavioral relevance. Their dependence on SC activity could reflect a circuit that prioritizes cortical processing of events detected subcortically. Bogadhi and Katz et al. determine how activity from the midbrain superior colliculus (SC) is necessary for expression of high-level visual properties—attention-related modulation, event detection activity, and object-selective responses—in a newly identified region of the temporal cortex (fSTS) in primates.},
author = {Bogadhi, Amarender R. and Katz, Leor N. and Bollimunta, Anil and Leopold, David A. and Krauzlis, Richard J.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/1-s2.0-S0896627320309284-main.pdf:pdf},
journal = {Neuron},
keywords = {attentional modulation,change detection,detection activity,object recognition,object selectivity,selective attention,subcortical pathways,superior colliculus,superior temporal sulcus,temporal cortex},
number = {4},
pages = {690--699.e5},
pmid = {33338395},
title = {{Midbrain activity shapes high-level visual properties in the primate temporal cortex}},
volume = {109},
year = {2021}
}
@article{Liu2013,
author = {Liu, Xiao and Duyn, Jeff H},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proc. Natl. Acad. Sci. U.S.A. 2013 Liu.pdf:pdf},
journal = {Proc. Natl. Acad. Sci. U. S. A.},
month = {jan},
number = {11},
pages = {4392--4397},
title = {{Time-varying functional network information extracted from brief instances of spontaneous brain activity}},
volume = {110},
year = {2013}
}
@article{LeCun1998,
abstract = {Abstract: Multilayer neural networks trained with the back-propagation algorithm constitute the best example of a successful gradient based learning technique. Given an appropriate network architecture, gradient - based learning algorithms can be used to synthesize a 
},
author = {LeCun, Y and Bottou, L and Bengio, Y},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proceedings of the IEEE 1998 LeCun.pdf:pdf},
journal = {Proc. IEEE},
month = {jan},
number = {11},
pages = {2278--2324},
title = {{Gradient-based learning applied to document recognition}},
volume = {86},
year = {1998}
}
@article{Lee1999a,
abstract = {Is perception of the whole based on perception of its parts? There is psychological and physiological evidence for parts-based representations in the brain, and certain computational theories of object recognition rely on such representations. But little is known about how brains or computers might learn the parts of objects. Here we demonstrate an algorithm for non-negative matrix factorization that is able to learn parts of faces and semantic features of text. This is in contrast to other methods, such as principal components analysis and vector quantization, that learn holistic, not parts-based, representations. Non-negative matrix factorization is distinguished from the other methods by its use of non-negativity constraints. These constraints lead to a parts-based representation because they allow only additive, not subtractive, combinations. When non-negative matrix factorization is implemented as a neural network, parts-based representations emerge by virtue of two properties: the firing rates of neurons are never negative and synaptic strengths do not change sign.},
address = {Bell Laboratories, Lucent Technologies, Murray Hill, New Jersey 07974, USA.},
author = {Lee, D D and Seung, H S},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature 1999 Lee.pdf:pdf},
journal = {Nature},
month = {oct},
number = {6755},
pages = {788--791},
title = {{Learning the parts of objects by non-negative matrix factorization.}},
volume = {401},
year = {1999}
}
@article{Yoon2016,
abstract = {Neuron, Corrected proof. doi:10.1016/j.neuron.2016.01.039},
author = {Yoon, KiJung and Lewallen, Sam and Kinkhabwala, Amina A and Tank, David W and Fiete, Ila R},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neuron 2016 Yoon.pdf:pdf},
journal = {Neuron},
month = {feb},
number = {5},
pages = {1--15},
title = {{Grid Cell Responses in 1D Environments Assessed as Slices through a 2D Lattice}},
volume = {89},
year = {2016}
}
@article{Khawaja2013,
author = {Khawaja, F A and Liu, L D and Pack, C C},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neurophysiology 2013 Khawaja.pdf:pdf},
journal = {J. Neurophysiol.},
month = {jul},
number = {1},
pages = {63--74},
title = {{Responses of MST neurons to plaid stimuli}},
volume = {110},
year = {2013}
}
@article{Laird2011,
abstract = {An increasingly large number of neuroimaging studies have investigated functionally connected networks during rest, providing insight into human brain architecture. Assessment of the functional qualities of resting state networks has been limited by the task-independent state, which results in an inability to relate these networks to specific mental functions. However, it was recently demonstrated that similar brain networks can be extracted from resting state data and data extracted from thousands of task-based neuroimaging experiments archived in the BrainMap database. Here, we present a full functional explication of these intrinsic connectivity networks at a standard low order decomposition using a neuroinformatics approach based on the BrainMap behavioral taxonomy as well as a stratified, data-driven ordering of cognitive processes. Our results serve as a resource for functional interpretations of brain networks in resting state studies and future investigations into mental operations and the tasks that drive them.},
address = {Research Imaging Institute, University of Texas Health Science Center San Antonio, 7703 Floyd Curl Drive, San Antonio, TX 78229-3900, USA. lairda@uthscsa.edu},
author = {Laird, Angela R and Fox, P Mickle and Eickhoff, Simon B and Turner, Jessica A and Ray, Kimberly L and McKay, D Reese and Glahn, David C and Beckmann, Christian F and Smith, Stephen M and Fox, Peter T},
file = {:Users/hahosoya/Documents/Mendeley Desktop/J Cogn Neurosci 2011 Laird.pdf:pdf},
journal = {J. Cogn. Neurosci.},
month = {dec},
number = {12},
pages = {4022--4037},
title = {{Behavioral interpretations of intrinsic connectivity networks.}},
volume = {23},
year = {2011}
}
@article{Banino2018,
author = {Banino, Andrea and Barry, Caswell and Uria, Benigno and Blundell, Charles and Lillicrap, Timothy and Mirowski, Piotr and Pritzel, Alexander and Chadwick, Martin J and Degris, Thomas and Modayil, Joseph and Wayne, Greg and Soyer, Hubert and Viola, Fabio and Zhang, Brian and Goroshin, Ross and Rabinowitz, Neil and Pascanu, Razvan and Beattie, Charlie and Petersen, Stig and Sadik, Amir and Gaffney, Stephen and King, Helen and Kavukcuoglu, Koray and Hassabis, Demis and Hadsell, Raia and Kumaran, Dharshan},
file = {:Users/hahosoya/Documents/Mendeley Desktop/8111DCD1-1A16-4E66-B336-46CF57E63036.pdf:pdf},
journal = {Nature},
month = {may},
number = {7705},
pages = {429--433},
title = {{Vector-based navigation using grid-like representations in artificial agents}},
volume = {557},
year = {2018}
}
@article{Adesnik2012,
abstract = {Nature 490, 226 (2012). doi:10.1038/nature11526},
author = {Adesnik, Hillel and Bruns, William and Taniguchi, Hiroki and Huang, Z Josh and Scanziani, Massimo},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature 2012 Adesnik.pdf:pdf},
journal = {Nature},
month = {oct},
number = {7419},
pages = {226--231},
title = {{A neural circuit for spatial summation in visual cortex}},
volume = {490},
year = {2012}
}
@article{Ponce2019,
abstract = {What specific features should visual neurons encode, given the infinity of real-world images and the limited number of neurons available to represent them? We investigated neuronal selectivity in monkey inferotemporal cortex via the vast hypothesis space of a generative deep neural network, avoiding assumptions about features or semantic categories. A genetic algorithm searched this space for stimuli that maximized neuronal firing. This led to the evolution of rich synthetic images of objects with complex combinations of shapes, colors, and textures, sometimes resembling animals or familiar people, other times revealing novel patterns that did not map to any clear semantic category. These results expand our conception of the dictionary of features encoded in the cortex, and the approach can potentially reveal the internal representations of any system whose input can be captured by a generative model. Neurons guided the evolution of their own best stimuli with a generative deep neural network.},
author = {Ponce, Carlos R. and Xiao, Will and Schade, Peter F. and Hartmann, Till S. and Kreiman, Gabriel and Livingstone, Margaret S.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/PIIS0092867419303915.pdf:pdf},
journal = {Cell},
keywords = {generative adversarial network,inferotemporal cortex,neural networks},
number = {4},
pages = {999--1009.e10},
pmid = {31051108},
title = {{Evolving Images for Visual Neurons Using a Deep Generative Network Reveals Coding Principles and Neuronal Preferences}},
volume = {177},
year = {2019}
}
@article{Rajan2013,
abstract = {Models of neural responses to stimuli with complex spatiotemporal correlation structure often assume that neurons are selective for only a small number of linear projections of a potentially high-dimensional input. In this review, we explore recent modeling approaches where the neural response depends on the quadratic form of the input rather than on its linear projection, that is, the neuron is sensitive to the local covariance structure of the signal preceding the spike. To infer this quadratic dependence in the presence of arbitrary (e.g., naturalistic) stimulus distribution, we review several inference methods, focusing in particular on two information theory-based approaches (maximization of stimulus energy and of noise entropy) and two likelihood-based approaches (Bayesian spike-triggered covariance and extensions of generalized linear models). We analyze the formal relationship between the likelihood-based and information-based approaches to demonstrate how they lead to consistent inference. We demonstrate the practical feasibility of these procedures by using model neurons responding to a flickering variance stimulus.},
address = {Joseph Henry Laboratories of Physics and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, U.S.A. krajan@princeton.edu.},
author = {Rajan, Kanaka and Marre, Olivier and Tka{\v{c}}ik, Ga{\v{s}}per},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neural Computation 2013 Rajan.pdf:pdf},
journal = {Neural Comput.},
month = {jul},
number = {7},
pages = {1661--1692},
title = {{Learning quadratic receptive fields from neural responses to natural stimuli.}},
volume = {25},
year = {2013}
}
@article{Logethetis1998,
abstract = {Conscious experience involves perceiving, attending, remembering, and recognizing. Recent neuroscientific research has made significant contributions to our understanding of the mechanisms that mediate such capacities. Physiological and neuropsychological ... 
},
author = {Logethetis, N},
journal = {Curr. Opin. Neurobiol.},
month = {jan},
title = {{ScienceDirect.com - Current Opinion in Neurobiology - Object vision and visual awareness}},
year = {1998}
}
@article{Bock2012,
abstract = {Nature 471, 177 (2011). doi:10.1038/nature09802},
author = {Bock, Davi D and Lee, Wei-Chung Allen and Kerlin, Aaron M and Andermann, Mark L and Hood, Greg and Wetzel, Arthur W and Yurgenson, Sergey and Soucy, Edward R and Kim, Hyon Suk and Reid, R Clay},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature 2012 Bock.pdf:pdf},
journal = {Nature},
month = {apr},
number = {7337},
pages = {177--182},
title = {{Network anatomy and in vivo physiology of visual cortical neurons}},
volume = {471},
year = {2012}
}
@article{Higgins2021,
abstract = {In order to better understand how the brain perceives faces, it is important to know what objective drives learning in the ventral visual stream. To answer this question, we model neural responses to faces in the macaque inferotemporal (IT) cortex with a deep self-supervised generative model, $\beta$-VAE, which disentangles sensory data into interpretable latent factors, such as gender or age. Our results demonstrate a strong correspondence between the generative factors discovered by $\beta$-VAE and those coded by single IT neurons, beyond that found for the baselines, including the handcrafted state-of-the-art model of face perception, the Active Appearance Model, and deep classifiers. Moreover, $\beta$-VAE is able to reconstruct novel face images using signals from just a handful of cells. Together our results imply that optimising the disentangling objective leads to representations that closely resemble those in the IT at the single unit level. This points at disentangling as a plausible learning objective for the visual brain.},
author = {Higgins, Irina and Chang, Le and Langston, Victoria and Hassabis, Demis and Summerfield, Christopher and Tsao, Doris and Botvinick, Matthew},
file = {:Users/hahosoya/Documents/Mendeley Desktop/61910ad0f56a6e5643fcf4e7849b89a7a79eb1e0.pdf:pdf},
journal = {Nat. Commun.},
number = {1},
pages = {1--14},
pmid = {34753913},
title = {{Unsupervised deep learning identifies semantic disentanglement in single inferotemporal face patch neurons}},
volume = {12},
year = {2021}
}
@article{Brefczynski1999,
abstract = {Here we identify a neural correlate of the ability to precisely direct visual attention to locations other than the center of gaze. Human subjects performed a task requiring shifts of visual attention (but not of gaze) from one location to the next within a dense array of targets and distracters while functional MRI was used to map corresponding displacements of neural activation within visual cortex. The cortical topography of the purely attention-driven activity precisely matched the topography of activity evoked by the cued targets when presented in isolation. Such retinotopic mapping of attention-related activation was found in primary visual cortex, as well as in dorsomedial and ventral occipital visual areas previously implicated in processing the attended target features. These results identify a physiological basis for the effects of spatially directed visual attention.},
address = {Department of Cellular Biology, Neurobiology and Anatomy, Medical College of Wisconsin, Milwaukee 53226, USA.},
author = {Brefczynski, J A and DeYoe, E A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature neuroscience 1999 Brefczynski.pdf:pdf},
journal = {Nat. Neurosci.},
month = {apr},
number = {4},
pages = {370--374},
title = {{A physiological correlate of the 'spotlight' of visual attention.}},
volume = {2},
year = {1999}
}
@article{Osindero2006,
abstract = {We present an energy-based model that uses a product of generalized Student-t distributions to capture the statistical structure in data sets. This model is inspired by and particularly applicable to "natural" data sets such as images. We begin by providing the mathematical framework, where we discuss complete and overcomplete models and provide algorithms for training these models from data. Using patches of natural scenes, we demonstrate that our approach represents a viable alternative to independent component analysis as an interpretive model of biological visual systems. Although the two approaches are similar in flavor, there are also important differences, particularly when the representations are overcomplete. By constraining the interactions within our model, we are also able to study the topographic organization of Gabor-like receptive fields that our model learns. Finally, we discuss the relation of our new approach to previous work--in particular, gaussian scale mixture models and variants of independent components analysis.},
address = {Department of Computer Science, University of Toronto, Toronto, Ontario, M5S 3G4, Canada. osindero@cs.toronto.edu},
author = {Osindero, Simon and Welling, Max and Hinton, Geoffrey E},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neural Computation 2006 Osindero.pdf:pdf},
journal = {Neural Comput.},
month = {feb},
number = {2},
pages = {381--414},
title = {{Topographic product models applied to natural scene statistics.}},
volume = {18},
year = {2006}
}
@article{Ito2004,
abstract = {Angles and junctions embedded within contours are important features to represent the shape of objects. To study the neuronal basis to extract these features, we conducted extracellular recordings while two macaque monkeys performed a fixation task. Angle stimuli were the combination of two straight half-lines larger than the size of the classical receptive fields (CRFs). Each line was drawn from the center to outside the CRFs in 1 of 12 directions, so that the stimuli passed through the CRFs and formed angles at the center of the CRFs. Of 114 neurons recorded from the superficial layer of area V2, 91 neurons showed selective responses to these angle stimuli. Of these, 41 neurons (36.0%) showed selective responses to wide angles between 60 degrees and 150 degrees that were distinct from responses to straight lines or sharp angles (30 degrees ). Responses were highly selective to a particular angle in approximately one-fourth of neurons. When we tested the selectivity of the same neurons to individual half-lines, the preferred direction was more or less consistent with one or two components of the optimal angle stimuli. These results suggest that the selectivity of the neurons depends on both the combination of two components and the responses to individual components. Angle-selective V2 neurons are unlikely to be specific angle detectors, because the magnitude of their responses to the optimal angle was indistinguishable from that to the optimal half-lines. We suggest that the extraction of information of angles embedded within contour stimuli may start in area V2.},
address = {Division of Sensory and Cognitive Information, Department of Information Physiology, National Institute for Physiological Sciences, Okazaki, Aichi 444-8585, Japan. minami@nips.ac.jp},
author = {Ito, Minami and Komatsu, Hidehiko},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2004 Ito.pdf:pdf},
journal = {J. Neurosci.},
month = {mar},
number = {13},
pages = {3313--3324},
title = {{Representation of angles embedded within contour stimuli in area V2 of macaque monkeys.}},
volume = {24},
year = {2004}
}
@article{Yoshida2020,
abstract = {Natural scenes sparsely activate neurons in the primary visual cortex (V1). However, how sparsely active neurons reliably represent complex natural images and how the information is optimally decoded from these representations have not been revealed. Using two-photon calcium imaging, we recorded visual responses to natural images from several hundred V1 neurons and reconstructed the images from neural activity in anesthetized and awake mice. A single natural image is linearly decodable from a surprisingly small number of highly responsive neurons, and the remaining neurons even degrade the decoding. Furthermore, these neurons reliably represent the image across trials, regardless of trial-to-trial response variability. Based on our results, diverse, partially overlapping receptive fields ensure sparse and reliable representation. We suggest that information is reliably represented while the corresponding neuronal patterns change across trials and collecting only the activity of highly responsive neurons is an optimal decoding strategy for the downstream neurons.},
author = {Yoshida, Takashi and Ohki, Kenichi},
file = {:Users/hahosoya/Documents/Mendeley Desktop/0d11084e446b99610cd5e2c1f40d815043d65035.pdf:pdf},
journal = {Nat. Commun.},
number = {1},
pmid = {32054847},
title = {{Natural images are reliably represented by sparse and variable populations of neurons in visual cortex}},
volume = {11},
year = {2020}
}
@article{Qiu2005,
author = {Qiu, Fangtu T and von der Heydt, R{\"{u}}diger},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neuron 2005 Qiu.pdf:pdf},
journal = {Neuron},
month = {jul},
number = {1},
pages = {155--166},
title = {{Figure and Ground in the Visual Cortex: V2 Combines Stereoscopic Cues with Gestalt Rules}},
volume = {47},
year = {2005}
}
@article{David2005,
abstract = {A model that fully describes the response properties of visual neurons must be able to predict their activity during natural vision. While many models have been proposed for the visual system, few have ever been tested against this criterion. To address this issue, we have developed a general framework for fitting and validating nonlinear models of visual neurons using natural visual stimuli. Our approach derives from linear spatiotemporal receptive field (STRF) analysis, which has frequently been used to study the visual system. However, prior to the linear filtering stage typical of STRFs, a linearizing transformation is applied to the stimulus to account for nonlinear response properties. We used this approach to compare two models for neurons in primary visual cortex: a nonlinear Fourier power model, which accounts for spatial phase invariant tuning, and a traditional linear model. We characterized prediction accuracy in terms of the total explainable variance, given intrinsic experimental noise. On average, Fourier power STRFs predicted 40% of explainable variance while linear STRFs were able to predict only 21% of explainable variance. The performance of the Fourier power model provides a benchmark for evaluating more sophisticated models in the future.},
address = {Group in Bioengineering, University of California, Berkeley 94720-1650, USA.},
author = {David, Stephen V and Gallant, Jack L},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Network Computation in Neural Systems 2005 David.pdf:pdf},
journal = {Netw. Comput. Neural Syst.},
month = {may},
number = {2-3},
pages = {239--260},
title = {{Predicting neuronal responses during natural vision.}},
volume = {16},
year = {2005}
}
@inproceedings{pmlr-v70-pritzel17a,
abstract = {Deep reinforcement learning methods attain super-human performance in a wide range of environments. Such methods are grossly inefficient, often taking orders of magnitudes more data than humans to achieve reasonable performance. We propose Neural Episodic Control: a deep reinforcement learning agent that is able to rapidly assimilate new experiences and act upon them. Our agent uses a semi-tabular representation of the value function: a buffer of past experience containing slowly changing state representations and rapidly updated estimates of the value function. We show across a wide range of environments that our agent learns significantly faster than other state-of-the-art, general purpose deep reinforcement learning agents.},
address = {International Convention Centre, Sydney, Australia},
author = {Pritzel, Alexander and Uria, Benigno and Srinivasan, Sriram and Badia, Adri{\`{a}} Puigdom{\`{e}}nech and Vinyals, Oriol and Hassabis, Demis and Wierstra, Daan and Blundell, Charles},
booktitle = {Proc. 34th Int. Conf. Mach. Learn.},
editor = {Precup, Doina and Teh, Yee Whye},
file = {:Users/hahosoya/Documents/Mendeley Desktop/00e1b1b6919097dc4d20fbabd1de6b5822ee9d07.pdf:pdf},
pages = {2827--2836},
series = {Proceedings of Machine Learning Research},
title = {{Neural Episodic Control}},
volume = {70},
year = {2017}
}
@article{Sasaki2010,
author = {Sasaki, K S and Sasaki, K S and Tabuchi, Y and Tabuchi, Y and Ohzawa, I},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2010 Sasaki.pdf:pdf},
journal = {J. Neurosci.},
month = {oct},
number = {41},
pages = {13826--13837},
title = {{Complex Cells in the Cat Striate Cortex Have Multiple Disparity Detectors in the Three-Dimensional Binocular Receptive Fields}},
volume = {30},
year = {2010}
}
@article{Lake2015,
abstract = {People learning new concepts can often generalize successfully from just a single example, yet machine learning algorithms typically require tens or hundreds of examples to perform with similar accuracy. People can also use learned concepts in richer ways than conventional algorithms-for action, imagination, and explanation. We present a computational model that captures these human learning abilities for a large class of simple visual concepts: handwritten characters from the world's alphabets. The model represents concepts as simple programs that best explain observed examples under a Bayesian criterion. On a challenging one-shot classification task, the model achieves human-level performance while outperforming recent deep learning approaches. We also present several "visual Turing tests" probing the model's creative generalization abilities, which in many cases are indistinguishable from human behavior.},
address = {Center for Data Science, New York University, 726 Broadway, New York, NY 10003, USA. brenden@nyu.edu.},
author = {Lake, Brenden M and Salakhutdinov, Ruslan and Tenenbaum, Joshua B},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Science 2015 Lake.pdf:pdf},
journal = {Science},
month = {dec},
number = {6266},
pages = {1332--1338},
title = {{Human-level concept learning through probabilistic program induction.}},
volume = {350},
year = {2015}
}
@article{Haxby1991,
abstract = {The existence and neuroanatomical locations of separate extrastriate visual pathways for object recognition and spatial localization were investigated in healthy young men. Regional cerebral blood flow was measured by positron emission tomography and bolus injections of H2(15)O, while subjects performed face matching, dot-location matching, or sensorimotor control tasks. Both visual matching tasks activated lateral occipital cortex. Face discrimination alone activated a region of occipitotemporal cortex that was anterior and inferior to the occipital area activated by both tasks. The spatial location task alone activated a region of lateral superior parietal cortex. Perisylvian and anterior temporal cortices were not activated by either task. These results demonstrate the existence of three functionally dissociable regions of human visual extrastriate cortex. The ventral and dorsal locations of the regions specialized for object recognition and spatial localization, respectively, suggest some homology between human and nonhuman primate extrastriate cortex, with displacement in human brain, possibly related to the evolution of phylogenetically newer cortical areas.},
address = {Laboratory of Neurosciences, National Institute on Aging, National Institutes of Health, Bethesda, MD 20892.},
author = {Haxby, J V and Grady, C L and Horwitz, B and Ungerleider, L G and Mishkin, M and Carson, R E and Herscovitch, P and Schapiro, M B and Rapoport, S I},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proc. Natl. Acad. Sci. U.S.A. 1991 Haxby.pdf:pdf},
journal = {Proc. Natl. Acad. Sci. U. S. A.},
month = {mar},
number = {5},
pages = {1621--1625},
title = {{Dissociation of object and spatial visual processing pathways in human extrastriate cortex.}},
volume = {88},
year = {1991}
}
@article{Virtanen2011,
author = {Virtanen, S and Klami, A and Kaski, S},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2011 Virtanen.pdf:pdf},
month = {jan},
title = {{Bayesian CCA via group sparsity}},
year = {2011}
}
@article{McKone2007,
author = {McKone, Elinor and Kanwisher, Nancy and Duchaine, Bradley C},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Trends in cognitive Sciences 2007 McKone.pdf:pdf},
journal = {Trends Cogn. Sci.},
month = {jan},
number = {1},
pages = {8--15},
title = {{Can generic expertise explain special processing for faces?}},
volume = {11},
year = {2007}
}
@article{Zhu2014,
author = {Zhu, Zhenyao and Luo, Ping and Wang, Xiaogang and Tang, Xiaoou},
file = {:Users/hahosoya/Documents/Mendeley Desktop/EEB5A0FC-ECB2-4616-8363-C85A43154CFD.pdf:pdf},
journal = {Adv. Neural Inf. Process. Syst.},
month = {jan},
title = {{Multi-View Perceptron - a Deep Model for Learning Face Identity and View Representations.}},
year = {2014}
}
@article{Graves2014,
abstract = {We extend the capabilities of neural networks by coupling them to external memory resources, which they can interact with by attentional processes. The combined system is analogous to a Turing Machine or Von Neumann architecture but is differentiable end-to-end, allowing it to be efficiently trained with gradient descent. Preliminary results demonstrate that Neural Turing Machines can infer simple algorithms such as copying, sorting, and associative recall from input and output examples.},
archivePrefix = {arXiv},
arxivId = {1410.5401},
author = {Graves, Alex and Wayne, Greg and Danihelka, Ivo},
eprint = {1410.5401},
file = {:Users/hahosoya/Documents/Mendeley Desktop/1410.5401.pdf:pdf},
month = {oct},
pages = {1--26},
title = {{Neural Turing Machines}},
year = {2014}
}
@article{Ballard1997,
abstract = {The receptive fields of neurons in the mammalian primary visual cortex are oriented not only in the domain of space, but in most cases, also in the domain of space-time. While the orientation of a receptive field in space determines the selectivity of the neuron to image structures at ...},
author = {Ballard, D H},
file = {:Users/hahosoya/Documents/Mendeley Desktop/University of Rochester 1997 Ballard.pdf:pdf},
journal = {Univ. Rochester},
month = {jan},
title = {{Efficient encoding of natural time varying images produces oriented space-time receptive fields}},
year = {1997}
}
@article{Zhang2016,
abstract = {Abstract Experiments reveal that in the dorsal medial superior temporal (MSTd) and the ventral intraparietal (VIP) areas, where visual and vestibular cues are integrated to infer heading direction, there are two types of neurons with roughly the same number. One is “congruent” cells, whose preferred heading directions are similar in response to visual and vestibular cues; and the other is “opposite” cells, whose preferred heading directions are ...
},
author = {Zhang, W H and Wang, H and Wong, K Y M and Wu, S},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Advances in neural {\ldots} 2016 Zhang.pdf:pdf},
journal = {Adv. neural  {\ldots}},
month = {jan},
title = {{“Congruent” and “Opposite” Neurons: Sisters for Multisensory Integration and Segregation}},
year = {2016}
}
@article{Hyvarinen2001b,
abstract = {In ordinary independent component analysis, the components are assumed to be completely independent , and they do not necessarily have any meaningful order relationships. In practice, however, the estimated “ independent ” components are often not  ...},
author = {Hyv{\"{a}}rinen, Aapo and Hoyer, P O and Inki, M},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neural Computation 2001 Hyv{\"{a}}rinen.pdf:pdf},
journal = {Neural Comput.},
month = {jan},
title = {{Topographic independent component analysis}},
year = {2001}
}
@article{Peterson2021,
abstract = {Predicting and understanding how people make decisions has been a long-standing goal in many fields, with quantitative models of human decision-making informing research in both the social sciences and engineering. We show how progress toward this goal can be accelerated by using large datasets to power machine-learning algorithms that are constrained to produce interpretable psychological theories. Conducting the largest experiment on risky choice to date and analyzing the results using gradient-based optimization of differentiable decision theories implemented through artificial neural networks, we were able to recapitulate historical discoveries, establish that there is room to improve on existing theories, and discover a new, more accurate model of human decision-making in a form that preserves the insights from centuries of research.},
author = {Peterson, Joshua C. and Bourgin, David D. and Agrawal, Mayank and Reichman, Daniel and Griffiths, Thomas L.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/1209.full.pdf:pdf},
journal = {Science},
number = {6547},
pages = {1209--1214},
pmid = {34112693},
title = {{Using large-scale experiments and machine learning to discover theories of human decision-making}},
volume = {372},
year = {2021}
}
@article{Bartlett2002,
author = {Bartlett, M S and Movellan, J R and Sejnowski, T J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/IEEE Trans. Neural Netw. 2002 Bartlett.pdf:pdf},
journal = {IEEE Trans. Neural Networks},
month = {nov},
number = {6},
pages = {1450--1464},
title = {{Face recognition by independent component analysis}},
volume = {13},
year = {2002}
}
@article{Hoel2013,
author = {Hoel, Erik P and Albantakis, Larissa and Tononi, Giulio},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proc. Natl. Acad. Sci. U.S.A. 2013 Hoel.pdf:pdf},
journal = {Proc. Natl. Acad. Sci. U. S. A.},
month = {jan},
number = {49},
pages = {19790--19795},
title = {{Quantifying causal emergence shows that macro can beat micro}},
volume = {110},
year = {2013}
}
@article{Dehaene2007,
abstract = {Part of human cortex is specialized for cultural domains such as reading and arithmetic, whose invention is too recent to have influenced the evolution of our species. Representations of letter strings and of numbers occupy reproducible locations within large-scale macromaps, respectively in the left occipito-temporal and bilateral intraparietal cortex. Furthermore, recent fMRI studies reveal a systematic architecture within these areas. To explain this paradoxical cerebral invariance of cultural maps, we propose a neuronal recycling hypothesis, according to which cultural inventions invade evolutionarily older brain circuits and inherit many of their structural constraints.},
author = {Dehaene, Stanislas and Cohen, Laurent},
file = {:Users/hahosoya/Documents/Mendeley Desktop/1-s2.0-S0896627307007593-main.pdf:pdf},
journal = {Neuron},
number = {2},
pages = {384--398},
title = {{Cultural recycling of cortical maps.}},
volume = {56},
year = {2007}
}
@article{Siddharth2017,
abstract = {Variational autoencoders (VAEs) learn representations of data by jointly training a probabilistic encoder and decoder network. Typically these models encode all features of the data into a single variable. Here we are interested in learning disentangled representations that encode distinct aspects of the data into separate variables. We propose to learn such representations using model architectures that generalize from standard VAEs, employing a general graphical model structure in the encoder and decoder. This allows us to train partially-specified models that make relatively strong assumptions about a subset of interpretable variables and rely on the flexibility of neural networks to learn representations for the remaining variables. We further define a general objective for semi-supervised learning in this model class, which can be approximated using an importance sampling procedure. We evaluate our framework's ability to learn disentangled representations, both by qualitative exploration of its generative capacity, and quantitative evaluation of its discriminative ability on a variety of models and datasets.},
author = {Siddharth, N and Paige, Brooks and van de Meent, Jan-Willem and Desmaison, Alban and Wood, Frank and Goodman, Noah D and Kohli, Pushmeet and Torr, Philip H S},
file = {:Users/hahosoya/Documents/Mendeley Desktop/1E6BEDAE-5480-46FC-BC88-70204F2C8A39.pdf:pdf},
journal = {Adv. Neural Inf. Process. Syst.},
month = {jan},
title = {{Learning Disentangled Representations with Semi-Supervised Deep Generative Models.}},
year = {2017}
}
@article{Daugman1985,
abstract = {Received May 17, 1982; accepted February 12, 1985 Two - dimensional spatial linear filters are constrained by general uncertainty relations that limit their attainable information resolution for orientation , spatial frequency , and two - dimensional (2D) spatial position. The theoreti- cal ...},
author = {Daugman, J G},
file = {:Users/hahosoya/Documents/Mendeley Desktop/1985 Daugman.pdf:pdf},
journal = {Opt. Soc. Am.},
month = {jan},
title = {{Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters}},
year = {1985}
}
@article{Gallant1996,
abstract = {1. We studied the responses of 103 neurons in visual area V4 of anesthetized macaque monkeys to two novel classes of visual stimuli, polar and hyperbolic sinusoidal gratings. We suspected on both theoretical and experimental grounds that these stimuli would be useful for characterizing cells involved in intermediate stages of form analysis. Responses were compared with those obtained with conventional Cartesian sinusoidal gratings. Five independent, quantitative analyses of neural responses were carried out on the entire population of cells. 2. For each cell, responses to the most effective Cartesian, polar, and hyperbolic grating were compared directly. In 18 of 103 cells, the peak response evoked by one stimulus class was significantly different from the peak response evoked by the remaining two classes. Of the remaining 85 cells, 74 had response peaks for the three stimulus classes that were all within a factor of 2 of one another. 3. An information-theoretic analysis of the trial-by-trial responses to each stimulus showed that all but two cells transmitted significant information about the stimulus set as a whole. Comparison of the information transmitted about each stimulus class showed that 23 of 103 cells transmitted a significantly different amount of information about one class than about the remaining two classes. Of the remaining 80 cells, 55 had information transmission rates for the three stimulus classes that were all within a factor of 2 of one another. 4. To identify cells that had orderly tuning profiles in the various stimulus spaces, responses to each stimulus class were fit with a simple Gaussian model. Tuning curves were successfully fit to the data from at least one stimulus class in 98 of 103 cells, and such fits were obtained for at least two classes in 87 cells. Individual neurons showed a wide range of tuning profiles, with response peaks scattered throughout the various stimulus spaces; there were no major differences in the distributions of the widths or positions of tuning curves obtained for the different stimulus classes. 5. Neurons were classified according to their response profiles across the stimulus set with two objective methods, hierarchical cluster analysis and multidimensional scaling. These two analyses produced qualitatively similar results. The most distinct group of cells was highly selective for hyperbolic gratings. The majority of cells fell into one of two groups that were selective for polar gratings: one selective for radial gratings and one selective for concentric or spiral gratings. There was no group whose primary selectivity was for Cartesian gratings. 6. To determine whether cells belonging to identified classes were anatomically clustered, we compared the distribution of classified cells across electrode penetrations with the distribution that would be expected if the cells were distributed randomly. Cells with similar response profiles were often anatomically clustered. 7. A position test was used to determine whether response profiles were sensitive to precise stimulus placement. A subset of Cartesian and non-Cartesian gratings was presented at several positions in and near the receptive field. The test was run on 13 cells from the present study and 28 cells from an earlier study. All cells showed a significant degree of invariance in their selectivity across changes in stimulus position of up to 0.5 classical receptive field diameters. 8. A length and width test was used to determine whether cells preferring non-Cartesian gratings were selective for Cartesian grating length or width. Responses to Cartesian gratings shorter or narrower than the classical receptive field were compared with those obtained with full-field Cartesian and non-Cartesian gratings in 29 cells. Of the four cells that had shown significant preferences for non-Cartesian gratings in the main test, none showed tuning for Cartesian grating length or width that would account for their non-Cartesian res},
address = {Division of Biology, California Institute of Technology, Pasodena 91125, USA.},
author = {Gallant, J L and Connor, C E and Rakshit, S and Lewis, J W and {Van Essen}, D C},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neurophysiology 1996 Gallant.pdf:pdf},
journal = {J. Neurophysiol.},
month = {oct},
number = {4},
pages = {2718--2739},
title = {{Neural responses to polar, hyperbolic, and Cartesian gratings in area V4 of the macaque monkey.}},
volume = {76},
year = {1996}
}
@article{Wilkinson2000,
abstract = {The ventral form vision pathway of the primate brain comprises a sequence of areas that include V1, V2, V4 and the inferior temporal cortex (IT) [1]. Although contour extraction in the V1 area and responses to complex images, such as faces, in the IT have been studied extensively, much less is known about shape extraction at intermediate cortical levels such as V4. Here, we used functional magnetic resonance imaging (fMRI) to demonstrate that the human V4 is more strongly activated by concentric and radial patterns than by conventional sinusoidal gratings. This is consistent with global pooling of local V1 orientations to extract concentric and radial shape information in V4. Furthermore, concentric patterns were found to be effective in activating the fusiform face area. These findings support recent psychophysical [2,3] and physiological [4,5] data indicating that analysis of concentric and radial structure represents an important aspect of processing at intermediate levels of form vision.},
address = {Centre for Vision Research, York University, 4700 Keele Street, ON M3J 1P3, Toronto, Canada. franw@yorku.ca},
author = {Wilkinson, F and James, T W and Wilson, H R and Gati, J S and Menon, R S and Goodale, M A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Curr. Biol. 2000 Wilkinson.pdf:pdf},
journal = {Curr. Biol.},
month = {nov},
number = {22},
pages = {1455--1458},
title = {{An fMRI study of the selective activation of human extrastriate form vision areas by radial and concentric gratings.}},
volume = {10},
year = {2000}
}
@article{Poggio2020,
abstract = {Overparametrized deep networks predict well, despite the lack of an explicit complexity control during training, such as an explicit regularization term. For exponential-type loss functions, we solve this puzzle by showing an effective regularization effect of gradient descent in terms of the normalized weights that are relevant for classification.},
author = {Poggio, Tomaso and Liao, Qianli and Banburski, Andrzej},
file = {:Users/hahosoya/Documents/Mendeley Desktop/d1700faf71b810481f5f5ae8d082152c53dcc319.pdf:pdf},
journal = {Nat. Commun.},
number = {1},
pages = {1--5},
pmid = {32094327},
title = {{Complexity control by gradient descent in deep networks}},
volume = {11},
year = {2020}
}
@article{Nandy2016a,
abstract = {Neuron, Corrected proof. doi:10.1016/j.neuron.2016.07.026},
author = {Nandy, Anirvan S and Mitchell, Jude F and Jadi, Monika P and Reynolds, John H},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neuron 2016 Nandy.pdf:pdf},
journal = {Neuron},
month = {aug},
pages = {1--22},
title = {{Neurons in Macaque Area V4 Are Tuned for Complex Spatio-Temporal Patterns}},
year = {2016}
}
@article{Farzmahdi2016,
abstract = {Converging reports indicate that face images are processed through specialized neural networks in the brain -i.e. face patches in monkeys and the fusiform face area (FFA) in humans. These studies were designed to find out how faces are processed in visual system compared to other objects. Yet, the underlying mechanism of face processing is not completely revealed. Here, we show that a hierarchical computational model, inspired by electrophysiological evidence on face processing in primates, is able to generate representational properties similar to those observed in monkey face patches (posterior, middle and anterior patches). Since the most important goal of sensory neuroscience is linking the neural responses with behavioral outputs, we test whether the proposed model, which is designed to account for neural responses in monkey face patches, is also able to predict well-documented behavioral face phenomena observed in humans. We show that the proposed model satisfies several cognitive face effects such as: composite face effect and the idea of canonical face views. Our model provides insights about the underlying computations that transfer visual information from posterior to anterior face patches.},
address = {School of Cognitive Sciences (SCS), Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.},
author = {Farzmahdi, Amirhossein and Rajaei, Karim and Ghodrati, Masoud and Ebrahimpour, Reza and Khaligh-Razavi, Seyed-Mahdi},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Sci. Rep. 2016 Farzmahdi.pdf:pdf},
journal = {Sci. Rep.},
month = {jan},
pages = {25025},
title = {{A specialized face-processing model inspired by the organization of monkey face patches explains several face-specific phenomena observed in humans.}},
volume = {6},
year = {2016}
}
@article{Nilsback2008,
author = {Nilsback, Maria-Elena and Zisserman, Andrew},
file = {:Users/hahosoya/Documents/Mendeley Desktop/715F5B7A-8D7D-433C-AC48-A8F5C5CEB5A7.pdf:pdf},
journal = {Indian Conf. Comput. Vis. Graph. Image Process.},
month = {jan},
pages = {722--729},
title = {{Automated Flower Classification over a Large Number of Classes.}},
year = {2008}
}
@article{Rezende2016,
abstract = {Abstract Humans have an impressive ability to reason about new concepts and experiences from just a single example. In particular, humans have an ability for one-shot generalization: an ability to encounter a new concept, understand its structure, and then be able to generate compelling alternative variations of the concept. We develop machine learning systems with this important capacity by developing new deep generative models, models that combine ...
},
author = {Rezende, D and Danihelka, I and Gregor, K and Wierstra, D},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2016 Rezende.pdf:pdf},
journal = {{\ldots} Conf. Mach. {\ldots}},
month = {jan},
title = {{One-shot generalization in deep generative models}},
year = {2016}
}
@article{Bengio2007,
abstract = {Abstract Complexity theory of circuits strongly suggests that deep architectures can be much more ef cient (sometimes exponentially) than shallow architectures, in terms of computational elements required to represent some functions. Deep multi-layer neural  ...},
author = {Bengio, Y and Lamblin, P and Popovici, D and et Al},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Advances in neural {\ldots} 2007 Bengio.pdf:pdf},
journal = {Adv. neural  {\ldots}},
month = {jan},
title = {{Greedy layer-wise training of deep networks}},
year = {2007}
}
@article{Terashima2013,
abstract = {Neurocomputing, 103 + (2012) 14-21. doi:10.1016/j.neucom.2012.07.009},
author = {Terashima, Hiroki and Hosoya, Haruo and Tani, Toshiki and Ichinohe, Noritaka and Okada, Masato},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neurocomputing 2013 Terashima.pdf:pdf},
journal = {Neurocomputing},
month = {mar},
number = {C},
pages = {14--21},
title = {{Sparse coding of harmonic vocalization in monkey auditory cortex}},
volume = {103},
year = {2013}
}
@article{Movshon1978,
abstract = {1. All complex cells in the cat's striate cortex exhibit gross non-linearities of spatial summation when tested with sinusoidal grating stimuli. Their responses to moving gratings of all but the lowest spatial frequencies are usually dominated by a component that is not modulated by the passage of the bars of the grating across the receptive field. They give responses to temporally modulated stationary gratings that consist mostly of even harmonics of the stimulus frequency and that vary little in amplitude or wave form as the spatial phase of the grating is varied. 2. We compared complex cells' receptive fields with their sensitivity to sinusoidal gratings of different spatial frequencies. Qualitatively, the receptive fields are usually two to five times wider than the bars of the gratings that stimulate them most effectively. Quantitatively, the receptive field profiles of complex cells are invariably broader than those predicted by Fourier synthesis of their spatial frequency tuning curves, and in particular lack predicted spatially antagonistic regions. 3. We further examined the receptive field organization of these cells, using pairs of stationary lines flashed synchronously on their receptive fields. If both lines are of the same polarity (bright or dark), complex cells respond to the paired stimulus much less well than they do to either of its component bars, unless the bars are separated by less than about one quarter of the width of the receptive field. If the lines are of opposite polarity, one bright and one dark, the opposite situation obtains: closely spaced bars elicit small responses, while paired bars of larger separation are much more effective. In either case, the results are independent in general character of the absolute positions of the stimuli within the receptive field; rather, they depend in a manner characteristic of each cell on the relative positions of the two bars. 4. The two-line interaction profile that plots the change in a complex cell's response to one bar as a function of the position of a second added bar corresponds closely to the receptive field profile predicted from Fourier synthesis of the cell's spatial frequency tuning curve. These profiles may thus reveal the spatial characteristics of subunits within complex cell-receptive fields. We examined the nature of the interaction between these subunits by performing several two-line interaction experiments in which the onset of the second bar was delayed some time after the onset of the first. The results suggest that neighbouring subunits interact in a facilitatory fashion: for an interval after the presentation of one bar, responses to neighbouring bars are enhanced. 5. The subunits of a complex receptive field may, by their spatial properties, determine the spatial selectivities of complex cells, while the nature of the interaction among the subunits may determine these cells' sensitivity and selectivity for moving visual stimuli...},
author = {Movshon, J A and Thompson, I D and Tolhurst, D J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/The Journal of Physiology 1978 Movshon.pdf:pdf},
journal = {J. Physiol.},
month = {oct},
pages = {79--99},
title = {{Receptive field organization of complex cells in the cat's striate cortex.}},
volume = {283},
year = {1978}
}
@article{Polosecki2013,
author = {Polosecki, P and Moeller, S and Schweers, N and Romanski, L M and Tsao, D Y and Freiwald, W A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2013 Polosecki.pdf:pdf},
journal = {J. Neurosci.},
month = {jul},
number = {29},
pages = {11768--11773},
title = {{Faces in Motion: Selectivity of Macaque and Human Face Processing Areas for Dynamic Stimuli}},
volume = {33},
year = {2013}
}
@article{Payne2021a,
abstract = {Spatial memory in vertebrates requires brain regions homologous to the mammalian hippocampus. Between vertebrate clades, however, these regions are anatomically distinct and appear to produce different spatial patterns of neural activity. We asked whether hippocampal activity is fundamentally different even between distant vertebrates that share a strong dependence on spatial memory. We studied tufted titmice, food-caching birds capable of remembering many concealed food locations. We found mammalian-like neural activity in the titmouse hippocampus, including sharp-wave ripples and anatomically organized place cells. In a non–food-caching bird species, spatial firing was less informative and was exhibited by fewer neurons. These findings suggest that hippocampal circuit mechanisms are similar between birds and mammals, but that the resulting patterns of activity may vary quantitatively with species-specific ethological needs.},
author = {Payne, H. L. and Lynch, G. F. and Aronov, D.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/343.full.pdf:pdf},
journal = {Science},
number = {6552},
pages = {343--348},
title = {{Neural representations of space in the hippocampus of a food-caching bird}},
volume = {373},
year = {2021}
}
@article{Brodt2018,
abstract = {Models of systems memory consolidation postulate a fast-learning hippocampal store and a slowly developing, stable neocortical store. Accordingly, early neocortical contributions to memory are deemed to reflect a hippocampus-driven online reinstatement of encoding activity. In contrast, we found that learning rapidly engenders an enduring memory engram in the human posterior parietal cortex. We assessed microstructural plasticity via diffusion-weighted magnetic resonance imaging as well as functional brain activity in an object-location learning task. We detected neocortical plasticity as early as 1 hour after learning and found that it was learning specific, enabled correct recall, and overlapped with memory-related functional activity. These microstructural changes persisted over 12 hours. Our results suggest that new traces can be rapidly encoded into the parietal cortex, challenging views of a slow-learning neocortex.},
address = {Institute of Medical Psychology and Behavioral Neurobiology, University of T{\"{u}}bingen, T{\"{u}}bingen, Germany. svenja.brodt@uni-tuebingen.de.},
author = {Brodt, S and Gais, S and Beck, J and Erb, M and Scheffler, K and Sch{\"{o}}nauer, M},
file = {:Users/hahosoya/Documents/Mendeley Desktop/391FBFFE-F441-4871-8D58-7F0DB0718FB3.pdf:pdf},
journal = {Science},
month = {nov},
number = {6418},
pages = {1045--1048},
title = {{Fast track to the neocortex: A memory engram in the posterior parietal cortex.}},
volume = {362},
year = {2018}
}
@article{Fang2005,
author = {Fang, Fang and He, Sheng},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature neuroscience 2005 Fang-1.pdf:pdf},
journal = {Nat. Neurosci.},
month = {sep},
number = {10},
pages = {1380--1385},
title = {{Cortical responses to invisible objects in the human dorsal and ventral pathways}},
volume = {8},
year = {2005}
}
@article{Wang1998,
abstract = {To investigate the functional organization in the monkey inferotemporal cortex, which is the last exclusively visual area along the ventral visual cortical pathway, optical imaging based on intrinsic signals was carried out. We first conducted single-cell recordings with microelectrodes and determined the features critical for the activation of single cells. For the subsequent optical imaging, each critical feature was presented, which evoked multiple dark spots. Individual spots were approximately 0.5 mm in diameter and one of them covered the site of the electrode penetration at which the particular critical feature had been determined. The degree of stimulus selectivity varied from spot to spot, and from region to region even within a spot. Some regions were activated only by one of 12- 16 stimuli, while others by more than three stimuli. There were spots specifically activated by faces, and the position of activation spot changed gradually along the cortical surface as the stimulus face was rotated in depth. The length of the overall region along the direction of shift of these spots was approximately 1 mm. These results confirm the regional clustering of cells with similar stimulus selectivity and suggest larger units in which some parameters of object features are continuously mapped.},
address = {RIKEN Brain Science Institute, Saitama, Japan.},
author = {Wang, G and Tanifuji, M and Tanaka, K},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neurosci. Res. 1998 Wang.pdf:pdf},
journal = {Neurosci. Res.},
month = {sep},
number = {1},
pages = {33--46},
title = {{Functional architecture in monkey inferotemporal cortex revealed by in vivo optical imaging.}},
volume = {32},
year = {1998}
}
@article{Ni2018,
abstract = {Prior studies have demonstrated that correlated variability changes with cognitive processes that improve perceptual performance. We tested whether correlated variability covaries with subjects' performance-whether performance improves quickly with attention or slowly with perceptual learning. We found a single, consistent relationship between correlated variability and behavioral performance, regardless of the time frame of correlated variability change. This correlated variability was oriented along the dimensions in population space used by the animal on a trial-by-trial basis to make decisions. That subjects' choices were predicted by specific dimensions that were aligned with the correlated variability axis clarifies long-standing paradoxes about the relationship between shared variability and behavior.},
address = {Department of Neuroscience and Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA 15260, USA.},
author = {Ni, A M and Ruff, D A and Alberts, J J and Symmonds, J and Cohen, M R},
file = {:Users/hahosoya/Documents/Mendeley Desktop/5D0002D7-A5A3-4282-8E3A-4915A7F5E5D3.pdf:pdf},
journal = {Science},
month = {jan},
number = {6374},
pages = {463--465},
title = {{Learning and attention reveal a general relationship between population activity and behavior.}},
volume = {359},
year = {2018}
}
@article{Guclu2015,
author = {G{\"{u}}{\c{c}}l{\"{u}}, U and van Gerven, M A J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2015 Guclu.pdf:pdf},
journal = {J. Neurosci.},
month = {jul},
number = {27},
pages = {10005--10014},
title = {{Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Ventral Stream}},
volume = {35},
year = {2015}
}
@article{Zou2012,
abstract = {Abstract We apply salient feature detection and tracking in videos to simulate fixations and smooth pursuit in human vision. With tracked sequences as input, a hierarchical network of modules learns invariant features using a temporal slowness constraint. The network ... 
},
author = {Zou, W and Ng, A and Zhu, S and Yu, K},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Advances in neural information {\ldots} 2012 Zou.pdf:pdf},
journal = {Adv. Neural Inf. {\ldots}},
month = {jan},
title = {{Deep Learning of Invariant Features via Simulated Fixations in Video}},
year = {2012}
}
@article{Jiang2007,
author = {Jiang, Yi and Zhou, Ke and He, Sheng},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature neuroscience 2007 Jiang-1.pdf:pdf},
journal = {Nat. Neurosci.},
month = {apr},
number = {5},
pages = {657--662},
title = {{Human visual cortex responds to invisible chromatic flicker}},
volume = {10},
year = {2007}
}
@article{Rowekamp2017,
abstract = {Nature Communications 8,  (2017). doi:10.1038/ncomms15739},
author = {Rowekamp, Ryan J and Sharpee, Tatyana O},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature Communications 2017 Rowekamp.pdf:pdf},
journal = {Nat. Commun.},
month = {may},
pages = {1--9},
title = {{Cross-orientation suppression in visual area V2}},
volume = {8},
year = {2017}
}
@inproceedings{pmlr-v119-le20b,
abstract = {Heretofore, neural networks with external memory are restricted to single memory with lossy representations of memory interactions. A rich representation of relationships between memory pieces urges a high-order and segregated relational memory. In this paper, we propose to separate the storage of individual experiences (item memory) and their occurring relationships (relational memory). The idea is implemented through a novel Self-attentive Associative Memory (SAM) operator. Found upon outer product, SAM forms a set of associative memories that represent the hypothetical high-order relationships between arbitrary pairs of memory elements, through which a relational memory is constructed from an item memory. The two memories are wired into a single sequential model capable of both memorization and relational reasoning. We achieve competitive results with our proposed two-memory model in a diversity of machine learning tasks, from challenging synthetic problems to practical testbeds such as geometry, graph, reinforcement learning, and question answering.},
author = {Le, Hung and Tran, Truyen and Venkatesh, Svetha},
booktitle = {Proc. 37th Int. Conf. Mach. Learn.},
editor = {III, Hal Daum{\'{e}} and Singh, Aarti},
file = {:Users/hahosoya/Documents/Mendeley Desktop/31911c1a2efc56fd53e13108f69f9c23750f400a.pdf:pdf},
keywords = {ICML,Machine Learning},
pages = {5682--5691},
series = {Proceedings of Machine Learning Research},
title = {{Self-Attentive Associative Memory}},
volume = {119},
year = {2020}
}
@article{Heeger1992,
abstract = {Simple cells in striate cortex have been depicted as rectified linear operators, and complex cells have been depicted as energy mechanisms (constructed from the squared sums of linear operator outputs). This paper discusses two essential hypotheses of the linear/energy model: (1) that a cell's selectivity is due to an underlying (spatiotemporal and binocular) linear stage; and (2) that a cell's firing rate depends on the squared output of the underlying linear stage. This paper reviews physiological measurements of cat striate cell responses, and concludes that both of these hypotheses are supported by the data.},
address = {NASA-Ames Research Center, Moffett Field, CA.},
author = {Heeger, D J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Visual Neuroscience 1992 Heeger.pdf:pdf},
journal = {Vis. Neurosci.},
month = {nov},
number = {5},
pages = {427--443},
title = {{Half-squaring in responses of cat striate cells.}},
volume = {9},
year = {1992}
}
@article{Makhzani2015,
abstract = {In this paper, we propose the" adversarial autoencoder"(AAE), which is a probabilistic autoencoder that uses the recently proposed generative adversarial networks (GAN) to perform variational inference by matching the aggregated posterior of the hidden code {\ldots}
},
author = {Makhzani, Alireza and Shlens, Jonathon and Jaitly, Navdeep and Goodfellow, Ian and Frey, Brendan},
file = {:Users/hahosoya/Documents/Mendeley Desktop/3F7B1262-F6CF-404B-B00F-9AE7D8DDDE09.pdf:pdf},
journal = {arxiv.org },
month = {nov},
title = {{Adversarial Autoencoders}},
year = {2015}
}
@article{Henaff2014,
abstract = {We develop a new statistical model for photographic images, in which the local responses of a bank of linear filters are described as jointly Gaussian, with zero mean and a covariance that varies slowly over spatial position. We optimize sets of filters so as to minimize the nuclear norms of matrices of their local activations (i.e., the sum of the singular values), thus encouraging a flexible form of sparsity that is not tied to any particular dictionary or coordinate system. Filters optimized according to this objective are oriented and bandpass, and their responses exhibit substantial local correlation. We show that images can be reconstructed nearly perfectly from estimates of the local filter response covariances alone, and with minimal degradation (either visual or MSE) from low-rank approximations of these covariances. As such, this representation holds much promise for use in applications such as denoising, compression, and texture representation, and may form a useful substrate for hierarchical decompositions.},
author = {H{\'{e}}naff, Olivier J and Ball{\'{e}}, Johannes and Rabinowitz, Neil C and Simoncelli, Eero P},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2015.pdf:pdf},
journal = {arXiv.org},
month = {dec},
title = {{The local low-dimensionality of natural images}},
volume = {cs.CV},
year = {2014}
}
@article{Ren2007,
abstract = {Abstract We found that, in the mouse visual cortex, action potentials generated in a single layer-2/3 pyramidal (excitatory) neuron can reliably evoke large, constant-latency inhibitory postsynaptic currents in other nearby pyramidal cells. This effect is mediated by axo- ... 
},
author = {Ren, M and Yoshimura, Y and Takada, N and Horibe, S and Komatsu, Y},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Science 2007 Ren.pdf:pdf},
journal = {Science},
month = {may},
number = {5825},
pages = {758--761},
title = {{Specialized Inhibitory Synaptic Actions Between Nearby Neocortical Pyramidal Neurons}},
volume = {316},
year = {2007}
}
@article{Maunsell1983a,
abstract = {1. Electrophysiological recordings were made in the middle temporal visual area (MT) of five macaque monkeys. Binocularity and selectivity for disparity were examined using a computer-driven stimulator to activate each eye independently. Results were obtained from 91 single units in MT. 2. Most units in MT receive approximately balanced inputs from the two eyes, and very few could be driven through only one eye. 3. In one type of test for disparity selectivity, units were examined with stimuli that had different but fixed horizontal disparities, thereby simulating frontoparallel movements at different distances from the animal. About two-thirds of ;the units tested for fixed disparity selectivity (52/76) showed pronounced sensitivity to horizontal disparity. Most of these units could be grouped into the same four classes of disparity-tuned units that have previously been described in V1 and V2 of the macaque: near, far, tuned excitatory, and tuned inhibitory. 4. Twenty units were tested for sensitivity to vertical stimulus disparity, which does not normally contribute to stereopsis. Most were as sensitive to vertical disparities as to horizontal. 5. Units were also tested for selectivity for stimuli that moved with changing disparity, simulating trajectories with components of motion toward or away from the animal (motion in depth). No units were found to be truly selective for motion in depth. Units tuned for fixed disparity could appear to prefer motion in depth if tested only with trajectories whose common center point was far from the unit's optimal fixed disparity. However, we do not consider this to represent genuine selectivity for motion in depth, since 1) the responses ara adequately and more easily explained in terms of selectivity for fixed disparity and 2) the best overall response of these units is to frontoparallel motion at the optimal fixed disparity. This observation bears importantly on the interpretation of motion in depth selectivity in previous investigations. 6. The presence of a substantial degree of selectivity for fixed disparity in MT, together with previously demonstrated selectivities for direction and speed, indicates that MT is well suited for the analysis of motion in three-dimensional space.},
author = {Maunsell, J H and {Van Essen}, D C},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neurophysiology 1983 Maunsell-3.pdf:pdf},
journal = {J. Neurophysiol.},
month = {may},
number = {5},
pages = {1148--1167},
title = {{Functional properties of neurons in middle temporal visual area of the macaque monkey. II. Binocular interactions and sensitivity to binocular disparity.}},
volume = {49},
year = {1983}
}
@article{Taylor2007,
abstract = {We propose a non-linear generative model for human motion data that uses an undirected model 
with binary latent variables and real-valued “visible” variables that represent joint angles. The
latent and visible variables at each time step re- ceive directed connections from the  ...},
author = {Taylor, G W and et Al},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Advances in neural information {\ldots} 2007 Taylor.pdf:pdf},
journal = {Adv. Neural Inf. {\ldots}},
month = {jan},
title = {{Modeling human motion using binary latent variables}},
year = {2007}
}
@article{Dosovitskiy2020,
abstract = {While the Transformer architecture has become the de-facto standard for natural language processing tasks, its applications to computer vision remain limited. In vision, attention is either applied in conjunction with convolutional networks, or used to replace certain components of convolutional networks while keeping their overall structure in place. We show that this reliance on CNNs is not necessary and a pure transformer applied directly to sequences of image patches can perform very well on image classification tasks. When pre-trained on large amounts of data and transferred to multiple mid-sized or small image recognition benchmarks (ImageNet, CIFAR-100, VTAB, etc.), Vision Transformer (ViT) attains excellent results compared to state-of-the-art convolutional networks while requiring substantially fewer computational resources to train.},
archivePrefix = {arXiv},
arxivId = {2010.11929},
author = {Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and Uszkoreit, Jakob and Houlsby, Neil},
eprint = {2010.11929},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2010.11929.pdf:pdf},
pages = {1--21},
title = {{An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale}},
year = {2020}
}
@article{Kingma2014a,
abstract = {Abstract The ever-increasing size of modern data sets combined with the difficulty of obtaining label information has made semi-supervised learning one of the problems of significant practical importance in modern data analysis. We revisit the approach to semi- supervised learning with generative models and develop new models that allow for effective generalisation from small labelled data sets to large unlabelled ones. Generative ...
},
author = {Kingma, D P and Mohamed, S and Rezende, D J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Advances in neural information processing systems 2014 Kingma.pdf:pdf},
journal = {Adv. Neural Inf. Process. Syst.},
month = {jan},
title = {{Semi-supervised learning with deep generative models}},
year = {2014}
}
@article{Sheahan2021,
abstract = {A prerequisite for intelligent behavior is to understand how stimuli are related and to generalize this knowledge across contexts. Generalization can be challenging when relational patterns are shared across contexts but exist on different physical scales. Here, we studied neural representations in humans and recurrent neural networks performing a magnitude comparison task, for which it was advantageous to generalize concepts of “more” or “less” between contexts. Using multivariate analysis of human brain signals and of neural network hidden unit activity, we observed that both systems developed parallel neural “number lines” for each context. In both model systems, these number state spaces were aligned in a way that explicitly facilitated generalization of relational concepts (more and less). These findings suggest a previously overlooked role for neural normalization in supporting transfer of a simple form of abstract relational knowledge (magnitude) in humans and machine learning systems.},
author = {Sheahan, Hannah and Luyckx, Fabrice and Nelli, Stephanie and Teupe, Clemens and Summerfield, Christopher},
file = {:Users/hahosoya/Documents/Mendeley Desktop/1-s2.0-S0896627321000787-main.pdf:pdf},
journal = {Neuron},
keywords = {alignment,generalization,magnitude,neural network,normalization,number,parietal cortex,representation},
number = {7},
pages = {1214--1226.e8},
pmid = {33626322},
title = {{Neural state space alignment for magnitude generalization in humans and recurrent networks}},
volume = {109},
year = {2021}
}
@article{Kriegeskorte2008,
abstract = {Inferior temporal (IT) object representations have been intensively studied in monkeys and humans, but representations of the same particular objects have never been compared between the species. Moreover, IT's role in categorization is not well understood. Here, we  ...},
author = {Kriegeskorte, Nikolaus and Mur, M and Ruff, D A and Kiani, Roozbeh and Bodurka, J and et Al},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neuron 2008 Kriegeskorte.pdf:pdf},
journal = {Neuron},
month = {jan},
title = {{Matching categorical object representations in inferior temporal cortex of man and monkey}},
year = {2008}
}
@article{Elazary2010,
author = {Elazary, Lior and Itti, Laurent},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Vision Research 2010 Elazary.pdf:pdf},
journal = {Vision Res.},
month = {jun},
number = {14},
pages = {1338--1352},
title = {{A Bayesian model for efficient visual search and recognition}},
volume = {50},
year = {2010}
}
@article{Rollenhagen2000,
abstract = {Abstract Humans and animals confuse lateral mirror images, such as the letters" b" and" d," more often than vertical mirror images, such as the letters" b" and" p." Experiments were performed to find a neural correlate of this phenomenon.
},
author = {Rollenhagen, J E and Olson, C R},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Science 2000 Rollenhagen.pdf:pdf},
journal = {Science},
month = {jan},
title = {{Mirro-image confusion in single neurons of the macaque inferotemporal cortex}},
year = {2000}
}
@article{Hjelm2018,
abstract = {In this work, we perform unsupervised learning of representations by maximizing mutual information between an input and the output of a deep neural network encoder. Importantly, we show that structure matters: incorporating knowledge about locality of the input to the objective can greatly influence a representation's suitability for downstream tasks. We further control characteristics of the representation by matching to a prior distribution adversarially. Our method, which we call Deep InfoMax (DIM), outperforms a number of popular unsupervised learning methods and competes with fully-supervised learning on several classification tasks. DIM opens new avenues for unsupervised learning of representations and is an important step towards flexible formulations of representation-learning objectives for specific end-goals.},
author = {Hjelm, R Devon and Fedorov, Alex and Lavoie-Marchildon, Samuel and Grewal, Karan and Bachman, Phil and Trischler, Adam and Bengio, Yoshua},
file = {:Users/hahosoya/Documents/Mendeley Desktop/EE474D02-EF6F-4BDF-87F9-C0CB003B6DA5.pdf:pdf},
journal = {ICLR},
month = {aug},
title = {{Learning deep representations by mutual information estimation and maximization}},
volume = {stat.ML},
year = {2018}
}
@article{Bethge2008,
author = {Bethge, M and Berens, P},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Advances in neural information processing systems 2008 Bethge.pdf:pdf},
journal = {Adv. Neural Inf. Process. Syst.},
month = {jan},
pages = {97--104},
title = {{Near-maximum entropy models for binary neural representations of natural images}},
volume = {20},
year = {2008}
}
@article{Leonard1985,
author = {Rolls, E.T. and Baylis, G.C. and Leonard, C.M.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/98_Rolls, Baylis and Leonard 1985 Role of low and high spatial frequencies.pdf:pdf},
journal = {Vision Res.},
month = {jan},
number = {8},
pages = {1021--1035},
title = {{Role of low and high spatial frequencies in the face-selective responses of neurons in the cortex in the superior temporal sulcus in the monkey}},
volume = {25},
year = {1985}
}
@article{Mao2021,
abstract = {The hippocampal formation is linked to spatial navigation, but there is little corroboration from freely moving primates with concurrent monitoring of head and gaze stances. We recorded neural activity across hippocampal regions in rhesus macaques during free foraging in an open environment while tracking their head and eye. Theta activity was intermittently present at movement onset and modulated by saccades. Many neurons were phase-locked to theta, with few showing phase precession. Most neurons encoded a mixture of spatial variables beyond place and grid tuning. Spatial representations were dominated by facing location and allocentric direction, mostly in head, rather than gaze, coordinates. Importantly, eye movements strongly modulated neural activity in all regions. These findings reveal that the macaque hippocampal formation represents three-dimensional (3D) space using a multiplexed code, with head orientation and eye movement properties being dominant during free exploration.},
author = {Mao, Dun and Avila, Eric and Caziot, Baptiste and Laurens, Jean and Dickman, J. David and Angelaki, Dora E.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/1-s2.0-S0896627321007054-main.pdf:pdf},
journal = {Neuron},
keywords = {motion tracking,non-human primate},
number = {21},
pages = {3521--3534.e6},
pmid = {34644546},
title = {{Spatial modulation of hippocampal activity in freely moving macaques}},
volume = {109},
year = {2021}
}
@article{Ranzato2007,
abstract = {... In sec. 5, we compare the filters learned by SESM and RBM for handwritten numerals and natural image patches. In sec.5.1.1, we describe a simple way to produce a deep belief  net by stacking multiple levels of SESM modules. ... 
},
author = {Ranzato, Y Marc'Aurelio and Boureau, L},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Advances in neural {\ldots} 2007 Ranzato.pdf:pdf},
journal = {Adv. neural  {\ldots}},
month = {jan},
title = {{Sparse feature learning for deep belief networks}},
year = {2007}
}
@article{Peelen2007,
author = {Peelen, Marius V and Downing, Paul E},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nat Rev Neurosci 2007 Peelen.pdf:pdf},
journal = {Nat. Rev. Neurosci.},
month = {aug},
number = {8},
pages = {636--648},
title = {{The neural basis of visual body perception}},
volume = {8},
year = {2007}
}
@article{Li2001,
author = {Li, Stan Z and Lv, Xiao Guang and Zhang, HongJiang},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2001 Li.pdf:pdf},
journal = {Comput. Vision, 2001. ICCV 2001. Proceedings. Eighth IEEE Int. Conf.},
month = {jan},
pages = {295--300},
title = {{View-based clustering of object appearances based on independent subspace analysis}},
volume = {2},
year = {2001}
}
@article{Russin2020,
abstract = {Research seeking to build artificial systems capable of reproducing elements of human intelligence may benefit from a deeper consideration of the architecture and learning mechanisms of the human brain. In this brief review, we note a connection between many current challenges facing artificial intelligence and the functions of a particular brain area-the prefrontal cortex (PFC). This brain area is known to be involved in executive functions such as reasoning, rule-learning, deliberate or controlled processing, and abstract planning. Motivated by the hypothesis that these functions provide a form of out-of-distribution robustness currently not available in state-of-the-art AI systems, we elaborate on this connection and highlight some computational principles thought to be at work in PFC, with the goal of enhancing the synergy between neuroscience and machine learning.},
author = {Russin, Jacob and Reilly, Randall C O},
file = {:Users/hahosoya/Documents/Mendeley Desktop/02d298ae7fb05113844f66d67ec60f2cc43fea0b.pdf:pdf},
journal = {Work. “Bridging AI Cogn. Sci. (ICLR 2020)},
number = {Iclr},
pages = {1--11},
title = {{Deep Learning Needs a Prefrontal Cortex}},
year = {2020}
}
@article{Thompson2012,
author = {Thompson, Garth John and Magnuson, Matthew Evan and Merritt, Michael Donelyn and Schwarb, Hillary and Pan, Wen-Ju and McKinley, Andrew and Tripp, Lloyd D and Schumacher, Eric H and Keilholz, Shella Dawn},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Hum. Brain Mapp 2012 Thompson.pdf:pdf},
journal = {Hum. Brain Mapp.},
month = {jun},
pages = {n/a--n/a},
title = {{Short-time windows of correlation between large-scale functional brain networks predict vigilance intraindividually and interindividually}},
year = {2012}
}
@inproceedings{Lin2018,
abstract = {Recent efforts on combining deep models with probabilistic graphical models are promising in providing flexible models that are also easy to interpret. We propose a variational message-passing algorithm for variational inference in such models. We make three contributions. First, we propose structured inference networks that incorporate the structure of the graphical model in the inference network of variational auto-encoders (VAE). Second, we establish conditions under which such inference networks enable fast amortized inference similar to VAE. Finally, we derive a variational message passing algorithm to perform efficient natural-gradient inference while retaining the efficiency of the amortized inference. By simultaneously enabling structured, amortized, and natural-gradient inference for deep structured models, our method simplifies and generalizes existing methods.},
author = {Lin, Wu and Hubacher, Nicolas and Khan, Mohammad Emtiyaz},
booktitle = {Int. Conf. Learn. Represent.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/E8FBBBCA-F565-4F02-86B7-EB2F202F6196.pdf:pdf},
month = {mar},
title = {{Variational Message Passing with Structured Inference Networks}},
volume = {stat.ML},
year = {2018}
}
@article{Melcher2003,
abstract = {Saccadic eye movements pose many challenges for stable and continuous vision, such as how information from successive fixations is amalgamated into a single precept. Here we show in humans that motion signals are temporally integrated across separate fixations, but only when the motion stimulus falls either on the same retinal region (retinotopic integration) or on different retinal positions that correspond to the same external spatial coordinates (spatiotopic integration). We used individual motion signals that were below detection threshold, implicating spatiotopic trans-saccadic integration in relatively early stages of visual processing such as the middle temporal area (MT) or V5 of visual cortex. The trans-saccadic buildup of important congruent visual information while irrelevant non-congruent information fades could provide a simple and robust strategy to stabilize perception during eye movements.},
address = {Faculty of Psychology, Universit{\'{a}} Vita-salute San Raffaele, Via Olgettina 58, Milan, Italy.},
author = {Melcher, David and Morrone, M Concetta},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature neuroscience 2003 Melcher-1.pdf:pdf},
journal = {Nat. Neurosci.},
month = {aug},
number = {8},
pages = {877--881},
title = {{Spatiotopic temporal integration of visual motion across saccadic eye movements.}},
volume = {6},
year = {2003}
}
@article{Keller2012,
abstract = {Neuron, 74 (2012) 809-815. doi:10.1016/j.neuron.2012.03.040},
author = {Keller, Georg B and Bonhoeffer, Tobias and H{\"{u}}bener, Mark},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neuron 2012 Keller.pdf:pdf},
journal = {Neuron},
month = {jun},
number = {5},
pages = {809--815},
title = {{Sensorimotor Mismatch Signals in Primary Visual Cortex of the Behaving Mouse}},
volume = {74},
year = {2012}
}
@article{Fishman2014,
abstract = {The ability to attend to a particular sound in a noisy environment is an essential aspect of hearing. To accomplish this feat, the auditory system must segregate sounds that overlap in frequency and time. Many natural sounds, such as human voices, consist of harmonics of a common fundamental frequency (F0). Such harmonic complex tones (HCTs) evoke a pitch corresponding to their F0. A difference in pitch between simultaneous HCTs provides a powerful cue for their segregation. The neural mechanisms underlying concurrent sound segregation based on pitch differences are poorly understood. Here, we examined neural responses in monkey primary auditory cortex (A1) to two concurrent HCTs that differed in F0 such that they are heard as two separate "auditory objects" with distinct pitches. We found that A1 can resolve, via a rate-place code, the lower harmonics of both HCTs, a prerequisite for deriving their pitches and for their perceptual segregation. Onset asynchrony between the HCTs enhanced the neural representation of their harmonics, paralleling their improved perceptual segregation in humans. Pitches of the concurrent HCTs could also be temporally represented by neuronal phase-locking at their respective F0s. Furthermore, a model of A1 responses using harmonic templates could qualitatively reproduce psychophysical data on concurrent sound segregation in humans. Finally, we identified a possible intracortical homolog of the "object-related negativity" recorded noninvasively in humans, which correlates with the perceptual segregation of concurrent sounds. Findings indicate that A1 contains sufficient spectral and temporal information for segregating concurrent sounds based on differences in pitch.},
address = {Departments of Neurology and Neuroscience, Albert Einstein College of Medicine, Bronx, New York 10461, yonatan.fishman@einstein.yu.edu.},
author = {Fishman, Yonatan I and Steinschneider, Mitchell and Micheyl, Christophe},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2014 Fishman.pdf:pdf},
journal = {J. Neurosci.},
month = {sep},
number = {37},
pages = {12425--12443},
title = {{Neural representation of concurrent harmonic sounds in monkey primary auditory cortex: implications for models of auditory scene analysis.}},
volume = {34},
year = {2014}
}
@article{Tong1998,
abstract = {We used functional magnetic resonance imaging (fMRI) to monitor stimulus-selective responses of the human fusiform face area (FFA) and parahippocampal place area (PPA) during binocular rivalry in which a face and a house stimulus were presented to different eyes. Though retinal stimulation remained constant, subjects perceived changes from house to face that were accompanied by increasing FFA and decreasing PPA activity; perceived changes from face to house led to the opposite pattern of responses. These responses during rivalry were equal in magnitude to those evoked by nonrivalrous stimulus alternation, suggesting that activity in the FFA and PPA reflects the perceived rather than the retinal stimulus, and that neural competition during binocular rivalry has been resolved by these stages of visual processing.},
address = {Department of Psychology, Harvard University, Cambridge, Massachusetts 02138, USA. frank@wjh.harvard.edu},
author = {Tong, F and Nakayama, K and Vaughan, J T and Kanwisher, N},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neuron 1998 Tong.pdf:pdf},
journal = {Neuron},
month = {oct},
number = {4},
pages = {753--759},
title = {{Binocular rivalry and visual awareness in human extrastriate cortex.}},
volume = {21},
year = {1998}
}
@article{Hyvarinen2000,
author = {Hyv{\"{a}}rinen, Aapo and Hoyer, Patrik},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neural Computation 2000 Hyv{\"{a}}rinen.pdf:pdf},
journal = {Neural Comput.},
month = {jan},
number = {7},
pages = {1705--1720},
title = {{Emergence of phase-and shift-invariant features by decomposition of natural images into independent feature subspaces}},
volume = {12},
year = {2000}
}
@article{Lake2017,
abstract = {Recent progress in artificial intelligence has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats that of humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn and how they learn it. Specifically, we argue that these machines should (1) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (2) ground learning in intuitive theories of physics and psychology to support and enrich the knowledge that is learned; and (3) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes toward these goals that can combine the strengths of recent neural network advances with more structured cognitive models.},
archivePrefix = {arXiv},
arxivId = {1604.00289},
author = {Lake, Brenden M. and Ullman, Tomer D. and Tenenbaum, Joshua B. and Gershman, Samuel J.},
eprint = {1604.00289},
file = {:Users/hahosoya/Documents/Mendeley Desktop/56832ee314a023756ca11107c7dd4da6e157ac8d.pdf:pdf},
journal = {Behav. Brain Sci.},
number = {2012},
pages = {1--58},
pmid = {27881212},
title = {{Building machines that learn and think like people}},
volume = {40},
year = {2017}
}
@article{Schurger2015,
author = {Schurger, Aaron and Sarigiannidis, Ioannis and Naccache, Lionel and Sitt, Jacobo D and Dehaene, Stanislas},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proceedings of the National Academy of Sciences 2015 Schurger.pdf:pdf},
journal = {Proc. Natl. Acad. Sci.},
month = {apr},
number = {16},
pages = {E2083--E2092},
title = {{Cortical activity is more stable when sensory stimuli are consciously perceived}},
volume = {112},
year = {2015}
}
@article{Horan2019,
abstract = {During natural behavior, animals actively gather information that is relevant for learning or actions; however, the mechanisms of active sampling are rarely investigated. We tested parietal neurons involved in oculomotor control in a task in which monkeys made saccades to gather visual information relevant to a subsequent action. We show that the neurons encode, before the saccade, the information gain (reduction in decision uncertainty) that the saccade was expected to bring for the following action. Sensitivity to information gain correlates with the monkeys' efficiency at processing the information in the post-saccadic fixation, but is independent of neuronal reward sensitivity. Reward sensitivity, in turn, is unreliable across task contexts, inconsistent with the view that the cells encode economic utility. The findings suggest that parietal cells involved in oculomotor decisions show uncertainty-dependent boosts of neural gain that facilitate the implementation of active sampling policies, including the selection of relevant cues and the efficient use of the information delivered by these cues.},
author = {Horan, Mattias and Daddaoua, Nabil and Gottlieb, Jacqueline},
file = {:Users/hahosoya/Documents/Mendeley Desktop/s41593-019-0440-1.pdf:pdf},
journal = {Nat. Neurosci.},
number = {August},
title = {{Parietal neurons encode information sampling based on decision uncertainty}},
volume = {22},
year = {2019}
}
@article{DeBaene2007,
author = {{De Baene}, W and Premereur, E and Vogels, R},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neurophysiology 2007 De Baene.pdf:pdf},
journal = {J. Neurophysiol.},
month = {jan},
number = {4},
pages = {2900--2916},
title = {{Properties of Shape Tuning of Macaque Inferior Temporal Neurons Examined Using Rapid Serial Visual Presentation}},
volume = {97},
year = {2007}
}
@article{Connor2017,
author = {Connor, Charles E and Knierim, James J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/8FDBE838-E491-4CF5-AE53-CB9A644DB421.pdf:pdf},
journal = {Nat. Neurosci.},
month = {oct},
number = {11},
pages = {1493--1503},
title = {{Integration of objects and space in perception and memory}},
volume = {20},
year = {2017}
}
@article{Yang2021,
abstract = {The last two decades have established that a network of face-selective areas in the temporal lobe of macaque monkeys supports the visual processing of faces. Each area within the network contains a large fraction of face-selective cells. And each area encodes facial identity and head orientation differently. A recent brain-imaging study discovered an area outside of this network selective for naturalistic facial motion, the middle dorsal (MD) face area. This finding offers the opportunity to determine whether coding principles revealed inside the core network would generalize to face areas outside the core network. We investigated the encoding of static faces and objects, facial identity, and head orientation, dimensions which had been studied in multiple areas of the core face-processing network before, as well as facial expressions and gaze. We found that MD populations form a face-selective cluster with a degree of selectivity comparable to that of areas in the core face-processing network. MD encodes facial identity robustly across changes in head orientation and expression, it encodes head orientation robustly against changes in identity and expression, and it encodes expression robustly across changes in identity and head orientation. These three dimensions are encoded in a separable manner. Furthermore, MD also encodes the direction of gaze in addition to head orientation. Thus, MD encodes both structural properties (identity) and changeable ones (expression and gaze) and thus provides information about another animal's direction of attention (head orientation and gaze). MD contains a heterogeneous population of cells that establish a multidimensional code for faces.},
author = {Yang, Zetian and Freiwald, Winrich A.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/e2108283118.full.pdf:pdf},
journal = {Proc. Natl. Acad. Sci. U. S. A.},
keywords = {FMRI-targeted electrophysiology,Face-processing systems,Facial expression,Facial identity,Gaze},
number = {33},
title = {{Joint encoding of facial identity, orientation, gaze, and expression in the middle dorsal face area}},
volume = {118},
year = {2021}
}
@article{Bondy2018,
abstract = {Nature Neuroscience, doi:10.1038/s41593-018-0089-1},
author = {Bondy, Adrian G and Haefner, Ralf M and Cumming, Bruce G},
file = {:Users/hahosoya/Documents/Mendeley Desktop/27141672-3420-4AE3-B093-FB9E82BC8C34.pdf:pdf},
journal = {Nat. Neurosci.},
month = {feb},
number = {4},
pages = {1--15},
title = {{Feedback determines the structure of correlated variability in primary visual cortex}},
volume = {21},
year = {2018}
}
@article{Karklin2012,
abstract = {Abstract Natural sounds exhibit complex statistical regularities at multiple scales. Acoustic events underlying speech, for example, are characterized by precise temporal and frequency relationships, but they can also vary substantially according to the pitch, ... 
},
author = {Karklin, Y and Ekanadham, C and SIMONCELLI, E},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Advances in neural {\ldots} 2012 Karklin.pdf:pdf},
journal = {Adv. neural  {\ldots}},
month = {jan},
title = {{Hierarchical spike coding of sound}},
year = {2012}
}
@article{Fairhall2013,
author = {Fairhall, S L and Caramazza, A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2013 Fairhall.pdf:pdf},
journal = {J. Neurosci.},
month = {jun},
number = {25},
pages = {10552--10558},
title = {{Brain Regions That Represent Amodal Conceptual Knowledge}},
volume = {33},
year = {2013}
}
@article{Yildirim2018,
abstract = {The visual system must not only recognize and localize objects, but perform much richer inferences about the underlying causes in the world that give rise to observed sense data. Analyzing scenes by inverting causal generative models, also known as" analysis-by- synthesis", has a long history in computational vision, and these models have some behavioral support, but they are typically too slow to support online perception and have no known mapping to actual neural circuits. Here we present a neurally plausible model for {\ldots}},
author = {Yildirim, I and Belledonne, M and Freiwald, W and Tenenbaum, J B},
file = {:Users/hahosoya/Documents/Mendeley Desktop/125AD403-5146-4D2B-BBF7-2D3BCCF401F8.pdf:pdf},
journal = {bioRxiv},
month = {jan},
title = {{Efficient inverse graphics in biological face processing}},
volume = {http://dx.},
year = {2018}
}
@article{Li2008,
abstract = {Object recognition is challenging because each object produces myriad retinal images. Responses of neurons from the inferior temporal cortex (IT) are selective to different objects, yet tolerant ("invariant") to changes in object position, scale, and pose. How does the brain construct this neuronal tolerance? We report a form of neuronal learning that suggests the underlying solution. Targeted alteration of the natural temporal contiguity of visual experience caused specific changes in IT position tolerance. This unsupervised temporal slowness learning (UTL) was substantial, increased with experience, and was significant in single IT neurons after just 1 hour. Together with previous theoretical work and human object perception experiments, we speculate that UTL may reflect the mechanism by which the visual stream builds and maintains tolerant object representations.},
address = {McGovern Institute for Brain Research and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.},
author = {Li, Nuo and DiCarlo, James J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Science 2008 Li.pdf:pdf},
journal = {Science},
month = {sep},
number = {5895},
pages = {1502--1507},
title = {{Unsupervised natural experience rapidly alters invariant object representation in visual cortex.}},
volume = {321},
year = {2008}
}
@article{Fischer2014,
abstract = {Nature Neuroscience 17, 738 (2014). doi:10.1038/nn.3689},
author = {Fischer, Jason and Whitney, David},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature Publishing Group 2014 Fischer.pdf:pdf},
journal = {Nat. Neurosci.},
month = {mar},
number = {5},
pages = {738--743},
title = {{Serial dependence in visual perception}},
volume = {17},
year = {2014}
}
@article{Bach2005,
author = {Bach, F R and Jordan, M I},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2005 Bach.pdf:pdf},
month = {jan},
title = {{A probabilistic interpretation of canonical correlation analysis}},
year = {2005}
}
@article{Hosoya2015,
author = {Hosoya, H and Hyv{\"{a}}rinen, A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2015 Hosoya.pdf:pdf},
journal = {J. Neurosci.},
month = {jul},
number = {29},
pages = {10412--10428},
title = {{A Hierarchical Statistical Model of Natural Images Explains Tuning Properties in V2}},
volume = {35},
year = {2015}
}
@article{Jordan1999,
author = {Jordan, M I and Ghahramani, Z and Jaakkola, T S and Saul, L K},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Mach Learn 1999 Jordan.pdf:pdf},
journal = {Mach. Learn.},
month = {jan},
number = {2},
pages = {183--233},
title = {{An introduction to variational methods for graphical models}},
volume = {37},
year = {1999}
}
@article{Hachiya,
abstract = {... For this reason, we will employ uLSIF in our experiments. A brief review of uLSIF is 
provided in B. A MATLAB implementation of IWLSPC is available from 'http:
// sugiyama -www.cs.titech.ac.jp/∼ hachiya /software/IWLSPC/'. 3 Related Works ...
},
author = {Hachiya, H and et Al},
file = {:Users/hahosoya/Documents/Mendeley Desktop/ibisml.org Hachiya.pdf:pdf},
journal = {ibisml.org},
title = {{Importance-Weighted Least-Squares Probabilistic Classifier for Covariate Shift Adaptation with Application to Human Activity Recognition}},
}
@article{Mimica2018,
abstract = {Animals constantly update their body posture to meet behavioral demands, but little is known about the neural signals on which this depends. We therefore tracked freely foraging rats in three dimensions while recording from the posterior parietal cortex (PPC) and the frontal motor cortex (M2), areas critical for movement planning and navigation. Both regions showed strong tuning to posture of the head, neck, and back, but signals for movement were much less dominant. Head and back representations were organized topographically across the PPC and M2, and more neurons represented postures that occurred less often. Simultaneous recordings across areas were sufficiently robust to decode ongoing behavior and showed that spiking in the PPC tended to precede that in M2. Both the PPC and M2 strongly represent posture by using a spatially organized, energetically efficient population code.},
address = {Kavli Institute for Systems Neuroscience, Norwegian University of Science and Technology, NO-7489 Trondheim, Norway. bartul.mimica@ntnu.no whitlock@ntnu.no.},
author = {Mimica, Bartul and Dunn, Benjamin A and Tombaz, Tuce and Bojja, V P T N C Srikanth and Whitlock, Jonathan R},
file = {:Users/hahosoya/Documents/Mendeley Desktop/A7B6F63D-03C8-4124-99F3-8895E3B821CA.pdf:pdf},
journal = {Science},
month = {nov},
number = {6414},
pages = {584--589},
title = {{Efficient cortical coding of 3D posture in freely behaving rats.}},
volume = {362},
year = {2018}
}
@article{Kersten2004,
abstract = {We perceive the shapes and material properties of objects quickly and reliably despite the complexity and objective ambiguities of natural images. Typical images are highly complex because they consist of many objects embedded in background clutter. Moreover, the image features of ...},
author = {Kersten, Daniel and Mamassian, Pascal and Yuille, Alan},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Annu. Rev. Psychol. 2004 Kersten.pdf:pdf},
journal = {Annu. Rev. Psychol.},
month = {feb},
number = {1},
pages = {271--304},
title = {{Object Perception as Bayesian Inference}},
volume = {55},
year = {2004}
}
@article{Spratling2009,
abstract = {This paper demonstrates that nonnegative matrix factorisation is mathematically related to a class of neural networks that employ negative feedback as a mechanism of competition. This observation inspires a novel learning algorithm which we call Divisive Input Modulation (DIM). The proposed algorithm provides a mathematically simple and computationally efficient method for the unsupervised learning of image components, even in conditions where these elementary features overlap considerably. To test the proposed algorithm, a novel artificial task is introduced which is similar to the frequently-used bars problem but employs squares rather than bars to increase the degree of overlap between components. Using this task, we investigate how the proposed method performs on the parsing of artificial images composed of overlapping features, given the correct representation of the individual components; and secondly, we investigate how well it can learn the elementary components from artificial training images. We compare the performance of the proposed algorithm with its predecessors including variations on these algorithms that have produced state-of-the-art performance on the bars problem. The proposed algorithm is more successful than its predecessors in dealing with overlap and occlusion in the artificial task that has been used to assess performance.},
address = {Division of Engineering, King's College London, London WC2R 2LS, UK. michael.spratling@kcl.ac.uk},
author = {Spratling, M W and {De Meyer}, K and Kompass, R},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Computational Intelligence and Neuroscience 2009 Spratling.pdf:pdf},
journal = {Comput. Intell. Neurosci.},
month = {jan},
pages = {1--19},
title = {{Unsupervised Learning of Overlapping Image Components Using Divisive Input Modulation}},
volume = {2009},
year = {2009}
}
@article{Murris2021,
abstract = {Perception improves by repeated practice with visual stimuli, a phenomenon known as visual perceptual learning (VPL). The interplay of attentional and neuromodulatory reward signals is hypothesized to cause these behavioral and associated neuronal changes, although VPL can occur without attention (i.e., task-irrelevant VPL). In addition, task-relevant VPL can be category-selective for simple attended oriented stimuli. Yet, it is unclear whether category-selective task-irrelevant VPL occurs and which brain centers mediate underlying forms of adult cortical plasticity. Here, we show that pairing subliminal complex visual stimuli (faces and bodies) with electrical microstimulation of the ventral tegmental area (VTA-EM) causes category-selective task-irrelevant VPL. These perceptual improvements are accompanied by fMRI signal changes in early and late visual and frontal areas, as well as the cerebellum, hippocampus, claustrum, and putamen. In conclusion, Pavlovian pairing of unattended complex stimuli with VTA-EM causes category-selective learning accompanied by changes of cortical and subcortical neural representations in macaques.},
author = {Murris, Sjoerd R. and Arsenault, John T. and Raman, Rajani and Vogels, Rufin and Vanduffel, Wim},
file = {:Users/hahosoya/Documents/Mendeley Desktop/1-s2.0-S0896627321001136-main.pdf:pdf},
journal = {Neuron},
keywords = {Attention,Dopamine,Microstimulation,Neuromodulation,Rhesus monkey,Task-Irrelevant,Ventral Tegmental Area,Visual Perceptual Learning,fMRI},
number = {8},
pages = {1381--1395.e7},
pmid = {33667342},
title = {{Electrical stimulation of the macaque ventral tegmental area drives category-selective learning without attention}},
volume = {109},
year = {2021}
}
@article{Viola2004,
abstract = {Abstract This paper describes a face detection framework that is capable of processing images extremely rapidly while achieving high detection rates. There are three key contributions. The first is the introduction of a new image representation called the “ ...
},
author = {Viola, P and Jones, M J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/International journal on machine learning 2004 Viola.pdf:pdf},
journal = {Int. J. Comput. Vis.},
month = {jan},
number = {2},
pages = {137--154},
title = {{Robust real-time face detection}},
volume = {57},
year = {2004}
}
@article{Schuck2019,
abstract = {Sequential neural activity patterns related to spatial experiences are "replayed" in the hippocampus of rodents during rest. We investigated whether replay of nonspatial sequences can be detected noninvasively in the human hippocampus. Participants underwent functional magnetic resonance imaging (fMRI) while resting after performing a decision-making task with sequential structure. Hippocampal fMRI patterns recorded at rest reflected sequentiality of previously experienced task states, with consecutive patterns corresponding to nearby states. Hippocampal sequentiality correlated with the fidelity of task representations recorded in the orbitofrontal cortex during decision-making, which were themselves related to better task performance. Our findings suggest that hippocampal replay may be important for building representations of complex, abstract tasks elsewhere in the brain and establish feasibility of investigating fast replay signals with fMRI.},
author = {Schuck, Nicolas W. and Niv, Yael},
file = {:Users/hahosoya/Documents/Mendeley Desktop/eaaw5181.full.pdf:pdf},
journal = {Science},
number = {6447},
title = {{Sequential replay of nonspatial task states in the human hippocampus}},
volume = {364},
year = {2019}
}
@article{DiCarlo2007,
abstract = {Despite tremendous variation in the appearance of visual objects, primates can recognize a multitude of objects, each in a fraction of a second, with no apparent effort. However, the brain mechanisms that enable this fundamental ability are not understood. Drawing on ideas from neurophysiology and computation, we present a graphical perspective on the key computational challenges of object recognition, and argue that the format of neuronal population representation and a property that we term 'object tangling' are central. We use this perspective to show that the primate ventral visual processing stream achieves a particularly effective solution in which single-neuron invariance is not the goal. Finally, we speculate on the key neuronal mechanisms that could enable this solution, which, if understood, would have far-reaching implications for cognitive neuroscience.},
address = {McGovern Institute for Brain Research, and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. dicarlo@mit.edu},
author = {DiCarlo, James J and Cox, David D},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Trends in cognitive Sciences 2007 DiCarlo.pdf:pdf},
journal = {Trends Cogn. Sci.},
month = {aug},
number = {8},
pages = {333--341},
title = {{Untangling invariant object recognition.}},
volume = {11},
year = {2007}
}
@article{Stokes2013,
abstract = {Neuron, doi:10.1016/j.neuron.2013.01.039},
author = {Stokes, Mark G and Kusunoki, Makoto and Sigala, Natasha and Nili, Hamed and Gaffan, David and Duncan, John},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neuron 2013 Stokes.pdf:pdf},
journal = {Neuron},
month = {apr},
pages = {1--12},
title = {{Dynamic Coding for Cognitive Control in Prefrontal Cortex}},
year = {2013}
}
@article{Aitchison2016,
author = {Aitchison, Laurence and Lengyel, M{\'{a}}t{\'{e}}},
editor = {Kording, Konrad P},
file = {:Users/hahosoya/Documents/Mendeley Desktop/17DC2387-D393-4F5D-971C-534029026558.pdf:pdf},
journal = {PLoS Comput. Biol.},
month = {dec},
number = {12},
pages = {e1005186--24},
title = {{The Hamiltonian Brain: Efficient Probabilistic Inference with Excitatory-Inhibitory Neural Circuit Dynamics}},
volume = {12},
year = {2016}
}
@article{Weston2015,
abstract = {We describe a new class of learning models called memory networks. Memory networks reason with inference components combined with a long-term memory component; they learn how to use these jointly. The long-term memory can be read and written to, with the goal of using it for prediction. We investigate these models in the context of question answering (QA) where the long-term memory effectively acts as a (dynamic) knowledge base, and the output is a textual response. We evaluate them on a large-scale QA task, and a smaller, but more complex, toy task generated from a simulated world. In the latter, we show the reasoning power of such models by chaining multiple supporting sentences to answer questions that require understanding the intension of verbs.},
archivePrefix = {arXiv},
arxivId = {1410.3916},
author = {Weston, Jason and Chopra, Sumit and Bordes, Antoine},
eprint = {1410.3916},
file = {:Users/hahosoya/Documents/Mendeley Desktop/af2dd6743b26f6cb6ae907d62e859cf7879f1a8e.pdf:pdf},
journal = {3rd Int. Conf. Learn. Represent. ICLR 2015 - Conf. Track Proc.},
pages = {1--15},
title = {{Memory networks}},
year = {2015}
}
@article{Hesse2016,
author = {Hesse, J K and Tsao, D Y},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2016 Hesse.pdf:pdf},
journal = {J. Neurosci.},
month = {nov},
number = {44},
pages = {11338--11349},
title = {{Consistency of Border-Ownership Cells across Artificial Stimuli, Natural Stimuli, and Stimuli with Ambiguous Contours}},
volume = {36},
year = {2016}
}
@article{Olshausen2003,
author = {Olshausen, B A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2003 Olshausen.pdf:pdf},
journal = {Image Process. 2003. ICIP 2003. Proceedings. 2003 Int. Conf.},
month = {jan},
pages = {I--41--4 vol. 1},
title = {{Learning sparse, overcomplete representations of time-varying natural images}},
volume = {1},
year = {2003}
}
@article{Hinton2000,
author = {Hinton, Geoffrey E and Brown, A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Advances in neural information processing systems 2000 Hinton.pdf:pdf},
journal = {Adv. Neural Inf. Process. Syst.},
month = {jan},
pages = {122--129},
title = {{Spiking boltzmann machines}},
volume = {12},
year = {2000}
}
@article{Smyth2003,
abstract = {... Each represents the two-dimensional spatial organization of those features in the natural scenes ... 
ON) receptive - field regions, whereas dark areas suggest inhibitory (OFF) receptive - field regions. ...
to reflect the limited orientation and spatial-frequency response of simple  cells (cf. ...
},
author = {Smyth, D and Willmore, B and et Al},
file = {:Users/hahosoya/Documents/Mendeley Desktop/The Journal of {\ldots} 2003 Smyth.pdf:pdf},
journal = {J. {\ldots}},
month = {jan},
title = {{The receptive-field organization of simple cells in primary visual cortex of ferrets under natural scene stimulation}},
year = {2003}
}
@article{Wiltschut2009,
author = {Wiltschut, J and Hamker, F H},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Visual Neuroscience 2009 Wiltschut.pdf:pdf},
journal = {Vis. Neurosci.},
month = {jan},
number = {01},
pages = {157},
title = {{Efficient coding correlates with spatial frequency tuning in a model of V1 receptive field organization}},
volume = {26},
year = {2009}
}
@article{Dayan1995,
abstract = {Discovering the structure inherent in a set of patterns is a fundamental aim of statistical inference or learning. One fruitful approach is to build a parameterized stochastic generative model, independent draws from which are likely to produce the patterns. For all but the simplest generative models, each pattern can be generated in exponentially many ways. It is thus intractable to adjust the parameters to maximize the probability of the observed patterns. We describe a way of finessing this combinatorial explosion by maximizing an easily computed lower bound on the probability of the observations. Our method can be viewed as a form of hierarchical self-supervised learning that may relate to the function of bottom-up and top-down cortical processing pathways.},
address = {Department of Computer Science, University of Toronto, Ontario, Canada.},
author = {Dayan, P and Hinton, G E and Neal, R M and Zemel, R S},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neural Computation 1995 Dayan.pdf:pdf},
journal = {Neural Comput.},
month = {sep},
number = {5},
pages = {889--904},
title = {{The Helmholtz machine.}},
volume = {7},
year = {1995}
}
@article{Koch2007,
author = {Koch, Christof and Tsuchiya, Naotsugu},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Trends in cognitive Sciences 2007 Koch.pdf:pdf},
journal = {Trends Cogn. Sci.},
month = {jan},
number = {1},
pages = {16--22},
title = {{Attention and consciousness: two distinct brain processes}},
volume = {11},
year = {2007}
}
@article{Sliwa2017,
abstract = {Abstract Primate cognition requires interaction processing. Interactions can reveal otherwise hidden properties of intentional agents, such as thoughts and feelings, and of inanimate objects, such as mass and material. Where and how interaction analyses are implemented in the brain is unknown. Using whole-brain functional magnetic resonance imaging in macaque monkeys, we discovered a network centered in the medial and ventrolateral ...
},
author = {Sliwa, J and Freiwald, W A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Science 2017 Sliwa.pdf:pdf},
journal = {Science},
month = {jan},
title = {{A dedicated network for social interaction processing in the primate brain}},
year = {2017}
}
@article{Rehn2007,
abstract = {Abstract Computational models of primary visual cortex have demonstrated that principles of efficient coding and neuronal sparseness can explain the emergence of neurones with localised oriented receptive fields . Yet, existing models have failed to predict the diverse  ...},
author = {Rehn, M and Sommer, F T},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of computational neuroscience 2007 Rehn.pdf:pdf},
journal = {J. Comput. Neurosci.},
month = {jan},
title = {{A network that uses few active neurones to code visual input predicts the diverse shapes of cortical receptive fields}},
year = {2007}
}
@article{Devlin2019,
abstract = {We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models (Peters et al., 2018a; Radford et al., 2018), BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5% (7.7% point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement).},
archivePrefix = {arXiv},
arxivId = {1810.04805},
author = {Devlin, Jacob and Chang, Ming Wei and Lee, Kenton and Toutanova, Kristina},
eprint = {1810.04805},
file = {:Users/hahosoya/Documents/Mendeley Desktop/a7b7a17d0cf9953e2079d8935dce32794ba1ed98.pdf:pdf},
isbn = {9781950737130},
journal = {NAACL HLT 2019 - 2019 Conf. North Am. Chapter Assoc. Comput. Linguist. Hum. Lang. Technol. - Proc. Conf.},
number = {Mlm},
pages = {4171--4186},
title = {{BERT: Pre-training of deep bidirectional transformers for language understanding}},
volume = {1},
year = {2019}
}
@article{Stringer2019,
abstract = {A neuronal population encodes information most efficiently when its stimulus responses are high-dimensional and uncorrelated, and most robustly when they are lower-dimensional and correlated. Here we analysed the dimensionality of the encoding of natural images by large populations of neurons in the visual cortex of awake mice. The evoked population activity was high-dimensional, and correlations obeyed an unexpected power law: the nth principal component variance scaled as 1/n. This scaling was not inherited from the power law spectrum of natural images, because it persisted after stimulus whitening. We proved mathematically that if the variance spectrum was to decay more slowly then the population code could not be smooth, allowing small changes in input to dominate population activity. The theory also predicts larger power-law exponents for lower-dimensional stimulus ensembles, which we validated experimentally. These results suggest that coding smoothness may represent a fundamental constraint that determines correlations in neural population codes.},
author = {Stringer, Carsen and Pachitariu, Marius and Steinmetz, Nicholas and Carandini, Matteo and Harris, Kenneth D.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/s41586-019-1346-5.pdf:pdf},
journal = {Nature},
title = {{High-dimensional geometry of population responses in visual cortex}},
year = {2019}
}
@article{Shushruth2009,
author = {Shushruth, S and Ichida, J M and Levitt, J B and Angelucci, A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neurophysiology 2009 Shushruth.pdf:pdf},
journal = {J. Neurophysiol.},
month = {oct},
number = {4},
pages = {2069--2083},
title = {{Comparison of Spatial Summation Properties of Neurons in Macaque V1 and V2}},
volume = {102},
year = {2009}
}
@article{Hegde2000,
abstract = {To explore the role of visual area V2 in shape analysis, we studied the responses of neurons in area V2 of the alert macaque using a set of 128 grating and geometric line stimuli that varied in their shape characteristics and geometric complexity. Simple stimuli included oriented bars and sinusoidal gratings; complex stimuli included angles, arcs, circles, and intersecting lines, plus hyperbolic and polar gratings. We found that most V2 cells responded well to at least some of the complex stimuli, and in many V2 cells the most effective complex stimulus elicited a significantly larger response than the most effective bar or sinusoid. Approximately one-third of the V2 cells showed significant differential responsiveness to various complex shape characteristics, and many were also selective for the orientation, size, and/or spatial frequency of the preferred shape. These results indicate that V2 cells explicitly represent complex shape information and suggest specific types of higher order visual information that V2 cells extract from visual scenes.},
address = {Department of Anatomy and Neurobiology, Washington University School of Medicine, St. Louis, Missouri 63110, USA.},
author = {Hegd{\'{e}}, J and {Van Essen}, D C},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2000 Hegd{\'{e}}.pdf:pdf},
journal = {J. Neurosci.},
month = {mar},
number = {5},
pages = {RC61},
title = {{Selectivity for complex shapes in primate visual area V2.}},
volume = {20},
year = {2000}
}
@article{Jiang2006,
abstract = {Human observers are constantly bombarded with a vast amount of information. Selective attention helps us to quickly process what is important while ignoring the irrelevant. In this study, we demonstrate that information that has not entered observers' consciousness, such as interocularly suppressed (invisible) erotic pictures, can direct the distribution of spatial attention. Furthermore, invisible erotic information can either attract or repel observers' spatial attention depending on their gender and sexual orientation. While unaware of the suppressed pictures, heterosexual males' attention was attracted to invisible female nudes, heterosexual females' attention was attracted to invisible male nudes, gay males behaved similarly to heterosexual females, and gay/bisexual females performed in-between heterosexual males and females.},
address = {Department of Psychology, University of Minnesota, 75 East River Parkway, Minneapolis, MN 55455, USA.},
author = {Jiang, Yi and Costello, Patricia and Fang, Fang and Huang, Miner and He, Sheng},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proc. Natl. Acad. Sci. U.S.A. 2006 Jiang.pdf:pdf},
journal = {Proc. Natl. Acad. Sci. U. S. A.},
month = {nov},
number = {45},
pages = {17048--17052},
title = {{A gender- and sexual orientation-dependent spatial attentional effect of invisible images.}},
volume = {103},
year = {2006}
}
@article{Adelson1985,
author = {Adelson, Edward H and Bergen, James R},
file = {:Users/hahosoya/Documents/Mendeley Desktop/J. Opt. Soc. Am. A 1985 Adelson.pdf:pdf},
journal = {J. Opt. Soc. Am. A},
month = {jan},
number = {2},
pages = {284--299},
title = {{Spatiotemporal energy models for the perception of motion}},
volume = {2},
year = {1985}
}
@article{Cossell2016,
abstract = {Nature 518, 399 (2015). doi:10.1038/nature14182},
author = {Cossell, Lee and Iacaruso, Maria Florencia and Muir, Dylan R and Houlton, Rachael and Sader, Elie N and Ko, Ho and Hofer, Sonja B and Mrsic-Flogel, Thomas D},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature 2016 Cossell.pdf:pdf},
journal = {Nature},
month = {apr},
number = {7539},
pages = {399--403},
title = {{Functional organization of excitatory synaptic strength in primary visual cortex}},
volume = {518},
year = {2016}
}
@article{Tang2018,
author = {Tang, Hanlin and Schrimpf, Martin and Lotter, William and Moerman, Charlotte and Paredes, Ana and {Ortega Caro}, Josue and Hardesty, Walter and Cox, David and Kreiman, Gabriel},
file = {:Users/hahosoya/Documents/Mendeley Desktop/C8F4D6F3-9C8C-4062-AAE9-071740705E38.pdf:pdf},
journal = {Proc. Natl. Acad. Sci. U. S. A.},
month = {aug},
number = {35},
pages = {8835--8840},
title = {{Recurrent computations for visual pattern completion}},
volume = {115},
year = {2018}
}
@article{Moutoussis2011,
abstract = {Introduction. When deciding about the cause underlying serially presented events, patients with delusions utilise fewer events than controls, showing a Jumping-to-Conclusions bias. This has been widely hypothesised to be because patients expect to incur higher costs if they sample more information. This hypothesis is, however, unconfirmed. Methods. The hypothesis was tested by analysing patient and control data using two models. The models provided explicit, quantitative variables characterising decision making. One model was based on calculating the potential costs of making a decision; the other compared a measure of certainty to a fixed threshold. Results. Differences between paranoid participants and controls were found, but not in the way that was previously hypothesised. A greater noise in decision making (relative to the effective motivation to get the task right), rather than greater perceived costs, best accounted for group differences. Paranoid participants also deviated from ideal Bayesian reasoning more than healthy controls. Conclusions. The Jumping-to-Conclusions Bias is unlikely to be due to an overestimation of the cost of gathering more information. The analytic approach we used, involving a Bayesian model to estimate the parameters characterising different participant populations, is well suited to testing hypotheses regarding hidden variables underpinning observed behaviours. {\textcopyright} 2011 Psychology Press, an imprint of the Taylor & Francis Group, an Informa business.},
author = {Moutoussis, Michael and Bentall, Richard P. and El-Deredy, Wael and Dayan, Peter},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Moutoussis et al. - 2011 - Bayesian modelling of Jumping-to-Conclusions bias .pdf:pdf},
journal = {Cogn. Neuropsychiatry},
keywords = {Bayesian reasoning,Jumping-to-Conclusions,Paranoia,Psychosis,Sequential probability ratio test},
number = {5},
pages = {422--447},
title = {{Bayesian modelling of Jumping-to-Conclusions bias in delusional patients}},
volume = {16},
year = {2011}
}
@article{Andermann2011,
abstract = {The mouse is emerging as an important model for understanding how sensory neocortex extracts cues to guide behavior, yet little is known about how these cues are processed beyond primary cortical areas. Here, we used two-photon calcium imaging in awake mice to compare visual responses in primary visual cortex (V1) and in two downstream target areas, AL and PM. Neighboring V1 neurons had diverse stimulus preferences spanning five octaves in spatial and temporal frequency. By contrast, AL and PM neurons responded best to distinct ranges of stimulus parameters. Most strikingly, AL neurons preferred fast-moving stimuli while PM neurons preferred slow-moving stimuli. By contrast, neurons in V1, AL, and PM demonstrated similar selectivity for stimulus orientation but not for stimulus direction. Based on these findings, we predict that area AL helps guide behaviors involving fast-moving stimuli (e.g., optic flow), while area PM helps guide behaviors involving slow-moving objects.},
address = {Department of Neurobiology, Harvard Medical School, Goldenson 243, 220 Longwood Avenue, Boston, MA 02115, USA.},
author = {Andermann, Mark L and Kerlin, Aaron M and Roumis, Demetris K and Glickfeld, Lindsey L and Reid, R Clay},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neuron 2011 Andermann.pdf:pdf},
journal = {Neuron},
month = {dec},
number = {6},
pages = {1025--1039},
title = {{Functional specialization of mouse higher visual cortical areas.}},
volume = {72},
year = {2011}
}
@article{Merriam2013,
author = {Merriam, E P and Gardner, J L and Movshon, J A and Heeger, D J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2013 Merriam.pdf:pdf},
journal = {J. Neurosci.},
month = {jun},
number = {24},
pages = {9879--9889},
title = {{Modulation of Visual Responses by Gaze Direction in Human Visual Cortex}},
volume = {33},
year = {2013}
}
@article{Logothetis1998,
abstract = {Conscious experience involves perceiving, attending, remembering, and recognizing. Recent neuroscientific research has made significant contributions to our understanding of the mechanisms that mediate such capacities. Physiological and neuropsychological investigations have provided increasingly detailed descriptions of the location and functional properties of the brain structures involved in conscious perception, in attentive behavior and working memory, and in the recognition of objects. Such studies suggest that awareness of a visual stimulus probably reflects the interconnectivity and the type of cells involved in the representation of this stimulus, rather than the activity of specific circumscribed visual areas or processing streams.},
address = {Max Planck Institute for Biological Cybernetics, T{\"{u}}bingen, Germany. nikos.logothetis@tuebingen.mpg.de},
author = {Logothetis, N},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Curr. Opin. Neurobiol. 1998 Logothetis.pdf:pdf},
journal = {Curr. Opin. Neurobiol.},
month = {aug},
number = {4},
pages = {536--544},
title = {{Object vision and visual awareness.}},
volume = {8},
year = {1998}
}
@article{Maunsell1983,
abstract = {1. Recordings were made from single units in the middle temporal visual area (MT) of anesthetized, paralyzed macaque monkeys. A computer-driven stimulator was used to make quantitative tests of selectivity for stimulus direction, speed, and orientation. The data were taken from 168 units that were histologically identified as being in MT. 2. The results confirm previous reports of a high degree of direction selectivity in MT. The response above background to stimuli moving in a unit's preferred direction was, an average, 10.9 times that to stimuli moving in the opposite direction. There was a marked tendency for nearby units to have similar preferred directions. 3. Most units were also sharply tuned for the speed of stimulus motion. For some cells the response fell to less than half-maximal at speeds only a factor of two from the optimum; on average, responses were greater than half-maximal only over a 7.7-fold range of speed. The distribution of preferred speeds for different units was unimodal, with a peak near 32 degrees/s; the total range of preferred speeds extended from 2 to 256 degrees/s. Nearby units generally responded best to similar speeds of motion. 4. Most units in MT showed selectivity for stimulus orientation when tested with stationary, flashed bars. However, stationary stimuli generally elicited only brief responses; when averaged over the duration of the stimulus, the responses were much less than those to moving stimuli. The preferred orientation was usually, but not always, perpendicular to the preferred direction of movement. 5. A comparison of the results of the present study with a previous quantitative investigation in the owl monkey shows a striking similarity in response properties in MT of the two species. 6. The presence of both direction and speed selectivity in MT of the macaque suggests that this area is more specialized for the analysis of visual motion than has been previously recognized.},
author = {Maunsell, J H and {Van Essen}, D C},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neurophysiology 1983 Maunsell.pdf:pdf},
journal = {J. Neurophysiol.},
month = {may},
number = {5},
pages = {1127--1147},
title = {{Functional properties of neurons in middle temporal visual area of the macaque monkey. I. Selectivity for stimulus direction, speed, and orientation.}},
volume = {49},
year = {1983}
}
@article{Miura2014,
author = {Miura, K and Inaba, N and Aoki, Y and Kawano, K},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2014 Miura.pdf:pdf},
journal = {J. Neurosci.},
month = {feb},
number = {6},
pages = {2160--2168},
title = {{Difference in Visual Motion Representation between Cortical Areas MT and MST during Ocular Following Responses}},
volume = {34},
year = {2014}
}
@article{DeAngelis1992,
abstract = {Neurons in the visual cortex respond to stimuli with specific and limited ranges of characteristics. This selectivity is generally thought to be based on the organization of receptive - field components, as suggested in the original detailed exploration of the visual  ...},
author = {{De Angelis}, G C and Robson, J G and Ohzawa, I and et Al},
file = {:Users/hahosoya/Documents/Mendeley Desktop/J Neurwphysiol 68 1992 De Angelis.pdf:pdf},
journal = {J Neurwphysiol 68},
month = {jan},
title = {{Organization of suppression in receptive fields of neurons in cat visual cortex}},
year = {1992}
}
@article{Chang2015,
abstract = {We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a collection of datasets providing many semantic annotations for each 3D model such as consistent rigid alignments, parts and bilateral symmetry planes, physical sizes, keywords, as well as other planned annotations. Annotations are made available through a public web-based interface to enable data visualization of object attributes, promote data-driven geometric analysis, and provide a large-scale quantitative benchmark for research in computer graphics and vision. At the time of this technical report, ShapeNet has indexed more than 3,000,000 models, 220,000 models out of which are classified into 3,135 categories (WordNet synsets). In this report we describe the ShapeNet effort as a whole, provide details for all currently available datasets, and summarize future plans.},
archivePrefix = {arXiv},
arxivId = {1512.03012},
author = {Chang, Angel X. and Funkhouser, Thomas and Guibas, Leonidas and Hanrahan, Pat and Huang, Qixing and Li, Zimo and Savarese, Silvio and Savva, Manolis and Song, Shuran and Su, Hao and Xiao, Jianxiong and Yi, Li and Yu, Fisher},
eprint = {1512.03012},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2327c389267019508bbb15788b5caa7bdda30998.pdf:pdf},
title = {{ShapeNet: An Information-Rich 3D Model Repository}},
year = {2015}
}
@article{Sadtler2015,
abstract = {Nature 512, 423 (2014). doi:10.1038/nature13665},
author = {Sadtler, Patrick T and Quick, Kristin M and Golub, Matthew D and Chase, Steven M and Ryu, Stephen I and Tyler-Kabara, Elizabeth C and Yu, Byron M and Batista, Aaron P},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature 2015 Sadtler.pdf:pdf},
journal = {Nature},
month = {apr},
number = {7515},
pages = {423--426},
title = {{Neural constraints on learning}},
volume = {512},
year = {2015}
}
@article{Bishop1998,
abstract = {... where (z) (1 + e?z)?1 is the logistic sigmoid function, pa(Si) denote the ... We illustrate the mixtures 
formalism by considering its application to sigmoid belief  net - works of the form ... Again we have
to introduce variational parameters mi for each component using (4). The parameters ...
},
author = {Bishop, C M and Lawrence, N and et Al},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Advances in neural {\ldots} 1998 Bishop.pdf:pdf},
journal = {Adv. neural  {\ldots}},
month = {jan},
title = {{Approximating posterior distributions in belief networks using mixtures}},
year = {1998}
}
@article{Moreno-Bote2007,
author = {Moreno-Bote, R and Rinzel, J and Rubin, N},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neurophysiology 2007 Moreno-Bote.pdf:pdf},
journal = {J. Neurophysiol.},
month = {sep},
number = {3},
pages = {1125--1139},
title = {{Noise-Induced Alternations in an Attractor Network Model of Perceptual Bistability}},
volume = {98},
year = {2007}
}
@article{Frey1999,
abstract = {We view perceptual tasks such as vision and speech recognition as inference problems where the goal is to estimate the posterior distribution over latent variables (e.g., depth in stereo vision) given the sensory input. The recent flurry of research in independent component analysis exemplifies the importance of inferring the continuous-valued latent variables of input data. The latent variables found by this method are linearly related to the input, but perception requires nonlinear inferences such as classification and depth estimation. In this article, we present a unifying framework for stochastic neural networks with nonlinear latent variables. Nonlinear units are obtained by passing the outputs of linear gaussian units through various nonlinearities. We present a general variational method that maximizes a lower bound on the likelihood of a training set and give results on two visual feature extraction problems. We also show how the variational method can be used for pattern classification and compare the performance of these nonlinear networks with other methods on the problem of handwritten digit recognition.},
address = {Beckman Institute for Advanced Science & Technology, University of Illinois at Urbana-Champaign, 405 North Mathew Avenue, Urbana IL 61801, USA. bfrey@dendrite.beckman.uiuc.edu},
author = {Frey, B J and Hinton, G E},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neural Computation 1999 Frey.pdf:pdf},
journal = {Neural Comput.},
month = {jan},
number = {1},
pages = {193--213},
title = {{Variational learning in nonlinear gaussian belief networks.}},
volume = {11},
year = {1999}
}
@article{Luczak2009,
author = {Luczak, Artur and BarthO, Peter and Harris, Kenneth D},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neuron 2009 Luczak.pdf:pdf},
journal = {Neuron},
month = {may},
number = {3},
pages = {413--425},
title = {{Spontaneous Events Outline the Realm of Possible Sensory Responses in Neocortical Populations}},
volume = {62},
year = {2009}
}
@article{Bao2018,
abstract = {Object recognition in the natural world usually occurs in the presence of multiple surrounding objects, but responses of neurons in inferotemporal (IT) cortex, the large brain area responsible for object recognition, have mostly been studied only to isolated objects. We study rules governing responses to multiple objects by cells in two category-selective regions of macaque IT cortex, the middle lateral face patch (ML) and the middle body patch (MB). We find that responses of single ML and MB cells to pairs of objects can be explained by the widely accepted framework of normalization, with one added ingredient: homogeneous category selectivity of neighboring neurons forming the normalization pool. This rule leads to winner-Take-All, contralateral-Take-All, or weighted averaging behavior in single cells, depending on the category, spatial configuration, and relative contrast of the two objects. The winner-Take-All behavior suggests a potential mechanism for clutter-invariant representation of face and bodies under certain conditions.},
author = {Bao, Pinglei and Tsao, Doris Y.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/s41467-018-04126-7.pdf:pdf},
journal = {Nat. Commun.},
number = {1},
pages = {1--16},
title = {{Representation of multiple objects in macaque category-selective areas}},
volume = {9},
year = {2018}
}
@article{Chang2010,
author = {Chang, Catie and Glover, Gary H},
file = {:Users/hahosoya/Documents/Mendeley Desktop/NeuroImage 2010 Chang.pdf:pdf},
journal = {Neuroimage},
month = {mar},
number = {1},
pages = {81--98},
title = {{Time–frequency dynamics of resting-state brain connectivity measured with fMRI}},
volume = {50},
year = {2010}
}
@article{Inala2020,
author = {Inala, Jeevana Priya and Yang, Yichen},
file = {:Users/hahosoya/Documents/Mendeley Desktop/7611a68a91028652e2e050a43d5262b7aa781efc.pdf:pdf},
number = {NeurIPS},
pages = {1--12},
title = {{Neurosymbolic Transformers for Multi-Agent Communication}},
year = {2020}
}
@article{Rao1999,
abstract = {We describe a model of visual processing in which feedback connections from a higher- to a lower-order visual cortical area carry predictions of lower-level neural activities, whereas the feedforward connections carry the residual errors between the predictions and the actual lower-level activities. When exposed to natural images, a hierarchical network of model neurons implementing such a model developed simple-cell-like receptive fields. A subset of neurons responsible for carrying the residual errors showed endstopping and other extra-classical receptive-field effects. These results suggest that rather than being exclusively feedforward phenomena, nonclassical surround effects in the visual cortex may also result from cortico-cortical feedback as a consequence of the visual system using an efficient hierarchical strategy for encoding natural images.},
address = {Salk Institute, Sloan Center for Theoretical Neurobiology, La Jolla, California, USA. rao@salk.edu},
author = {Rao, R P and Ballard, D H},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature neuroscience 1999 Rao.pdf:pdf},
journal = {Nat. Neurosci.},
month = {jan},
number = {1},
pages = {79--87},
title = {{Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects.}},
volume = {2},
year = {1999}
}
@article{Hyvarinen1999,
abstract = {Sparse coding is a method for finding a representation of data in which each of the components of the representation is only rarely significantly active. Such a representation is closely related to redundancy reduction and independent component analysis, and has some neurophysiological plausibility. In this article, we show how sparse coding can be used for denoising. Using maximum likelihood estimation of nongaussian variables corrupted by gaussian noise, we show how to apply a soft-thresholding (shrinkage) operator on the components of sparse coding so as to reduce noise. Our method is closely related to the method of wavelet shrinkage, but it has the important benefit over wavelet methods that the representation is determined solely by the statistical properties of the data. The wavelet representation, on the other hand, relies heavily on certain mathematical properties (like self-similarity) that may be only weakly related to the properties of natural data.},
address = {Helsinki University of Technology, Laboratory of Computer and Information Science, FIN-02015 HUT, Finland.},
author = {Hyv{\"{a}}rinen, A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neural Computation 1999 Hyv{\"{a}}rinen.pdf:pdf},
journal = {Neural Comput.},
month = {oct},
number = {7},
pages = {1739--1768},
title = {{Sparse code shrinkage: denoising of nongaussian data by maximum likelihood estimation}},
volume = {11},
year = {1999}
}
@article{Dupont2018,
abstract = {We present a framework for learning disentangled and interpretable jointly continuous and discrete representations in an unsupervised manner. By augmenting the continuous latent distribution of variational autoencoders with a relaxed discrete distribution and controlling the amount of information encoded in each latent unit, we show how continuous and categorical factors of variation can be discovered automatically from data. Experiments show that the framework disentangles continuous and discrete generative factors on various datasets and outperforms current disentangling methods when a discrete generative factor is prominent.},
archivePrefix = {arXiv},
arxivId = {1804.00104},
author = {Dupont, Emilien},
eprint = {1804.00104},
file = {:Users/hahosoya/Documents/Mendeley Desktop/148dea3afc132843254c46f05269c10f120e9a30.pdf:pdf},
journal = {Adv. Neural Inf. Process. Syst.},
number = {2016},
pages = {710--720},
title = {{Learning disentangled joint continuous and discrete representations}},
volume = {2018-Decem},
year = {2018}
}
@article{Yamins2013,
author = {Yamins, Daniel L and Hong, Ha and Cadieu, Charles and DiCarlo, James J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2013 Yamins.pdf:pdf},
journal = {Adv. Neural Inf. Process. Syst.},
month = {jan},
pages = {3093--3101},
title = {{Hierarchical Modular Optimization of Convolutional Networks Achieves Representations Similar to Macaque IT and Human Ventral Stream}},
year = {2013}
}
@article{Rust2010,
abstract = {Our ability to recognize objects despite large changes in position, size, and context is achieved through computations that are thought to increase both the shape selectivity and the tolerance ("invariance") of the visual representation at successive stages of the ventral pathway [visual cortical areas V1, V2, and V4 and inferior temporal cortex (IT)]. However, these ideas have proven difficult to test. Here, we consider how well population activity patterns at two stages of the ventral stream (V4 and IT) discriminate between, and generalize across, different images. We found that both V4 and IT encode natural images with similar fidelity, whereas the IT population is much more sensitive to controlled, statistical scrambling of those images. Scrambling sensitivity was proportional to receptive field (RF) size in both V4 and IT, suggesting that, on average, the number of visual feature conjunctions implemented by a V4 or IT neuron is directly related to its RF size. We also found that the IT population could better discriminate between objects across changes in position, scale, and context, thus directly demonstrating a V4-to-IT gain in tolerance. This tolerance gain could be accounted for by both a decrease in single-unit sensitivity to identity-preserving transformations (e.g., an increase in RF size) and an increase in the maintenance of rank-order object selectivity within the RF. These results demonstrate that, as visual information travels from V4 to IT, the population representation is reformatted to become more selective for feature conjunctions and more tolerant to identity preserving transformations, and they reveal the single-unit response properties that underlie that reformatting.},
address = {McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA. nrust@sas.upenn.edu},
author = {Rust, Nicole C and DiCarlo, James J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2010 Rust.pdf:pdf},
journal = {J. Neurosci.},
month = {sep},
number = {39},
pages = {12978--12995},
title = {{Selectivity and tolerance ("invariance") both increase as visual information propagates from cortical area V4 to IT.}},
volume = {30},
year = {2010}
}
@article{ElShafey2013,
abstract = {In this paper, we present a scalable and exact solution for probabilistic linear discriminant analysis (PLDA). PLDA is a probabilistic model that has been shown to provide state-of-the-art performance for both face and speaker recognition. However, it has one major drawback: At training time estimating the latent variables requires the inversion and storage of a matrix whose size grows quadratically with the number of samples for the identity (class). To date, two approaches have been taken to deal with this problem, to 1) use an exact solution that calculates this large matrix and is obviously not scalable with the number of samples or 2) derive a variational approximation to the problem. We present a scalable derivation which is theoretically equivalent to the previous nonscalable solution and thus obviates the need for a variational approximation. Experimentally, we demonstrate the efficacy of our approach in two ways. First, on labeled faces in the wild, we illustrate the equivalence of our scalable implementation with previously published work. Second, on the large Multi-PIE database, we illustrate the gain in performance when using more training samples per identity (class), which is made possible by the proposed scalable formulation of PLDA.},
address = {Idiap Research Institute and Ecole Polytechnique F{\'{e}}d{\'{e}}rale de Lausanne, Martigny 1920, Switzerland. laurent.el-shafey@idiap.ch},
author = {{El Shafey}, Laurent and McCool, Chris and Wallace, Roy and Marcel, S{\'{e}}bastien},
file = {:Users/hahosoya/Documents/Mendeley Desktop/D27FB70E-CC30-4C89-A7C1-11DA49C6D6D3.pdf:pdf},
journal = {IEEE Trans. Pattern Anal. Mach. Intell.},
month = {jul},
number = {7},
pages = {1788--1794},
title = {{A scalable formulation of probabilistic linear discriminant analysis: applied to face recognition.}},
volume = {35},
year = {2013}
}
@article{Lamme2006,
author = {Lamme, Victor A F},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Trends in cognitive Sciences 2006 Lamme.pdf:pdf},
journal = {Trends Cogn. Sci.},
month = {nov},
number = {11},
pages = {494--501},
title = {{Towards a true neural stance on consciousness}},
volume = {10},
year = {2006}
}
@article{Harville2008,
author = {Harville, David A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2008 Harville.pdf:pdf},
month = {jan},
title = {{Matrix algebra from a statistician's perspective}},
year = {2008}
}
@article{Willmore2010,
abstract = {Area V2 is a major visual processing stage in mammalian visual cortex, but little is currently known about how V2 encodes information during natural vision. To determine how V2 represents natural images, we used a novel nonlinear system identification approach to obtain quantitative estimates of spatial tuning across a large sample of V2 neurons. We compared these tuning estimates with those obtained in area V1, in which the neural code is relatively well understood. We find two subpopulations of neurons in V2. Approximately one-half of the V2 neurons have tuning that is similar to V1. The other half of the V2 neurons are selective for complex features such as those that occur in natural scenes. These neurons are distinguished from V1 neurons mainly by the presence of stronger suppressive tuning. Selectivity in these neurons therefore reflects a balance between excitatory and suppressive tuning for specific features. These results provide a new perspective on how complex shape selectivity arises, emphasizing the role of suppressive tuning in determining stimulus selectivity in higher visual cortex.},
address = {Psychology Department, Helen Wills Neuroscience Institute, and Physics Department, University of California, Berkeley, Berkeley, California 94720-1650, USA.},
author = {Willmore, Ben D B and Prenger, Ryan J and Gallant, Jack L},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2010 Willmore.pdf:pdf},
journal = {J. Neurosci.},
month = {feb},
number = {6},
pages = {2102--2114},
title = {{Neural representation of natural images in visual area V2.}},
volume = {30},
year = {2010}
}
@article{Sanada2014,
author = {Sanada, T M and DeAngelis, G C},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2014 Sanada.pdf:pdf},
journal = {J. Neurosci.},
month = {nov},
number = {47},
pages = {15508--15521},
title = {{Neural Representation of Motion-In-Depth in Area MT}},
volume = {34},
year = {2014}
}
@article{Hinkle2002,
author = {Hinkle, David A and Connor, Charles E},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature neuroscience 2002 Hinkle-1.pdf:pdf},
journal = {Nat. Neurosci.},
month = {jan},
number = {7},
pages = {665--670},
title = {{Three-dimensional orientation tuning in macaque area V4}},
volume = {5},
year = {2002}
}
@article{Okazawa2015,
author = {Okazawa, Gouki and Tajima, Satohiro and Komatsu, Hidehiko},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proceedings of the National Academy of Sciences 2015 Okazawa.pdf:pdf},
journal = {Proc. Natl. Acad. Sci.},
month = {jan},
number = {4},
pages = {E351--E360},
title = {{Image statistics underlying natural texture selectivity of neurons in macaque V4}},
volume = {112},
year = {2015}
}
@article{Zhou2019,
abstract = {Posterior parietal cortex (PPC) activity correlates with monkeys' decisions during visual discrimination and categorization tasks. However, recent work has questioned whether decision-correlated PPC activity plays a causal role in such decisions. That study focused on PPC's contribution to motor aspects of decisions (deciding where to move), but not sensory evaluation aspects (deciding what you are looking at). We employed reversible inactivation to compare PPC's contributions to motor and sensory aspects of decisions. Inactivation affected both aspects of behavior, but preferentially impaired decisions when visual stimuli, rather than motor response targets, were in the inactivated visual field. This demonstrates a causal role for PPC in decision-making, with preferential involvement in evaluating attended task-relevant sensory stimuli compared with motor planning.},
address = {New York, NY},
author = {Zhou, Yang and Freedman, David J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/180.full.pdf:pdf},
journal = {Science},
number = {6449},
pages = {180--185},
pmid = {31296771},
title = {{Posterior parietal cortex plays a causal role in perceptual and categorical decisions.}},
volume = {365},
year = {2019}
}
@article{Barat2018,
author = {Barat, Elodie and Wirth, Sylvia and Duhamel, Jean-Ren{\'{e}}},
file = {:Users/hahosoya/Documents/Mendeley Desktop/D05DC7C0-1DD9-46A1-911D-B92E9D42D278.pdf:pdf},
journal = {Proc. Natl. Acad. Sci. U. S. A.},
month = {nov},
number = {47},
pages = {E11158--E11167},
title = {{Face cells in orbitofrontal cortex represent social categories}},
volume = {115},
year = {2018}
}
@article{Haynes2005,
author = {Haynes, John-Dylan and Deichmann, Ralf and Rees, Geraint},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature 2005 Haynes.pdf:pdf},
journal = {Nature},
month = {oct},
number = {7067},
pages = {496--499},
title = {{Eye-specific effects of binocular rivalry in the human lateral geniculate nucleus}},
volume = {438},
year = {2005}
}
@article{Smith2010,
abstract = {Visual cortex shows smooth retinotopic organization on the macroscopic scale, but it is unknown how receptive fields are organized at the level of neighboring neurons. This information is crucial for discriminating among models of visual cortex. We used in vivo two-photon calcium imaging to independently map ON and OFF receptive field subregions of local populations of layer 2/3 neurons in mouse visual cortex. Receptive field subregions were often precisely shared among neighboring neurons. Furthermore, large subregions seem to be assembled from multiple smaller, non-overlapping subregions of other neurons in the same local population. These experiments provide, to our knowledge, the first characterization of the diversity of receptive fields in a dense local network of visual cortex and reveal elementary units of receptive field organization. Our results suggest that a limited pool of afferent receptive fields is available to a local population of neurons and reveal new organizational principles for the neural circuitry of the mouse visual cortex.},
address = {Wolfson Institute for Biomedical Research and Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK. spencer.smith@wibr.ucl.ac.uk},
author = {Smith, Spencer L and H{\"{a}}usser, Michael},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature neuroscience 2010 Smith-1.pdf:pdf},
journal = {Nat. Neurosci.},
month = {sep},
number = {9},
pages = {1144--1149},
title = {{Parallel processing of visual space by neighboring neurons in mouse visual cortex.}},
volume = {13},
year = {2010}
}
@article{Hosoya2016,
abstract = {In visual cortex, individual cells often show invariance properties, where the cells maintain their responses when the stimulus is varied in certain stimulus parameters. This property is considered to be the neural basis for the remarkable robustness of our visual system ...
},
author = {Hosoya, H and Hyv{\"{a}}rinen, A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neural Computation 2016 Hosoya.pdf:pdf},
journal = {Neural Comput.},
month = {jan},
pages = {1249--1263},
title = {{Learning Visual Spatial Pooling by Strong PCA Dimension Reduction}},
volume = {28},
year = {2016}
}
@article{Farroni2005,
abstract = {There is currently no agreement as to how specific or general are the mechanisms underlying newborns' face preferences. We address this issue by manipulating the contrast polarity of schematic and naturalistic face-related images and assessing the preferences of newborns. We find that for both schematic and naturalistic face images, the contrast polarity is important. Newborns did not show a preference for an upright face-related image unless it was composed of darker areas around the eyes and mouth. This result is consistent with either sensitivity to the shadowed areas of a face with overhead (natural) illumination and/or to the detection of eye contact.},
address = {Centre for Brain and Cognitive Development, School of Psychology, Birkbeck, University of London, Malet Street, London WC1E 7HX, United Kingdom.},
author = {Farroni, Teresa and Johnson, Mark H and Menon, Enrica and Zulian, Luisa and Faraguna, Dino and Csibra, Gergely},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proc. Natl. Acad. Sci. U.S.A. 2005 Farroni.pdf:pdf},
journal = {Proc. Natl. Acad. Sci. U. S. A.},
month = {nov},
number = {47},
pages = {17245--17250},
title = {{Newborns' preference for face-relevant stimuli: effects of contrast polarity.}},
volume = {102},
year = {2005}
}
@article{Vinyals2015,
abstract = {We introduce a new neural architecture to learn the conditional probability of an output sequence with elements that are discrete tokens corresponding to positions in an input sequence. Such problems cannot be trivially addressed by existent approaches such as sequence-to-sequence [1] and Neural Turing Machines [2], because the number of target classes in each step of the output depends on the length of the input, which is variable. Problems such as sorting variable sized sequences, and various combinatorial optimization problems belong to this class. Our model solves the problem of variable size output dictionaries using a recently proposed mechanism of neural attention. It differs from the previous attention attempts in that, instead of using attention to blend hidden units of an encoder to a context vector at each decoder step, it uses attention as a pointer to select a member of the input sequence as the output. We call this architecture a Pointer Net (Ptr-Net). We show Ptr-Nets can be used to learn approximate solutions to three challenging geometric problems - finding planar convex hulls, computing Delaunay triangulations, and the planar Travelling Salesman Problem - using training examples alone. Ptr-Nets not only improve over sequence-to-sequence with input attention, but also allow us to generalize to variable size output dictionaries. We show that the learnt models generalize beyond the maximum lengths they were trained on. We hope our results on these tasks will encourage a broader exploration of neural learning for discrete problems.},
archivePrefix = {arXiv},
arxivId = {1506.03134},
author = {Vinyals, Oriol and Fortunato, Meire and Jaitly, Navdeep},
eprint = {1506.03134},
file = {:Users/hahosoya/Documents/Mendeley Desktop/eb6f01faad603c61f2dbf687b26f00d19c867c00.pdf:pdf},
journal = {Adv. Neural Inf. Process. Syst.},
pages = {2692--2700},
title = {{Pointer networks}},
volume = {2015-Janua},
year = {2015}
}
@article{Fisher2015,
abstract = {Current Biology, 25 (2015) 261-266. doi:10.1016/j.cub.2014.11.038},
author = {Fisher, Clark and Freiwald, Winrich A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/47C5E96C-CDDF-4441-87E3-C3E377A25482.pdf:pdf},
journal = {Curr. Biol.},
month = {jan},
number = {2},
pages = {261--266},
title = {{Contrasting Specializations for Facial Motion within the Macaque Face-Processing System}},
volume = {25},
year = {2015}
}
@article{Sterzer2019,
abstract = {Dysfunctional decision-making has been implicated in the positive and negative symptoms of schizophrenia. Decision-making can be conceptualized within the framework of hierarchical predictive coding as the result of a Bayesian inference process that uses prior beliefs to infer states of the world. According to this idea, prior beliefs encoded at higher levels in the brain are fed back as predictive signals to lower levels. Whenever these predictions are violated by the incoming sensory data, a prediction error is generated and fed forward to update beliefs encoded at higher levels. Well-documented impairments in cognitive decision-making support the view that these neural inference mechanisms are altered in schizophrenia. There is also extensive evidence relating the symptoms of schizophrenia to aberrant signaling of prediction errors, especially in the domain of reward and value-based decision-making. Moreover, the idea of altered predictive coding is supported by evidence for impaired low-level sensory mechanisms and motor processes. We review behavioral and neural findings from these research areas and provide an integrated view suggesting that schizophrenia may be related to a pervasive alteration in predictive coding at multiple hierarchical levels, including cognitive and value-based decision-making processes as well as sensory and motor systems. We relate these findings to decision-making processes and propose that varying degrees of impairment in the implicated brain areas contribute to the variety of psychotic experiences.},
author = {Sterzer, Philipp and Voss, Martin and Schlagenhauf, Florian and Heinz, Andreas},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Sterzer et al. - 2019 - Decision-making in schizophrenia A predictive-cod.pdf:pdf},
journal = {Neuroimage},
number = {August 2017},
pages = {133--143},
title = {{Decision-making in schizophrenia: A predictive-coding perspective}},
volume = {190},
year = {2019}
}
@article{Issa2012,
abstract = {Functional magnetic resonance imaging (fMRI) has revealed multiple subregions in monkey inferior temporal cortex (IT) that are selective for images of faces over other objects. The earliest of these subregions, the posterior lateral face patch (PL), has not been studied previously at the neurophysiological level. Perhaps not surprisingly, we found that PL contains a high concentration of "face-selective" cells when tested with standard image sets comparable to those used previously to define the region at the level of fMRI. However, we here report that several different image sets and analytical approaches converge to show that nearly all face-selective PL cells are driven by the presence of a single eye in the context of a face outline. Most strikingly, images containing only an eye, even when incorrectly positioned in an outline, drove neurons nearly as well as full-face images, and face images lacking only this feature led to longer latency responses. Thus, bottom-up face processing is relatively local and linearly integrates features-consistent with parts-based models-grounding investigation of how the presence of a face is first inferred in the IT face processing hierarchy.},
address = {McGovern Institute for Brain Research and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA. issa@mit.edu},
author = {Issa, Elias B and DiCarlo, James J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2012 Issa.pdf:pdf},
journal = {J. Neurosci.},
month = {nov},
number = {47},
pages = {16666--16682},
title = {{Precedence of the eye region in neural processing of faces.}},
volume = {32},
year = {2012}
}
@article{Petersen2007,
abstract = {... layer 2/3 have provided evidence for a direction-preference map within the supragranular layers 
of a barrel  column (Figure 3A ... derive maps from electrode penetrations, and clearly it would be
of great interest to image the functional organization of the barrel  cortex with cellular ...
},
author = {Petersen, C C H},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neuron 2007 Petersen.pdf:pdf},
journal = {Neuron},
month = {jan},
title = {{The functional organization of the barrel cortex}},
year = {2007}
}
@article{Sasaki2013,
author = {Sasaki, Hiroaki and Gutmann, Michael U and Shouno, Hayaru and Hyv{\"{a}}rinen, Aapo},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Mach Learn 2013 Sasaki.pdf:pdf},
journal = {Mach. Learn.},
month = {jan},
pages = {1--33},
title = {{Correlated topographic analysis: estimating an ordering of correlated components}},
year = {2013}
}
@inproceedings{Sprague2009,
abstract = {This paper addresses the problem of learning con- trol policies in very high dimensional state spaces. We propose a linear dimensionality reduction algo- rithm that discovers predictive projections : projec- tions in which accurate predictions of future states can be made using ...},
author = {Sprague, N},
booktitle = {Proc. 21st Int. Jt. Conf. {\ldots}},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2009 Sprague.pdf:pdf},
month = {jan},
title = {{Predictive projections}},
year = {2009}
}
@article{Zhang2019,
abstract = {Modern generative models are usually designed to match target distributions directly in the data space, where the intrinsic dimensionality of data can be much lower than the ambient dimensionality. We argue that this discrepancy may contribute to the difficulties in training generative models. We therefore propose to map both the generated and target distributions to the latent space using the encoder of a standard autoencoder, and train the generator (or decoder) to match the target distribution in the latent space. The resulting method, perceptual generative autoencoder (PGA), is then incorporated with maximum likelihood or variational autoencoder (VAE) objective to train the generative model. With maximum likelihood, PGA generalizes the idea of reversible generative models to unrestricted neural network architectures and arbitrary latent dimensionalities. When combined with VAE, PGA can generate sharper samples than vanilla VAE.},
archivePrefix = {arXiv},
arxivId = {1906.10335},
author = {Zhang, Zijun and Zhang, Ruixiang and Li, Zongpeng and Bengio, Yoshua and Paull, Liam},
eprint = {1906.10335},
file = {:Users/hahosoya/Documents/Mendeley Desktop/feb6ee83247d6f7ff889fd0f173e7dd62381516c.pdf:pdf},
journal = {Deep Gener. Model. Highly Struct. Data, DGS@ICLR 2019 Work.},
title = {{Perceptual generative autoencoders}},
year = {2019}
}
@article{Nair2009,
abstract = {... can be performed by learning a top layer of features that models the joint density of the class labels and the highest layer of unsupervised features [6]. These unsupervised features (plus the class labels) then become the penultimate layer of the deep  belief  net [6]. Early work on ... 
},
author = {Nair, V and Hinton, G},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Advances in neural information {\ldots} 2009 Nair.pdf:pdf},
journal = {Adv. Neural Inf. {\ldots}},
month = {jan},
title = {3-d object recognition with deep belief nets},
year = {2009}
}
@article{McAdams1999,
abstract = {Abstract We examined how attention affected the orientation tuning of 262 isolated neurons in extrastriate area V4 and 135 neurons in area V1 of two rhesus monkeys. The animals were trained to perform a delayed match-to-sample task in which oriented stimuli were  ...},
author = {McAdams, Carrie J and Maunsell, John H R},
file = {:Users/hahosoya/Documents/Mendeley Desktop/The Journal of neuroscience 1999 McAdams.pdf:pdf},
journal = {J. Neurosci.},
month = {jan},
title = {{Effects of attention on orientation-tuning functions of single neurons in macaque cortical area V4}},
year = {1999}
}
@inproceedings{Gatys2016,
abstract = {Abstract Rendering the semantic content of an image in different styles is a difficult image processing task. Arguably, a major limiting factor for previous approaches has been the lack of image representations that explicitly represent semantic information and, thus, allow to separate image content from style. Here we use image representations derived from Convolutional Neural Networks optimised for object recognition, which make high level ...
},
author = {Gatys, L A and Ecker, A S and Bethge, M},
booktitle = {Proc. IEEE},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proceedings of the IEEE 2016 Gatys.pdf:pdf},
month = {jan},
title = {{Image style transfer using convolutional neural networks}},
year = {2016}
}
@article{LaChance2019,
abstract = {A topographic representation of local space is critical for navigation and spatial memory. In humans, topographic spatial learning relies upon the parahippocampal cortex, damage to which renders patients unable to navigate their surroundings or develop new spatial representations. Stable spatial signals have not yet been observed in its rat homolog, the postrhinal cortex. We recorded from single neurons in the rat postrhinal cortex whose firing reflects an animal's egocentric relationship to the geometric center of the local environment, as well as the animal's head direction in an allocentric reference frame. Combining these firing correlates revealed a population code for a stable topographic map of local space. This may form the basis for higher-order spatial maps such as those seen in the hippocampus and entorhinal cortex.},
author = {LaChance, Patrick A. and Todd, Travis P. and Taube, Jeffrey S.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/eaax4192.full.pdf:pdf},
journal = {Science},
number = {6449},
title = {{A sense of space in postrhinal cortex}},
volume = {365},
year = {2019}
}
@article{Ejaz2015,
author = {Ejaz, Naveed and Hamada, Masashi and Diedrichsen, J{\"{o}}rn},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature Publishing Group 2015 Ejaz.pdf:pdf},
journal = {Nat. Neurosci.},
month = {jun},
number = {7},
pages = {1034--1040},
title = {{Hand use predicts the structure of representations in sensorimotor cortex}},
volume = {18},
year = {2015}
}
@article{Gauthier2012,
author = {Gauthier, B and Eger, E and Hesselmann, G and Giraud, A L and Kleinschmidt, A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2012 Gauthier.pdf:pdf},
journal = {J. Neurosci.},
month = {oct},
number = {41},
pages = {14433--14441},
title = {{Temporal Tuning Properties along the Human Ventral Visual Stream}},
volume = {32},
year = {2012}
}
@article{Eimer2014,
abstract = {Trends in Cognitive Sciences, 18 (2014) 526-535. doi:10.1016/j.tics.2014.05.005},
author = {Eimer, Martin},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Trends in cognitive Sciences 2014 Eimer.pdf:pdf},
journal = {Trends Cogn. Sci.},
month = {oct},
number = {10},
pages = {526--535},
title = {{The neural basis of attentional control in visual search}},
volume = {18},
year = {2014}
}
@article{Carandini1997,
abstract = {Simple cells in the primary visual cortex often appear to compute a weighted sum of the light intensity distribution of the visual stimuli that fall on their receptive fields. A linear model of these cells has the advantage of simplicity and captures a number of basic aspects of cell function. It, however, fails to account for important response nonlinearities, such as the decrease in response gain and latency observed at high contrasts and the effects of masking by stimuli that fail to elicit responses when presented alone. To account for these nonlinearities we have proposed a normalization model, which extends the linear model to include mutual shunting inhibition among a large number of cortical cells. Shunting inhibition is divisive, and its effect in the model is to normalize the linear responses by a measure of stimulus energy. To test this model we performed extracellular recordings of simple cells in the primary visual cortex of anesthetized macaques. We presented large stimulus sets consisting of (1) drifting gratings of various orientations and spatiotemporal frequencies; (2) plaids composed of two drifting gratings; and (3) gratings masked by full-screen spatiotemporal white noise. We derived expressions for the model predictions and fitted them to the physiological data. Our results support the normalization model, which accounts for both the linear and the nonlinear properties of the cells. An alternative model, in which the linear responses are subject to a compressive nonlinearity, did not perform nearly as well.},
address = {Howard Hughes Medical Institute and Center for Neural Science, New York University, New York, New York 10003, USA.},
author = {Carandini, M and Heeger, D J and Movshon, J A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/The Journal of neuroscience 1997 Carandini.pdf:pdf},
journal = {J. Neurosci.},
month = {nov},
number = {21},
pages = {8621--8644},
title = {{Linearity and normalization in simple cells of the macaque primary visual cortex.}},
volume = {17},
year = {1997}
}
@article{Einhauser2005,
abstract = {Invariant object recognition is arguably one of the major challenges for contemporary machine vision systems. In contrast, the mammalian visual system performs this task virtually effortlessly. How can we exploit our knowledge on the biological system to improve artificial systems? Our understanding of the mammalian early visual system has been augmented by the discovery that general coding principles could explain many aspects of neuronal response properties. How can such schemes be transferred to system level performance? In the present study we train cells on a particular variant of the general principle of temporal coherence, the "stability" objective. These cells are trained on unlabeled real-world images without a teaching signal. We show that after training, the cells form a representation that is largely independent of the viewpoint from which the stimulus is looked at. This finding includes generalization to previously unseen viewpoints. The achieved representation is better suited for view-point invariant object classification than the cells' input patterns. This property to facilitate view-point invariant classification is maintained even if training and classification take place in the presence of an--also unlabeled--distractor object. In summary, here we show that unsupervised learning using a general coding principle facilitates the classification of real-world objects, that are not segmented from the background and undergo complex, non-isomorphic, transformations.},
address = {Institute of Neuroinformatics, University & ETH Z{\"{u}}rich, Z{\"{u}}rich, Switzerland. wet@klab.caltech.edu},
author = {Einh{\"{a}}user, Wolfgang and Hipp, J{\"{o}}rg and Eggert, Julian and K{\"{o}}rner, Edgar and K{\"{o}}nig, Peter},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Biological Cybernetics 2005 Einh{\"{a}}user.pdf:pdf},
journal = {Biol. Cybern.},
month = {jul},
number = {1},
pages = {79--90},
title = {{Learning viewpoint invariant object representations using a temporal coherence principle.}},
volume = {93},
year = {2005}
}
@article{Blanz1999,
abstract = {Abstract In this paper, a new technique for modeling textured 3D faces is introduced. 3D faces can either be generated automatically from one or more photographs, or modeled directly through an intuitive user interface. Users are assisted in two key problems of  ...},
author = {Blanz, Volker and et Al},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proceedings of the 26th annual conference on {\ldots} 1999 Blanz.pdf:pdf},
journal = {Proc. 26th Annu. Conf.  {\ldots}},
month = {jan},
title = {{A morphable model for the synthesis of 3D faces}},
year = {1999}
}
@article{Kululanga1999,
abstract = {Learning communication via deep reinforcement learning has recently been shown to be an effective way to solve cooperative multi-agent tasks. However, learning which communicated information is beneficial for each agent's decision-making remains a challenging task. In order to address this problem, we introduce a fully differentiable framework for communication and reasoning, enabling agents to solve cooperative tasks in partially-observable environments. The framework is designed to facilitate explicit reasoning between agents, through a novel memory-based attention network that can learn selectively from its past memories. The model communicates through a series of reasoning steps that decompose each agent's intentions into learned representations that are used first to compute the relevance of communicated information, and second to extract information from memories given newly received information. By selectively interacting with new information, the model effectively learns a communication protocol directly, in an end-to-end manner. We empirically demonstrate the strength of our model in cooperative multi-agent tasks, where inter-agent communication and reasoning over prior information substantially improves performance compared to baselines.},
author = {Kululanga, G. K. GK},
file = {:Users/hahosoya/Documents/Mendeley Desktop/eb7c72de110f0577da54170bf0cf2455a04dc9b7.pdf:pdf},
number = {August},
pages = {215--223},
title = {{Learning Multi-Agent Communication Through Structured Attentive Reasoning}},
volume = {125},
year = {1999}
}
@article{Maass2009,
abstract = {From a theoretical point of view, statistical inference is an attractive model of brain operation. However, it is unclear how to implement these inferential processes in neuronal networks. We offer a solution to this problem by showing in detailed simulations how the belief ...},
author = {Maass, W and Douglas, R},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neural Computation 2009 Maass.pdf:pdf},
journal = {Neural Comput.},
month = {jan},
title = {{Belief propagation in networks of spiking neurons}},
year = {2009}
}
@article{Yu2021,
abstract = {Although current deep generative adversarial networks (GANs) could synthesize high-quality (HQ) images, discovering novel GAN encoders for image reconstruction is still favorable. When embedding images to latent space, existing GAN encoders work well for aligned images (such as the human face), but they do not adapt to more generalized GANs. To our knowledge, current state-of-the-art GAN encoders do not have a proper encoder to reconstruct high-fidelity images from most misaligned HQ synthesized images on different GANs. Their performances are limited, especially on non-aligned and real images. We propose a novel method (named MTV-TSA) to handle such problems. Creating multi-type latent vectors (MTV) from latent space and two-scale attentions (TSA) from images allows designing a set of encoders that can be adaptable to a variety of pre-trained GANs. We generalize two sets of loss functions to optimize the encoders. The designed encoders could make GANs reconstruct higher fidelity images from most synthesized HQ images. In addition, the proposed method can reconstruct real images well and process them based on learned attribute directions. The designed encoders have unified convolutional blocks and could match well in current GAN architectures (such as PGGAN, StyleGANs, and BigGAN) by fine-tuning the corresponding normalization layers and the last block. Such well-designed encoders can also be trained to converge more quickly.},
archivePrefix = {arXiv},
arxivId = {2108.10201},
author = {Yu, Cheng and Wang, Wenmin},
eprint = {2108.10201},
file = {:Users/hahosoya/Documents/Mendeley Desktop/ae22e3a167df01dfad3b3407ce567cc2fc13e011.pdf:pdf},
pages = {1--14},
title = {{Adaptable GAN Encoders for Image Reconstruction via Multi-type Latent Vectors with Two-scale Attentions}},
year = {2021}
}
@article{Serre2007a,
abstract = {We introduce a new general framework for the recognition of complex visual scenes, which is motivated by biology: We describe a hierarchical system that closely follows the organization of visual cortex and builds an increasingly complex and invariant feature representation by alternating between a template matching and a maximum pooling operation. We demonstrate the strength of the approach on a range of recognition tasks: From invariant single object recognition in clutter to multiclass categorization problems and complex scene understanding tasks that rely on the recognition of both shape-based as well as texture-based objects. Given the biological constraints that the system had to satisfy, the approach performs surprisingly well: It has the capability of learning from only a few training examples and competes with state-of-the-art systems. We also discuss the existence of a universal, redundant dictionary of features that could handle the recognition of most object categories. In addition to its relevance for computer vision, the success of this approach suggests a plausibility proof for a class of feedforward models of object recognition in cortex.},
author = {Serre, Thomas and Wolf, Lior and Bileschi, Stanley and Riesenhuber, Maximilian and Poggio, Tomaso},
file = {:Users/hahosoya/Documents/Mendeley Desktop/IEEE Transactions on pattern Analysis and Machine Intelligence 2007 Serre.pdf:pdf},
journal = {IEEE Trans. Pattern Anal. Mach. Intell.},
month = {mar},
number = {3},
pages = {411--426},
title = {{Robust Object Recognition with Cortex-Like Mechanisms}},
volume = {29},
year = {2007}
}
@article{Grill-Spector2004,
abstract = {▪ Abstract The discovery and analysis of cortical visual areas is a major accomplishment of visual neuroscience. In the past decade the use of noninvasive functional imaging, particularly functional magnetic resonance imaging (fMRI), has dramatically increased our  ...},
author = {Grill-Spector, K and Malach, R},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Annu. Rev. Neurosci. 2004 Grill-Spector.pdf:pdf},
journal = {Annu. Rev. Neurosci.},
month = {jan},
title = {{The human visual cortex}},
year = {2004}
}
@article{Gong1996,
abstract = {Abstract Visual perception of faces is invariant under many transformations, perhaps the most problematic of which is pose change (face rotating in depth). We use a variation of Gabor wavelet transform (GWT) as a representation framework for investigating face pose ...
},
author = {Gong, S and McKenna, S and Collins, J J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/1996 Gong.pdf:pdf},
journal = {Autom. Face Gesture {\ldots}},
month = {jan},
title = {{An investigation into face pose distributions}},
year = {1996}
}
@article{Gross1972,
author = {Gross, C G and Rocha-Miranda, C E and Bender, D B},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neurophysiology 1972 Gross.pdf:pdf},
journal = {J. Neurophysiol.},
month = {jan},
number = {1},
pages = {96--111},
title = {{Visual properties of neurons in inferotemporal cortex of the Macaque.}},
volume = {35},
year = {1972}
}
@article{Sims2018,
abstract = {Perceptual generalization and discrimination are fundamental cognitive abilities. For example, if a bird eats a poisonous butterfly, it will learn to avoid preying on that species again by generalizing its past experience to new perceptual stimuli. In cognitive science, the "universal law of generalization" seeks to explain this ability and states that generalization between stimuli will follow an exponential function of their distance in "psychological space." Here, I challenge existing theoretical explanations for the universal law and offer an alternative account based on the principle of efficient coding. I show that the universal law emerges inevitably from any information processing system (whether biological or artificial) that minimizes the cost of perceptual error subject to constraints on the ability to process or transmit information.},
address = {Department of Cognitive Science, Rensselaer Polytechnic Institute, Troy, NY 12180, USA. simsc3@rpi.edu.},
author = {Sims, Chris R},
file = {:Users/hahosoya/Documents/Mendeley Desktop/06364663-A4B1-40E6-9B55-802C03EE9FFD.pdf:pdf},
journal = {Science},
month = {may},
number = {6389},
pages = {652--656},
title = {{Efficient coding explains the universal law of generalization in human perception.}},
volume = {360},
year = {2018}
}
@inproceedings{Bouchacourt2018,
abstract = {Abstract: We would like to learn a representation of the data which decomposes an observation into factors of variation which we can independently control. Specifically, we want to use minimal supervision to learn a latent representation that reflects the semantics behind a specific grouping of the data, where within a group the samples share a common factor of variation. For example, consider a collection of face images grouped by identity. We wish to anchor the semantics of the grouping into a relevant and disentangled {\ldots}
},
author = {Bouchacourt, Diane and Tomioka, Ryota and Nowozin, Sebastian},
booktitle = {AAAI Conf. Artif. Intell.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/8C2B3CFE-C574-460B-BEDB-B798AF7C091D.pdf:pdf},
month = {jan},
title = {{Multi-Level Variational Autoencoder: Learning Disentangled Representations from Grouped Observations}},
year = {2018}
}
@article{Saygin2011,
abstract = {Nature Neuroscience 15, 321 (2011). doi:10.1038/nn.3001},
author = {Saygin, Zeynep M and Osher, David E and Koldewyn, Kami and Reynolds, Gretchen and Gabrieli, John D E and Saxe, Rebecca R},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature Publishing Group 2011 Saygin.pdf:pdf},
journal = {Nat. Neurosci.},
month = {dec},
number = {2},
pages = {321--327},
title = {{Anatomical connectivity patterns predict face selectivity in the fusiform gyrus}},
volume = {15},
year = {2011}
}
@article{Munkres1957,
author = {Munkres, James},
file = {:Users/hahosoya/Documents/Mendeley Desktop/c7a4cfc19634cfea59fb0db825ff27572f0f67f6.pdf:pdf},
journal = {J. Soc. Ind. Appl. Math.},
month = {mar},
number = {1},
pages = {32--38},
title = {{Algorithms for the Assignment and Transportation Problems}},
volume = {5},
year = {1957}
}
@article{Roe2012,
abstract = {Visual area V4 is a midtier cortical area in the ventral visual pathway. It is crucial for visual object recognition and has been a focus of many studies on visual attention. However, there is no unifying view of V4's role in visual processing. Neither is there an understanding of how its role in feature processing interfaces with its role in visual attention. This review captures our current knowledge of V4, largely derived from electrophysiological and imaging studies in the macaque monkey. Based on recent discovery of functionally specific domains in V4, we propose that the unifying function of V4 circuitry is to enable selective extraction of specific functional domain-based networks, whether it be by bottom-up specification of object features or by top-down attentionally driven selection.},
address = {Department of Psychology, Vanderbilt University, 301 Wilson Hall, Nashville, TN 37240, USA. anna.roe@vanderbilt.edu},
author = {Roe, Anna W and Chelazzi, Leonardo and Connor, Charles E and Conway, Bevil R and Fujita, Ichiro and Gallant, Jack L and Lu, Haidong and Vanduffel, Wim},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neuron 2012 Roe.pdf:pdf},
journal = {Neuron},
month = {apr},
number = {1},
pages = {12--29},
title = {{Toward a unified theory of visual area V4.}},
volume = {74},
year = {2012}
}
@article{Donato2021,
abstract = {The formation of memories that contain information about the specific time and place of acquisition, which are commonly referred to as "autobiographical" or "episodic" memories, critically relies on the hippocampus and on a series of interconnected structures located in the medial temporal lobe of the mammalian brain. The observation that adults retain very few of these memories from the first years of their life has fueled a long-standing debate on whether infants can make the types of memories that in adults are processed by the hippocampus-dependent memory system, and whether the hippocampus is involved in learning and memory processes early in life. Recent evidence shows that, even at a time when its circuitry is not yet mature, the infant hippocampus is able to produce long-lasting memories. However, the ability to acquire and store such memories relies on molecular pathways and network-based activity dynamics different from the adult system, which mature with age. The mechanisms underlying the formation of hippocampus-dependent memories during infancy, and the role that experience exerts in promoting the maturation of the hippocampus-dependent memory system, remain to be understood. In this review, we discuss recent advances in our understanding of the ontogeny and the biological correlates of hippocampus-dependent memories.},
author = {Donato, Flavio and Alberini, Cristina M. and Amso, Dima and Dragoi, George and Dranovsky, Alex and Newcombe, Nora S.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/920.full.pdf:pdf},
journal = {J. Neurosci.},
keywords = {critical periods,development,early-life stress,hippocampus,learning and memory,place cell sequences},
number = {5},
pages = {920--926},
pmid = {33328296},
title = {{The Ontogeny of Hippocampus-Dependent Memories}},
volume = {41},
year = {2021}
}
@article{LeCun2004,
author = {LeCun, Yann and Huang, Fu Jie and Bottou, L{\'{e}}on},
file = {:Users/hahosoya/Documents/Mendeley Desktop/9C73762D-6B65-43BF-A427-9B621CA10E4A.pdf:pdf},
journal = {Comput. Vis. Pattern Recognit.},
month = {jan},
title = {{Learning Methods for Generic Object Recognition with Invariance to Pose and Lighting.}},
year = {2004}
}
@article{Felleman1991,
abstract = {In recent years, many new cortical areas have been identified in the macaque monkey. The number of identified connections between areas has increased even more dramatically. We report here on (1) a summary of the layout of cortical areas associated with vision and with other modalities, (2) a computerized database for storing and representing large amounts of information on connectivity patterns, and (3) the application of these data to the analysis of hierarchical organization of the cerebral cortex. Our analysis concentrates on the visual system, which includes 25 neocortical areas that are predominantly or exclusively visual in function, plus an additional 7 areas that we regard as visual-association areas on the basis of their extensive visual inputs. A total of 305 connections among these 32 visual and visual-association areas have been reported. This represents 31% of the possible number of pathways if each area were connected with all others. The actual degree of connectivity is likely to be closer to 40%. The great majority of pathways involve reciprocal connections between areas. There are also extensive connections with cortical areas outside the visual system proper, including the somatosensory cortex, as well as neocortical, transitional, and archicortical regions in the temporal and frontal lobes. In the somatosensory/motor system, there are 62 identified pathways linking 13 cortical areas, suggesting an overall connectivity of about 40%. Based on the laminar patterns of connections between areas, we propose a hierarchy of visual areas and of somatosensory/motor areas that is more comprehensive than those suggested in other recent studies. The current version of the visual hierarchy includes 10 levels of cortical processing. Altogether, it contains 14 levels if one includes the retina and lateral geniculate nucleus at the bottom as well as the entorhinal cortex and hippocampus at the top. Within this hierarchy, there are multiple, intertwined processing streams, which, at a low level, are related to the compartmental organization of areas V1 and V2 and, at a high level, are related to the distinction between processing centers in the temporal and parietal lobes. However, there are some pathways and relationships (about 10% of the total) whose descriptions do not fit cleanly into this hierarchical scheme for one reason or another. In most instances, though, it is unclear whether these represent genuine exceptions to a strict hierarchy rather than inaccuracies or uncertainities in the reported assignment.},
address = {Department of Neurobiology and Anatomy, University of Texas Medical School, Houston.},
author = {Felleman, D J and {Van Essen}, D C},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Cereb. Cortex 1991 Felleman.pdf:pdf},
journal = {Cereb. Cortex},
month = {jan},
number = {1},
pages = {1--47},
title = {{Distributed hierarchical processing in the primate cerebral cortex.}},
volume = {1},
year = {1991}
}
@article{DeAngelis1994,
abstract = {1. The classically defined receptive field of a visual neuron is the area of visual space over which the cell responds to visual stimuli. It is well established, however, that the discharge produced by an optimal stimulus can be modulated by the presence of additional stimuli that by themselves do not produce any response. This study examines inhibitory influences that originate from areas located outside of the classical (i.e., excitatory) receptive field. Previous work has shown that for some cells the response to a properly oriented bar of light becomes attenuated when the bar extends beyond the receptive field, a phenomenon known as end-inhibition (or length tuning). Analogously, it has been shown that increasing the number of cycles of a drifting grating stimulus may also inhibit the firing of some cells, an effect known as side-inhibition (or width tuning). Very little information is available, however, about the relationship between end- and side-inhibition. We have examined the spatial organization and tuning characteristics of these inhibitory effects by recording extracellularly from single neurons in the cat's striate cortex (Area 17). 2. For each cortical neuron, length and width tuning curves were obtained with the use of rectangular patches of drifting sinusoidal gratings that have variable length and width. Results from 82 cells show that the strengths of end- and side-inhibition tend to be correlated. Most cells that exhibit clear end-inhibition also show a similar degree of side-inhibition. For these cells, the excitatory receptive field is surrounded on all sides by inhibitory zones. Some cells exhibit only end- or side-inhibition, but not both. Data for 28 binocular cells show that length and width tuning curves for the dominant and nondominant eyes tend to be closely matched. 3. We also measured tuning characteristics of end- and side-inhibition. To obtain these data, the excitatory receptive field was stimulated with a grating patch having optimal orientation, spatial frequency, and size, whereas the end- or side-inhibitory regions were stimulated with patches of gratings that had a variable parameter (such as orientation). Results show that end- and side-inhibition tend to be strongest at the orientation and spatial frequency that yield maximal excitation. However, orientation and spatial frequency tuning curves for inhibition are considerably broader than those for excitation, suggesting that inhibition is mediated by a pool of neurons.(ABSTRACT TRUNCATED AT 400 WORDS)},
address = {Group in Bioengineering, University of California, Berkeley 94720.},
author = {DeAngelis, G C and Freeman, R D and Ohzawa, I},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neurophysiology 1994 DeAngelis.pdf:pdf},
journal = {J. Neurophysiol.},
month = {jan},
number = {1},
pages = {347--374},
title = {{Length and width tuning of neurons in the cat's primary visual cortex.}},
volume = {71},
year = {1994}
}
@article{Dayan,
author = {Dayan, Peter and Abott, L F},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Dayan.pdf:pdf},
title = {{Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems}},
}
@article{Rolls1986,
abstract = {There is a population of neurons in the cortex in the middle and anterior part of the superior temporal sulcus (STS) of the monkey with responses which are selective for faces. To investigate whether the responses of these neurons show some of the perceptual properties of face recognition such as tolerance to changes in the size and contrast of the face, the effects of alteration of the size and contrast of an effective face stimulus on the responses of these neurons were analysed quantitatively in macaque monkeys. First, it was shown that the majority of these neurons had responses which were relatively invariant with respect to the size of the stimulus. The median size change tolerated with a response of greater than half the maximal response was 12 times. Second, it was found that for a few of these neurons, the size of the face did affect the neuronal response. For most of these neurons, it was found that when the size of the image and its distance were altered, the neuronal response was related to the retinal angle subtended by the image. But for four neurons the absolute size of the image determined the magnitude of the neuronal response, independently of the distance of the image. Thus these four neurons showed size constancy. It is suggested that these neurons would be useful as part of a face recognition system, because only objects in a certain absolute size range should normally be classified as faces. Third, the responses of the neurons were relatively invariant with respect to the contrast of the face. The mean contrast at which the neurons still responded with more than half the maximal response was 0.26. Fourth, the responses of the neurons were relatively invariant with respect to the sign of the contrast of the face, that is the neurons responded to negative as well as to positive images of faces. Fifth, the neurons typically responded to a face when the information in it had been reduced from 3D to a 2D representation in gray on a monitor, with a response which was on average 0.5 that to a real face. These results show that the responses of these neurons have some of the invariant properties with respect to size and contrast alteration shown by face perception, and show that their processing is at a level which would be useful in face recognition.(ABSTRACT TRUNCATED AT 400 WORDS)},
author = {Rolls, E T and Baylis, G C},
file = {:Users/hahosoya/Documents/Mendeley Desktop/79902159-71D5-4889-9057-BDA1D80814B4.pdf:pdf},
journal = {Exp. brain Res.},
month = {jan},
number = {1},
pages = {38--48},
title = {{Size and contrast have only small effects on the responses to faces of neurons in the cortex of the superior temporal sulcus of the monkey.}},
volume = {65},
year = {1986}
}
@article{Lazaridou2018,
abstract = {The ability of algorithms to evolve or learn (compositional) communication protocols has traditionally been studied in the language evolution literature through the use of emergent communication tasks. Here we scale up this research by using contemporary deep learning methods and by training reinforcement-learning neural network agents on referential communication games. We extend previous work, in which agents were trained in symbolic environments, by developing agents which are able to learn from raw pixel data, a more challenging and realistic input representation. We find that the degree of structure found in the input data affects the nature of the emerged protocols, and thereby corroborate the hypothesis that structured compositional language is most likely to emerge when agents perceive the world as being structured.},
archivePrefix = {arXiv},
arxivId = {1804.03984},
author = {Lazaridou, Angeliki and Hermann, Karl Moritz and Tuyls, Karl and Clark, Stephen},
eprint = {1804.03984},
file = {:Users/hahosoya/Documents/Mendeley Desktop/c0617490e0ea57b28b89d4ca92e069f538609603.pdf:pdf},
journal = {arXiv},
pages = {1--13},
title = {{Emergence of linguistic communication from referential games with symbolic and pixel input}},
year = {2018}
}
@article{Axelrod2012,
abstract = {The ability to recognize objects across different viewpoints (view invariance) is a remarkable property of the primate visual system. According to a prominent theory, view information is represented by view-selective mechanisms at early stages of visual processing and gradually becomes view invariant in high-level visual areas. Single-cell recording studies have also reported an intermediate step of partial view invariance for mirror-symmetric face views. Nevertheless, similar evidence for this type of hierarchical processing for face view has not been reported yet in the human visual cortex. The present functional magnetic resonance imaging study used state-of-the-art multivariate pattern analysis to explore face-view tuning in the human visual cortex. Our results revealed that consistent with a view-selective representation, face view can be successfully decoded in face and object-selective regions as well as in early visual cortex. Critically, similar neural representations for mirror-symmetric views were found in high-level but not in low-level visual areas. Our results support the notion of gradual emergence of view-invariant representation with invariance for mirror-symmetric images as an intermediate step and propose putative neural correlates of mirror-image confusion in the human brain.},
address = {School of Psychological Sciences, Department of Psychology, Tel Aviv University, Tel Aviv 69987, Israel. vadim.axelrod@gmail.com},
author = {Axelrod, Vadim and Yovel, Galit},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2012 Axelrod.pdf:pdf},
journal = {J. Neurosci.},
month = {feb},
number = {7},
pages = {2442--2452},
title = {{Hierarchical processing of face viewpoint in human visual cortex.}},
volume = {32},
year = {2012}
}
@article{Larsen2016,
abstract = {We present an autoencoder that leverages learned representations to better measure similarities in data space. By combining a variational autoencoder (VAE) with a generative adversarial network (GAN) we can use learned feature representations in the GAN discriminator as basis for the VAE reconstruction objective. Thereby, we replace element-wise errors with feature-wise errors to better capture the data distribution while offering invariance towards e.g. translation. We apply our method to images of faces and show that it outperforms VAEs with element-wise similarity measures in terms of visual fidelity. Moreover, we show that the method learns an embedding in which high-level abstract visual features (e.g. wearing glasses) can be modified using simple arithmetic.},
archivePrefix = {arXiv},
arxivId = {1512.09300},
author = {Larsen, Anders Boesen Lindbo and S{\o}nderby, S{\o}ren Kaae and Larochelle, Hugo and Winther, Ole},
eprint = {1512.09300},
file = {:Users/hahosoya/Documents/Mendeley Desktop/b249c66411583a873fb6ff4d7a06fc7149ecf7c4.pdf:pdf},
isbn = {9781510829008},
journal = {33rd Int. Conf. Mach. Learn. ICML 2016},
pages = {2341--2349},
title = {{Autoencoding beyond pixels using a learned similarity metric}},
volume = {4},
year = {2016}
}
@article{Sawayama2017,
author = {Sawayama, Masataka and Adelson, Edward H and Nishida, Shin'ya},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Vision 2017 Sawayama.pdf:pdf},
journal = {J. Vis.},
month = {may},
number = {5},
pages = {7},
title = {{Visual wetness perception based on image color statistics}},
volume = {17},
year = {2017}
}
@article{Koumura2019,
author = {Koumura, Takuya and Terashima, Hiroki and Furukawa, Shigeto},
file = {:Users/hahosoya/Documents/Mendeley Desktop/5517.full.pdf:pdf},
journal = {J. Neurosci.},
month = {may},
title = {{Cascaded Tuning to Amplitude Modulation for Natural Sound Recognition}},
volume = {2914-18},
year = {2019}
}
@article{Memisevic2007,
abstract = {Unsupervised Learning of Image Transformations ... We describe a probabilistic model for learning 
rich, dis- tributed representations of image transformations. ... Inference in the model con- sists in
extracting the transformation , given a pair of im- ages, and can be performed exactly ...
},
author = {Memisevic, Roland and Hinton, Geoffrey E},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2007 IEEE Conference on Computer Vision {\ldots} 2007 Memisevic.pdf:pdf},
journal = {2007 IEEE Conf. Comput. Vis.  {\ldots}},
month = {jan},
title = {{Unsupervised learning of image transformations}},
year = {2007}
}
@article{Berkes2011,
abstract = {... J. Fiser ,; P. Berkes ,; G. Orb{\'{a}}n,; M. Lengyel. , Statistically optimal perception and learning: from behavior to neural representations. Trends Cogn. Sci. 14, 119 (2010). doi:10.1016/j.tics.2010.01.003 pmid:20153683. ...},
author = {Berkes, P and Orban, G and Lengyel, M and Fiser, J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Science 2011 Berkes-2.pdf:pdf},
journal = {Science},
month = {jan},
number = {6013},
pages = {83--87},
title = {{Spontaneous Cortical Activity Reveals Hallmarks of an Optimal Internal Model of the Environment}},
volume = {331},
year = {2011}
}
@article{Lee2003,
abstract = {Abstract Recently, there has been a great deal of interest in modeling the non-Gaussian structures of natural images. However, despite the many advances in the direction of sparse coding and multi-resolution analysis, the full probability distribution of pixel values in a ...
},
author = {Lee, Ann B},
file = {:Users/hahosoya/Documents/Mendeley Desktop/International journal on machine learning 2003 Lee.pdf:pdf},
journal = {Int. J. Comput. Vis.},
month = {jan},
number = {1/2},
pages = {83--103},
title = {{The Nonlinear Statistics of High-Contrast Patches in Natural Images - Springer}},
volume = {54},
year = {2003}
}
@article{Heinz2019,
abstract = {Psychotic experiences may be understood as altered information processing due to aberrant neural computations. A prominent example of such neural computations is the computation of prediction errors (PEs), which signal the difference between expected and experienced events. Among other areas showing PE coding, hippocampal-prefrontal-striatal neurocircuits play a prominent role in information processing. Dysregulation of dopaminergic signaling, often secondary to psychosocial stress, is thought to interfere with the processing of biologically important events (such as reward prediction errors) and result in the aberrant attribution of salience to irrelevant sensory stimuli and internal representations. Bayesian hierarchical predictive coding offers a promising framework for the identification of dysfunctional neurocomputational processes and the development of a mechanistic understanding of psychotic experience. According to this framework, mismatches between prior beliefs encoded at higher levels of the cortical hierarchy and lower-level (sensory) information can also be thought of as PEs, with important consequences for belief updating. Low levels of precision in the representation of prior beliefs relative to sensory data, as well as dysfunctional interactions between prior beliefs and sensory data in an ever-changing environment, have been suggested as a general mechanism underlying psychotic experiences. Translating the promise of the Bayesian hierarchical predictive coding into patient benefit will come from integrating this framework with existing knowledge of the etiology and pathophysiology of psychosis, especially regarding hippocampal-prefrontal-striatal network function and neural mechanisms of information processing and belief updating.},
author = {Heinz, Andreas and Murray, Graham K. and Schlagenhauf, Florian and Sterzer, Philipp and Grace, Anthony A. and Waltz, James A.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Heinz et al. - 2019 - Towards a Unifying Cognitive, Neurophysiological, .pdf:pdf},
journal = {Schizophr. Bull.},
keywords = {computational modeling,delusions,dopamine,hallucinations,prediction error,reward,schizophrenia},
number = {5},
pages = {1092--1100},
pmid = {30388260},
title = {{Towards a Unifying Cognitive, Neurophysiological, and Computational Neuroscience Account of Schizophrenia}},
volume = {45},
year = {2019}
}
@inproceedings{Vig2014,
abstract = {Abstract Saliency prediction typically relies on hand-crafted (multiscale) features that are combined in different ways to form a" master" saliency map, which encodes local image conspicuity. Recent improvements to the state of the art on standard benchmarks such as ...
},
author = {Vig, E and Dorr, M and Cox, D},
booktitle = {Proc. IEEE Conf. {\ldots}},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proceedings of the IEEE Conference on {\ldots} 2014 Vig.pdf:pdf},
month = {jan},
title = {{Large-scale optimization of hierarchical features for saliency prediction in natural images}},
year = {2014}
}
@article{Wei2015,
author = {Wei, Xue-Xin and Stocker, Alan A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature Publishing Group 2015 Wei.pdf:pdf},
journal = {Nat. Neurosci.},
month = {sep},
number = {10},
pages = {1509--1517},
title = {{A Bayesian observer model constrained by efficient coding can explain 'anti-Bayesian' percepts}},
volume = {18},
year = {2015}
}
@article{Nguyen2016,
abstract = {Deep neural networks (DNNs) have demonstrated state-of-the-art results on many pattern recognition tasks, especially vision classification problems. Understanding the inner workings of such computational brains is both fascinating basic science that is interesting in its own right---similar to why we study the human brain---and will enable researchers to further improve DNNs. One path to understanding how a neural network functions internally is to study what each of its neurons has learned to detect. One such method is called {\ldots}
},
author = {Nguyen, Anh and Dosovitskiy, Alexey and Yosinski, Jason and Brox, Thomas and Clune, Jeff},
file = {:Users/hahosoya/Documents/Mendeley Desktop/25F89D6C-86BC-499D-865E-65F6F56880AB.pdf:pdf},
journal = {NIPS},
month = {jan},
pages = {3387--3395},
title = {{Synthesizing the preferred inputs for neurons in neural networks via deep generator networks}},
year = {2016}
}
@article{Morton1991,
abstract = {Abstract 1. Evidence from newborns leads to the conclusion that infants are born with some information about the structure of faces. This structural information, termed CONSPEC, guides the preference for facelike patterns found in newborn infants. CONSPEC is ...
},
author = {Morton, J and Johnson, M H},
file = {:Users/hahosoya/Documents/Mendeley Desktop/1991 Morton-1.pdf:pdf},
journal = {Psychol. Rev.},
month = {jan},
number = {2},
pages = {164--181},
title = {{CONSPEC and CONLERN: a two-process theory of infant face recognition.}},
volume = {98},
year = {1991}
}
@article{Zylberberg2011,
abstract = {Abstract Sparse coding algorithms trained on natural images can accurately predict the features that excite visual cortical neurons, but it is not known whether such codes can be learned using biologically realistic plasticity rules. We have developed a biophysically ... 
},
author = {Zylberberg, J and Murphy, J T and DeWeese, M R},
file = {:Users/hahosoya/Documents/Mendeley Desktop/PLoS Comp Biol 2011 Zylberberg.pdf:pdf},
journal = {PLoS Comput. Biol.},
month = {jan},
title = {{PLOS Computational Biology: A Sparse Coding Model with Synaptically Local Plasticity and Spiking Neurons Can Account for the Diverse Shapes of V1 Simple Cell Receptive Fields}},
year = {2011}
}
@article{Hubel1962,
author = {Hubel, David H and Wiesel, Torsten N},
file = {:Users/hahosoya/Documents/Mendeley Desktop/The Journal of Physiology 1962 Hubel.pdf:pdf},
journal = {J. Physiol.},
month = {jan},
number = {1},
pages = {106--154},
title = {{Receptive fields, binocular interaction and functional architecture in the cat's visual cortex}},
volume = {160},
year = {1962}
}
@article{Sigman2001,
abstract = {To understand how the human visual system analyzes images, it is essential to know the structure of the visual environment. In particular, natural images display consistent statistical properties that distinguish them from random luminance distributions. We have studied the geometric regularities of oriented elements (edges or line segments) present in an ensemble of visual scenes, asking how much information the presence of a segment in a particular location of the visual scene carries about the presence of a second segment at different relative positions and orientations. We observed strong long-range correlations in the distribution of oriented segments that extend over the whole visual field. We further show that a very simple geometric rule, cocircularity, predicts the arrangement of segments in natural scenes, and that different geometrical arrangements show relevant differences in their scaling properties. Our results show similarities to geometric features of previous physiological and psychophysical studies. We discuss the implications of these findings for theories of early vision.},
address = {Laboratory of Mathematical Physics, The Rockefeller University, 1230 York Avenue, New York, NY 10021, USA.},
author = {Sigman, M and Cecchi, G A and Gilbert, C D and Magnasco, M O},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proc. Natl. Acad. Sci. U.S.A. 2001 Sigman.pdf:pdf},
journal = {Proc. Natl. Acad. Sci. U. S. A.},
month = {feb},
number = {4},
pages = {1935--1940},
title = {{On a common circle: natural scenes and Gestalt rules.}},
volume = {98},
year = {2001}
}
@article{Huang2018,
abstract = {We present a novel introspective variational autoencoder (IntroVAE) model for synthesizing high-resolution photographic images. IntroVAE is capable of self-evaluating the quality of its generated samples and improving itself accordingly. Its inference and generator models are jointly trained in an introspective way. On one hand, the generator is required to reconstruct the input images from the noisy outputs of the inference model as normal VAEs. On the other hand, the inference model is encouraged to classify between the generated and real samples while the generator tries to fool it as GANs. These two famous generative frameworks are integrated in a simple yet efficient single-stream architecture that can be trained in a single stage. IntroVAE preserves the advantages of VAEs, such as stable training and nice latent manifold. Unlike most other hybrid models of VAEs and GANs, IntroVAE requires no extra discriminators, because the inference model itself serves as a discriminator to distinguish between the generated and real samples. Experiments demonstrate that our method produces high-resolution photo-realistic images (e.g., CELEBA images at \(1024^{2}\)), which are comparable to or better than the state-of-the-art GANs.},
author = {Huang, Huaibo and Li, Zhihang and He, Ran and Sun, Zhenan and Tan, Tieniu},
file = {:Users/hahosoya/Documents/Mendeley Desktop/7291-introvae-introspective-variational-autoencoders-for-photographic-image-synthesis.pdf:pdf},
journal = {Adv. Neural Inf. Process. Syst.},
number = {NeurIPS},
pages = {52--63},
title = {{Introvae: Introspective variational autoencoders for photographic image synthesis}},
volume = {2018-Decem},
year = {2018}
}
@article{Haxby2001,
abstract = {Abstract The functional architecture of the object vision pathway in the human brain was investigated using functional magnetic resonance imaging to measure patterns of response in ventral temporal cortex while subjects viewed faces, cats, five categories of man-made objects, and nonsense pictures. A distinct pattern of response was found for each stimulus category. The distinctiveness of the response to a given category was not due simply to ...
},
author = {Haxby, J V and Gobbini, M I and Furey, M L and Ishai, A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2001 Haxby.pdf:pdf},
month = {jan},
title = {{Distributed and overlapping representations of faces and objects in ventral temporal cortex}},
year = {2001}
}
@article{Norman-Haignere2013,
author = {Norman-Haignere, S and Kanwisher, N and McDermott, J H},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2013 Norman-Haignere.pdf:pdf},
journal = {J. Neurosci.},
month = {dec},
number = {50},
pages = {19451--19469},
title = {{Cortical Pitch Regions in Humans Respond Primarily to Resolved Harmonics and Are Located in Specific Tonotopic Regions of Anterior Auditory Cortex}},
volume = {33},
year = {2013}
}
@article{Bonhoeffer1991,
abstract = {The mammalian cortex is organized in a columnar fashion: neurons lying below each other from the pia to the white matter usually share many functional properties. Across the cortical surface, cells with similar response properties are also clustered together, forming elongated bands or patches. Some response properties, such as orientation preference in the visual cortex, change gradually across the cortical surface forming 'orientation maps'. To determine the precise layout of iso-orientation domains, knowledge of responses not only to one but to many stimulus orientations is essential. Therefore, the exact depiction of orientation maps has been hampered by technical difficulties and remained controversial for almost thirty years. Here we use in vivo optical imaging based on intrinsic signals to gather information on the responses of a piece of cortex to gratings in many different orientations. This complete set of responses then provides detailed information on the structure of the orientation map in a large patch of cortex from area 18 of the cat. We find that cortical regions that respond best to one orientation form highly ordered patches rather than elongated bands. These iso-orientation patches are organized around 'orientation centres', producing pinwheel-like patterns in which the orientation preference of cells is changing continuously across the cortex. We have also analysed our data for fast changes in orientation preference and find that these 'fractures' are limited to the orientation centres. The pinwheels and orientation centres are such a prominent organizational feature that it should be important to understand their development as well as their function in the processing of visual information.},
address = {Rockefeller University, Laboratory of Neurobiology, New York, New York 10021.},
author = {Bonhoeffer, T and Grinvald, A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature 1991 Bonhoeffer.pdf:pdf},
journal = {Nature},
month = {oct},
number = {6343},
pages = {429--431},
title = {{Iso-orientation domains in cat visual cortex are arranged in pinwheel-like patterns.}},
volume = {353},
year = {1991}
}
@article{Shepherd2021,
abstract = {Functions of the neocortex depend on its bidirectional communication with the thalamus, via cortico-thalamo-cortical (CTC) loops. Recent work dissecting the synaptic connectivity in these loops is generating a clearer picture of their cellular organization. Here, we review findings across sensory, motor and cognitive areas, focusing on patterns of cell type-specific synaptic connections between the major types of cortical and thalamic neurons. We outline simple and complex CTC loops, and note features of these loops that appear to be general versus specialized. CTC loops are tightly interlinked with local cortical and corticocortical (CC) circuits, forming extended chains of loops that are probably critical for communication across hierarchically organized cerebral networks. Such CTC–CC loop chains appear to constitute a modular unit of organization, serving as scaffolding for area-specific structural and functional modifications. Inhibitory neurons and circuits are embedded throughout CTC loops, shaping the flow of excitation. We consider recent findings in the context of established CTC and CC circuit models, and highlight current efforts to pinpoint cell type-specific mechanisms in CTC loops involved in consciousness and perception. As pieces of the connectivity puzzle fall increasingly into place, this knowledge can guide further efforts to understand structure–function relationships in CTC loops.},
author = {Shepherd, Gordon M.G. and Yamawaki, Naoki},
file = {:Users/hahosoya/Documents/Mendeley Desktop/s41583-021-00459-3.pdf:pdf},
isbn = {0123456789},
journal = {Nat. Rev. Neurosci.},
number = {7},
pages = {389--406},
pmid = {33958775},
title = {{Untangling the cortico-thalamo-cortical loop: cellular pieces of a knotty circuit puzzle}},
volume = {22},
year = {2021}
}
@article{Loffler2005,
abstract = {Abstract fMRI (functional magnetic resonance imaging) studies on humans have shown a cortical area, the fusiform face area, that is specialized for face processing. An important question is how faces are represented within this area. This study provides direct ... 
},
author = {Loffler, Gunter and Yourganov, Grigori and Wilkinson, Frances and Wilson, Hugh R},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature neuroscience 2005 Loffler-1.pdf:pdf},
journal = {Nat. Neurosci.},
month = {sep},
number = {10},
pages = {1386--1391},
title = {{fMRI evidence for the neural representation of faces}},
volume = {8},
year = {2005}
}
@article{Gray1996,
abstract = {Abstract In response to visual stimulation, a subset of neurons in the striate and prestriate cortex displays synchronous rhythmic firing in the gamma frequency band (20 to 70 hertz). This finding has raised two fundamental questions: What is the functional significance of  ...},
author = {Gray, C M and McCormick, D A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Science 1996 Gray.pdf:pdf},
journal = {Science},
month = {jan},
title = {{Chattering cells: superficial pyramidal neurons contributing to the generation of synchronous oscillations in the visual cortex}},
year = {1996}
}
@article{Pasupathy2001,
abstract = {Visual shape recognition in primates depends on a multi-stage pathway running from primary visual cortex (V1) to inferotemporal cortex (IT). The mechanisms by which local shape signals from V1 are transformed into selectivity for abstract object categories in IT are unknown. One approach to this issue is to investigate shape representation at intermediate stages in the pathway, such as area V4. We studied 109 V4 cells that appeared sensitive to complex shape in preliminary tests. To achieve a more complete picture of shape representation in V4, we tested each cell with a set of 366 stimuli, constructed by systematically combining convex and concave boundary elements into closed shapes. Using this large, diverse stimulus set, we found that all the cells in our sample responded to a wide variety of shapes and did not appear to encode any single type of global shape. However, for most cells the shapes evoking strongest responses were characterized by a consistent type of boundary conformation at a specific position within the stimulus. For example, a given cell might be tuned for shapes containing concave curvature at the right, with other parts of the shape having little or no effect on responses. Many cells were tuned for more complex boundary configurations (e.g., a convex angle adjacent to a concave curve). We quantified this kind of shape tuning with Gaussian functions on a curvature x position domain. These tuning functions fit the neural responses much better than tuning functions based on edge or axis orientation. Thus individual V4 cells appear to encode moderately complex boundary information at specific locations within larger shapes. This finding suggests that, at intermediate stages in the V1-IT transformation, complex objects are represented at least partly in terms of the configurations and positions of their contour components.},
address = {Department of Biomedical Engineering, Baltimore, Maryland 21218, USA.},
author = {Pasupathy, A and Connor, C E},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neurophysiology 2001 Pasupathy.pdf:pdf},
journal = {J. Neurophysiol.},
month = {nov},
number = {5},
pages = {2505--2519},
title = {{Shape representation in area V4: position-specific tuning for boundary conformation.}},
volume = {86},
year = {2001}
}
@article{Nadler2008,
author = {Nadler, Jacob W and Angelaki, Dora E and DeAngelis, Gregory C},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature 2008 Nadler.pdf:pdf},
journal = {Nature},
month = {mar},
number = {7187},
pages = {642--645},
title = {{A neural representation of depth from motion parallax in macaque visual cortex}},
volume = {452},
year = {2008}
}
@article{Arcaro2017,
author = {Arcaro, Michael J and Schade, Peter F and Vincent, Justin L and Ponce, Carlos R and Livingstone, Margaret S},
file = {:Users/hahosoya/Documents/Mendeley Desktop/A0592E21-8879-4ABB-A55D-5F7776EB7628.pdf:pdf},
journal = {Nat. Neurosci.},
month = {sep},
number = {10},
pages = {1404--1412},
title = {{Seeing faces is necessary for face-domain formation}},
volume = {20},
year = {2017}
}
@article{Orban2011,
author = {Orban, Guy A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Annu. Rev. Neurosci. 2011 Orban.pdf:pdf},
journal = {Annu. Rev. Neurosci.},
month = {jul},
number = {1},
pages = {361--388},
title = {{The Extraction of 3D Shape in the Visual System of Human and Nonhuman Primates}},
volume = {34},
year = {2011}
}
@article{Rolls1994,
abstract = {In experiments to investigate the duration of the time for which cortical neurons respond when the identification of a visual stimulus is just possible, we presented a test face stimulus for 16 ms, and followed it at different intervals by a masking stimulus (either an N-O pattern or a face) while recording from single neurons in the temporal visual cortex of macaques. When there was no mask the cells responded to the 16 ms of the test stimulus for 200-300 ms, far longer than the presentation time. We suggest that this reflects the operation of a short-term memory system implemented in cortical circuitry. If the mask was a stimulus which did not stimulate the cells (either a non-face pattern or a face which was a non-effective stimulus for that cell), then, as the interval between the onset of the test stimulus and the onset of the mask stimulus (the stimulus onset asynchrony) was reduced, the length of time for which the cells fired in response to the test stimulus was reduced. It is suggested that this is due to the mask stimulating adjacent cells in the cortex which by lateral inhibition reduce the responses of the cells activated by the test stimulus. When the stimulus onset asynchrony was 20 ms, face-selective neurons in the inferior temporal cortex of macaques responded for a period of 20-30 ms before their firing was interrupted by the mask. With the same test-mask stimulus onset asynchrony of 20 ms, humans could just identify which of six faces was shown.(ABSTRACT TRUNCATED AT 250 WORDS)},
address = {University of Oxford, Department of Experimental Psychology, U.K.},
author = {Rolls, E T and Tovee, M J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proc. Biol. Sci. 1994 Rolls.pdf:pdf},
journal = {Proc. Biol. Sci.},
month = {jul},
number = {1348},
pages = {9--15},
title = {{Processing speed in the cerebral cortex and the neurophysiology of visual masking.}},
volume = {257},
year = {1994}
}
@article{Tredoux2002,
abstract = {Research is reported on a measure of facial similarity in which the similarity of 2 faces is defined as the Euclidean distance between them in a principal-component space. Five studies were conducted in which participants rated sets of facial images, and in which the measure was applied to 2 problems in the eyewitness literature. Comparisons of ratings with distances derived from the principal-component analysis suggest that the measure corresponds reasonably well to perceptions of facial similarity. In addition, the measure correlates strongly with empirical measures of lineup fairness and is related to eyewitness identification performance. Further potential applications include a software tool for constructing arrays of faces of varying similarity, and a software tool for reconstructing facial images from memory.},
address = {Department of Psychology, University of Cape Town, Rondebosch, South Africa. plato@humanities.uct.ac.za},
author = {Tredoux, Colin},
file = {:Users/hahosoya/Documents/Mendeley Desktop/J Exp Psychol Appl 2002 Tredoux.pdf:pdf},
journal = {J. Exp. Psychol. Appl.},
month = {sep},
number = {3},
pages = {180--193},
title = {{A direct measure of facial similarity and its relation to human similarity perceptions.}},
volume = {8},
year = {2002}
}
@article{Miller2010,
author = {Miller, P and Katz, D B},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2010 Miller.pdf:pdf},
journal = {J. Neurosci.},
month = {feb},
number = {7},
pages = {2559--2570},
title = {{Stochastic Transitions between Neural States in Taste Processing and Decision-Making}},
volume = {30},
year = {2010}
}
@article{Jones2012,
author = {Jones, David T and Vemuri, Prashanthi and Murphy, Matthew C and Gunter, Jeffrey L and Senjem, Matthew L and Machulda, Mary M and Przybelski, Scott A and Gregg, Brian E and Kantarci, Kejal and Knopman, David S and Boeve, Bradley F and Petersen, Ronald C and Jack, Clifford R},
editor = {He, Yong},
file = {:Users/hahosoya/Documents/Mendeley Desktop/PLoS ONE 2012 Jones.pdf:pdf},
journal = {PLoS One},
month = {jun},
number = {6},
pages = {e39731},
title = {{Non-Stationarity in the “Resting Brain's” Modular Architecture}},
volume = {7},
year = {2012}
}
@article{FeldmanHall2021,
abstract = {How do we evaluate whether someone will make a good friend or collaborative peer? A hallmark of human cognition is the ability to make adaptive decisions based on information garnered from limited prior experiences. Using an interactive social task measuring adaptive choice (deciding who to reengage or avoid) in male and female participants, we find the hippocampus supports value-based social choices following single-shot learning. These adaptive choices elicited a suppression signal in the hippocampus, revealing sensitivity for the subjective perception of a person and how well they treat you during choice. The extent to which the hippocampus was suppressed was associated with flexibly interacting with prior generous individuals and avoiding selfish individuals. Further, we found that hippocampal signals during decision-making were related to subsequent memory for a person and the offer they made before. Consistent with the hippocampus leveraging previously executed choices to solidify a reliable neural signature for future adaptive behavior, we also observed a later hippocampal enhancement. These findings highlight the hippocampus playing a multifaceted role in socially adaptive learning.SIGNIFICANCE STATEMENT Adaptively navigating social interactions requires an integration of prior experiences with information gleaned from the current environment. While most research has focused on striatal-based feedback learning, open questions remain regarding the role of hippocampal-based episodic memory systems. Here, we show that during social decisions based on prior experience, hippocampal suppression signals were sensitive to adaptive choice, while hippocampal enhancements was related to subsequent memory for the original social interaction. These findings highlight the hippocampus playing a multifaceted role in socially adaptive learning.},
author = {FeldmanHall, Oriel and Montez, David F. and Phelps, Elizabeth A. and Davachi, Lila and Murty, Vishnu P.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/1340.full.pdf:pdf},
journal = {J. Neurosci.},
keywords = {Dictator Task,choice,decision-making,hippocampus,social learning,value},
number = {6},
pages = {1340--1348},
pmid = {33361462},
title = {{Hippocampus Guides Adaptive Learning during Dynamic Social Interactions}},
volume = {41},
year = {2021}
}
@article{Olshausen1997,
abstract = {The spatial receptive fields of simple cells in mammalian striate cortex have been reasonably well described physiologically and can be characterized as being localized, oriented, and bandpass, comparable with the basis functions of wavelet transforms. Previously, we have shown that these receptive field properties may be accounted for in terms of a strategy for producing a sparse distribution of output activity in response to natural images. Here, in addition to describing this work in a more expansive fashion, we examine the neurobiological implications of sparse coding. Of particular interest is the case when the code is overcomplete--i.e., when the number of code elements is greater than the effective dimensionality of the input space. Because the basis functions are non-orthogonal and not linearly independent of each other, sparsifying the code will recruit only those basis functions necessary for representing a given input, and so the input-output function will deviate from being purely linear. These deviations from linearity provide a potential explanation for the weak forms of non-linearity observed in the response properties of cortical simple cells, and they further make predictions about the expected interactions among units in response to naturalistic stimuli.},
address = {Department of Psychology, Cornell University, Ithaca, NY 14853, USA. bruno@redwood.ucdavis.edu},
author = {Olshausen, B A and Field, D J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Vision Research 1997 Olshausen.pdf:pdf},
journal = {Vision Res.},
month = {dec},
number = {23},
pages = {3311--3325},
title = {{Sparse coding with an overcomplete basis set: a strategy employed by V1?}},
volume = {37},
year = {1997}
}
@article{Patel2016,
abstract = {Abstract We develop a probabilistic framework for deep learning based on the Deep Rendering Mixture Model (DRMM), a new generative probabilistic model that explicitly capture variations in data due to latent task nuisance variables. We demonstrate that max- sum inference in the DRMM yields an algorithm that exactly reproduces the operations in deep convolutional neural networks (DCNs), providing a first principles derivation. Our ...
},
author = {Patel, A B and Nguyen, M T and Baraniuk, R},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Advances in neural information {\ldots} 2016 Patel.pdf:pdf},
journal = {Adv. Neural Inf. {\ldots}},
month = {jan},
title = {{A probabilistic framework for deep learning}},
year = {2016}
}
@article{Connor1996,
abstract = {1. We studied the spatial interaction between stimulus and attention in macaque area V4. Monkeys were required to fixate a small spot while continuously attending to a ring-shaped target within a large array of identical rings. Meanwhile, the response of the V4 cell under study was tested by flashing behaviorally irrelevant bar stimuli in the cell's classical receptive field (CRF). The location of the attended ring was varied across four positions surrounding the CRF, and the location of the bar stimulus was varied across five positions spanning the CRF. 2. Response strength depended on two aspects of the spatial relationship between the stimulus driving the cell (the bar) and the position of attention (the target ring). First, for 49% of the cells studied, responses were greater for bar stimuli near the attended ring; i.e., the receptive field profile shifted toward the attentional focus. Second, for 84% of the cells, the overall response level depended on the direction in which attention lay relative to the stimulus in the CRF (e.g., to the left, right, above, or below). 3. This study confirms a key prediction of spatial models of attention, which postulate enhanced processing of all stimuli near the attentional focus. It also introduces the novel finding that responses are influenced by the relative direction of attention. This result indicates that area V4 carries information about the spatial relationship between visual stimuli and attention.},
address = {Department of Anatomy and Neurobiology, Washington University School of Medicine, St. Louis, Missouri 63110, USA.},
author = {Connor, C E and Gallant, J L and Preddie, D C and {Van Essen}, D C},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neurophysiology 1996 Connor.pdf:pdf},
journal = {J. Neurophysiol.},
month = {mar},
number = {3},
pages = {1306--1308},
title = {{Responses in area V4 depend on the spatial relationship between stimulus and attention.}},
volume = {75},
year = {1996}
}
@article{Maus2013,
abstract = {Neuron, 78 (2013) 554-562. doi:10.1016/j.neuron.2013.03.010},
author = {Maus, Gerrit W and Fischer, Jason and Whitney, David},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neuron 2013 Maus.pdf:pdf},
journal = {Neuron},
month = {may},
number = {3},
pages = {554--562},
title = {{Motion-Dependent Representation of Space in Area MT+}},
volume = {78},
year = {2013}
}
@article{Chen2013,
author = {Chen, X and DeAngelis, G C and Angelaki, D E},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2013 Chen.pdf:pdf},
journal = {J. Neurosci.},
month = {nov},
number = {47},
pages = {18574--18582},
title = {{Eye-Centered Representation of Optic Flow Tuning in the Ventral Intraparietal Area}},
volume = {33},
year = {2013}
}
@article{Kang2020,
abstract = {Emergentism and pragmatics are two research fields that study the dynamics of linguistic communication along substantially different timescales and intelligence levels. From the perspective of multi-agent reinforcement learning, they correspond to stochastic games with reinforcement training and stage games with opponent awareness. Given that their combination has been explored in linguistics, we propose computational models that combine short-term mutual reasoning-based pragmatics with long-term language emergentism. We explore this for agent communication referential games as well as in Starcraft II, assessing the relative merits of different kinds of mutual reasoning pragmatics models both empirically and theoretically. Our results shed light on their importance for making inroads towards getting more natural, accurate, robust, fine-grained, and succinct utterances.},
archivePrefix = {arXiv},
arxivId = {2006.04109},
author = {Kang, Yipeng and Wang, Tonghan and de Melo, Gerard},
eprint = {2006.04109},
file = {:Users/hahosoya/Documents/Mendeley Desktop/46df782760e2b230d936c485288333a19b5bcb39.pdf:pdf},
journal = {arXiv},
title = {{Incorporating pragmatic reasoning communication into emergent language}},
year = {2020}
}
@article{Puertas2010,
abstract = {Page 1. The Maximal  Causes of Natural Scenes are Edge Filters Gervasio Puertas∗ 
Frankfurt Institute for Advanced Studies Goethe-University Frankfurt, Germany
puertas@fias.uni-frankfurt.de J{\"{o}}rg Bornschein∗ Frankfurt Institute ...
},
author = {Puertas, G and et Al},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proc. NIPS 2010 Puertas.pdf:pdf},
journal = {Proc. NIPS},
month = {jan},
title = {{The maximal causes of natural scenes are edge filters}},
year = {2010}
}
@article{Dehaene2010,
abstract = {Does literacy improve brain function? Does it also entail losses? Using functional magnetic resonance imaging, we measured brain responses to spoken and written language, visual faces, houses, tools, and checkers in adults of variable literacy (10 were illiterate, 22 became literate as adults, and 31 were literate in childhood). As literacy enhanced the left fusiform activation evoked by writing, it induced a small competition with faces at this location, but also broadly enhanced visual responses in fusiform and occipital cortex, extending to area V1. Literacy also enhanced phonological activation to speech in the planum temporale and afforded a top-down activation of orthography from spoken inputs. Most changes occurred even when literacy was acquired in adulthood, emphasizing that both childhood and adult education can profoundly refine cortical organization.},
author = {Dehaene, Stanislas and Pegado, Felipe and Braga, Lucia W. and Ventura, Paulo and {Nunes Filho}, Gilberto and Jobert, Antoinette and Dehaene-Lambertz, Ghislaine and Kolinsky, R{\'{e}}gine and Morais, Jos{\'{e}} and Cohen, Laurent},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Dehaene.pdf:pdf},
journal = {Science},
number = {6009},
pages = {1359--1364},
title = {{How learning to read changes the cortical networks for vision and language}},
volume = {330},
year = {2010}
}
@article{Brea2013,
author = {Brea, J and Senn, W and Pfister, J P},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2013 Brea.pdf:pdf},
journal = {J. Neurosci.},
month = {jun},
number = {23},
pages = {9565--9575},
title = {{Matching Recall and Storage in Sequence Learning with Spiking Neural Networks}},
volume = {33},
year = {2013}
}
@article{Donahue2019,
abstract = {Adversarially trained generative models (GANs) have recently achieved compelling image synthesis results. But despite early successes in using GANs for unsupervised representation learning, they have since been superseded by approaches based on self-supervision. In this work we show that progress in image generation quality translates to substantially improved representation learning performance. Our approach, BigBiGAN, builds upon the state-of-the-art BigGAN model, extending it to representation learning by adding an encoder and modifying the discriminator. We extensively evaluate the representation learning and generation capabilities of these BigBiGAN models, demonstrating that these generation-based models achieve the state of the art in unsupervised representation learning on ImageNet, as well as in unconditional image generation. Pretrained BigBiGAN models - including image generators and encoders - are available on TensorFlow Hub.},
archivePrefix = {arXiv},
arxivId = {1907.02544},
author = {Donahue, Jeff and Simonyan, Karen},
eprint = {1907.02544},
file = {:Users/hahosoya/Documents/Mendeley Desktop/cde35c87aaabbc617d38f9cfaa2721a2e166d750.pdf:pdf},
journal = {Adv. Neural Inf. Process. Syst.},
title = {{Large scale adversarial representation learning}},
volume = {32},
year = {2019}
}
@article{Nadler2013,
author = {Nadler, J W and Barbash, D and Kim, H R and Shimpi, S and Angelaki, D E and DeAngelis, G C},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2013 Nadler.pdf:pdf},
journal = {J. Neurosci.},
month = {aug},
number = {35},
pages = {14061--14074},
title = {{Joint Representation of Depth from Motion Parallax and Binocular Disparity Cues in Macaque Area MT}},
volume = {33},
year = {2013}
}
@article{Hyvarinen1999b,
abstract = {Independent component analysis (ICA) is a statistical method for transforming an observed multidimensional random vector into components that are statistically as independent from each other as possible. In this paper, we use a combination of two different approaches for linear ICA: Comon's information-theoretic approach and the projection pursuit approach. Using maximum entropy approximations of differential entropy, we introduce a family of new contrast (objective) functions for ICA. These contrast functions enable both the estimation of the whole decomposition by minimizing mutual information, and estimation of individual independent components as projection pursuit directions. The statistical properties of the estimators based on such contrast functions are analyzed under the assumption of the linear mixture model, and it is shown how to choose contrast functions that are robust and/or of minimum variance. Finally, we introduce simple fixed-point algorithms for practical optimization of the contrast functions. These algorithms optimize the contrast functions very fast and reliably.},
address = {Helsinki University of Technology, Laboratory of Computer and Information Science, FIN-02015 HUT, Finland.},
author = {Hyv{\"{a}}rinen, A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/IEEE Trans. Neural Netw. 1999 Hyv{\"{a}}rinen.pdf:pdf},
journal = {IEEE Trans. Neural Networks},
month = {jan},
number = {3},
pages = {626--634},
title = {{Fast and robust fixed-point algorithms for independent component analysis.}},
volume = {10},
year = {1999}
}
@article{Tsunoda2001,
abstract = {Intrinsic signal imaging from inferotemporal (IT) cortex, a visual area essential for object perception and recognition, revealed that visually presented objects activated patches in a distributed manner. When visual features of these objects were partially removed, the simplified stimuli activated only a subset of the patches elicited by the originals. This result, in conjunction with extracellular recording, suggests that an object is represented by a combination of cortical columns, each of which represents a visual feature (feature column). Simplification of an object occasionally caused the appearance of columns that were not active when viewing the more complex form. Thus, not all the columns related to a particular feature were necessarily activated by the original objects. Taken together, these results suggest that objects may be represented not only by simply combining feature columns but also by using a variety of combinations of active and inactive columns for individual features.},
address = {Laboratory for Integrative Neural Systems, Brain Science Institute, The Institute of Physical and Chemical Research (RIKEN), Hirosawa 2-1, Wako-shi, Saitama 351-0198, Japan.},
author = {Tsunoda, K and Yamane, Y and Nishizaki, M and Tanifuji, M},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature neuroscience 2001 Tsunoda-1.pdf:pdf},
journal = {Nat. Neurosci.},
month = {aug},
number = {8},
pages = {832--838},
title = {{Complex objects are represented in macaque inferotemporal cortex by the combination of feature columns.}},
volume = {4},
year = {2001}
}
@article{HyodongLeea1EshedMargalitbMichaelA.CohencdeNancyKanwishereDanielL.K.Yaminsh2016,
abstract = {A hierarchy of timescales is ubiquitous in biological systems, where enzymatic reactions play an important role because they can hasten the relaxation to equilibrium. We introduced a statistical physics model of interacting spins that also incorporates enzymatic reactions to extend the classic model for allosteric regulation. Through Monte Carlo simulations and theoretical analysis, we found that the relaxation dynamics are much slower than the elementary reactions and are logarithmic in time with several plateaus, as is commonly observed for glasses. This is because of the kinetic constraints from the cooperativity via the competition between enzymes, which tend to bind to molecules with the limited structure. This inhibits the progress of the modification reaction. Our model showed symmetry breaking in the relaxation trajectories that led to inherently kinetic phase transitions without any correspondence to the equilibrium state. In this paper, we discuss the relevance of these results for diverse responses in biology.},
author = {{Hyodong Leea, 1, Eshed Margalitb, Michael A. Cohenc, d, e, Nancy Kanwishere, Daniel L. K. Yamins, h}, and James J. DiCarloe},
file = {:Users/hahosoya/Documents/Mendeley Desktop/623e3e9d9f881c8e934abd93e11a7754ae629a39.pdf:pdf},
journal = {Manuscript},
keywords = {1,keyword 2,keyword 3},
number = {Xx},
pages = {1--8},
title = {{Topographic deep artificial neural networks reproduce the hallmarks of face processing in primate inferior temporal cortex}},
volume = {XXX},
year = {2016}
}
@article{Salinas1997,
abstract = {Inferotemporal (IT) neurons exhibit a substantial degree of invariance with respect to translation of images used as visual stimuli. Through theoretical and computer-modeling methods, we show how translation-invariant receptive fields, like those of IT neurons, can be generated from the responses of V4 neurons if the effects of attention are taken into account. The model incorporates a recently reported form of attention-dependent gain modulation in V4 and produces IT receptive fields that shift so they are centered at the point where attention is directed. Receptive fields of variable, attention-controlled spatial scale are obtained when the mechanism is extended to scale-dependent attentional gain fields. The results indicate that gain modulation may play analogous roles in the dorsal and ventral visual pathways, generating transformations from retinal coordinates to body- and object-centered systems, respectively.},
address = {Volen Center for Complex Systems, Brandeis University, Waltham, Massachusetts 02254-9110, USA.},
author = {Salinas, E and Abbott, L F},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neurophysiology 1997 Salinas.pdf:pdf},
journal = {J. Neurophysiol.},
month = {jun},
number = {6},
pages = {3267--3272},
title = {{Invariant visual responses from attentional gain fields.}},
volume = {77},
year = {1997}
}
@article{Yoshimura2005,
abstract = {Abstract Excitatory cortical neurons form fine-scale networks of precisely interconnected neurons. Here we tested whether inhibitory cortical neurons in rat visual cortex might also be connected with fine-scale specificity. Using paired intracellular recordings and cross- ... 
},
author = {Yoshimura, Y},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature neuroscience 2005 Yoshimura-1.pdf:pdf},
journal = {Nat. Neurosci.},
month = {jan},
title = {{Fine-scale specificity of cortical networks depends on inhibitory cell type and connectivity : Article : Nature Neuroscience}},
year = {2005}
}
@inproceedings{pmlr-v80-ritter18a,
abstract = {Meta-learning agents excel at rapidly learning new tasks from open-ended task distributions; yet, they forget what they learn about each task as soon as the next begins. When tasks reoccur {–} as they do in natural environments {–} meta-learning agents must explore again instead of immediately exploiting previously discovered solutions. We propose a formalism for generating open-ended yet repetitious environments, then develop a meta-learning architecture for solving these environments. This architecture melds the standard LSTM working memory with a differentiable neural episodic memory. We explore the capabilities of agents with this episodic LSTM in five meta-learning environments with reoccurring tasks, ranging from bandits to navigation and stochastic sequential decision problems.},
address = {Stockholmsm{\"{a}}ssan, Stockholm Sweden},
author = {Ritter, Samuel and Wang, Jane X. and Kurth-Nelson, Zeb and Jayakumar, Siddhant M. and Blundell, Charles and Pascanu, Razvan and Botvinick, Matthew},
booktitle = {Proc. 35th Int. Conf. Mach. Learn.},
editor = {Dy, Jennifer and Krause, Andreas},
file = {:Users/hahosoya/Documents/Mendeley Desktop/68c16a949adbe7be52c89173404ef8f81df55a0f.pdf:pdf},
number = {1},
pages = {4354--4363},
series = {Proceedings of Machine Learning Research},
title = {{Been There, Done That: Meta-Learning with Episodic Recall}},
volume = {80},
year = {2018}
}
@article{Wu2006,
abstract = {System identification is a growing approach to sensory neurophysiology that facilitates the development of quantitative functional models of sensory processing. This approach provides a clear set of guidelines for combining experimental data with other knowledge about sensory function to obtain a description that optimally predicts the way that neurons process sensory information. This prediction paradigm provides an objective method for evaluating and comparing computational models. In this chapter we review many of the system identification algorithms that have been used in sensory neurophysiology, and we show how they can be viewed as variants of a single statistical inference problem. We then review many of the practical issues that arise when applying these methods to neurophysiological experiments: stimulus selection, behavioral control, model visualization, and validation. Finally we discuss several problems to which system identification has been applied recently, including one important long-term goal of sensory neuroscience: developing models of sensory systems that accurately predict neuronal responses under completely natural conditions.},
address = {Biophysics Graduate Group, University of California, Berkeley, California 94720, USA.},
author = {Wu, Michael C-K and David, Stephen V and Gallant, Jack L},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Annu. Rev. Neurosci. 2006 Wu.pdf:pdf},
journal = {Annu. Rev. Neurosci.},
month = {jan},
pages = {477--505},
title = {{Complete functional characterization of sensory neurons by system identification.}},
volume = {29},
year = {2006}
}
@article{Amit2003,
abstract = {We describe an architecture for invariant visual detection and recognition. Learning is performed in a single central module. The architecture makes use of a replica module consisting of copies of retinotopic layers of local features, with a particular design of inputs and outputs, that ...},
author = {Amit, Y and Mascaro, M},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Vision Research 2003 Amit.pdf:pdf},
journal = {Vision Res.},
month = {jan},
title = {{An integrated network for invariant visual detection and recognition}},
year = {2003}
}
@article{DiCarlo2012,
abstract = {Neuron, 73 (2012) 415-434. doi:10.1016/j.neuron.2012.01.010},
author = {DiCarlo, James J and Zoccolan, Davide and Rust, Nicole C},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neuron 2012 DiCarlo.pdf:pdf},
journal = {Neuron},
month = {feb},
number = {3},
pages = {415--434},
title = {{How Does the Brain Solve Visual Object Recognition?}},
volume = {73},
year = {2012}
}
@article{Lykou2010,
abstract = {Canonical correlation analysis ( CCA ) describes the relationship between two sets of variables by finding linear combinations of the variables with maximal correlation. A sparse version of CCA is proposed that reduces the chance of including unimportant variables in ... 
},
author = {Lykou, Anastasia and Whittaker, Joe},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Computational Statistics & Data Analysis 2010 Lykou.pdf:pdf},
journal = {Comput. Stat. Data Anal.},
month = {dec},
number = {12},
pages = {3144--3157},
title = {{Sparse CCA using a Lasso with positivity constraints}},
volume = {54},
year = {2010}
}
@article{Fournier2011,
abstract = {Receptive fields in primary visual cortex (V1) are categorized as simple or complex, depending on their spatial selectivity to stimulus contrast polarity. We studied the dependence of this classification on visual context by comparing, in the same cell, the synaptic responses to three classical receptive field mapping protocols: sparse noise, ternary dense noise and flashed Gabor noise. Intracellular recordings revealed that the relative weights of simple-like and complex-like receptive field components were scaled so as to make the same receptive field more simple-like with dense noise stimulation and more complex-like with sparse or Gabor noise stimulations. However, once these context-dependent receptive fields were convolved with the corresponding stimulus, the balance between simple-like and complex-like contributions to the synaptic responses appeared to be invariant across input statistics. This normalization of the linear/nonlinear input ratio suggests a previously unknown form of homeostatic control of V1 functional properties, optimizing the network nonlinearities to the statistical structure of the visual input.},
address = {Unit{\'{e}} de Neuroscience, Information et Complexit{\'{e}} (CNRS-UNIC), UPR CNRS 3293, Gif-sur-Yvette, France. Fournier@unic.cnrs-gif.fr},
author = {Fournier, Julien and Monier, Cyril and Pananceau, Marc and Fr{\'{e}}gnac, Yves},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature neuroscience 2011 Fournier-1.pdf:pdf},
journal = {Nat. Neurosci.},
month = {aug},
number = {8},
pages = {1053--1060},
title = {{Adaptation of the simple or complex nature of V1 receptive fields to visual statistics.}},
volume = {14},
year = {2011}
}
@article{Liu2016a,
abstract = {Clothing retrieval and clothing style recognition are important and practical problems. They have drawn a lot of attention in recent years. However, the clothing photos collected in existing datasets are mostly of front- or near-front view. There are no datasets designed to study the influences of dif-ferent viewing angles on clothing retrieval performance. To address view-invariant clothing retrieval problem properly, we construct a challenge clothing dataset, called Multi-View Clothing dataset. This dataset not only has four different views for each clothing item, but also provides 264 attributes for describing clothing appearance. We adopt a state-of-the- Art deep learning method to present baseline results for the attribute prediction and clothing retrieval performance. We also evaluate the method on a more difficult setting, cross- view exact clothing item retrieval. Our dataset will be made publicly available for further studies towards view-invariant clothing retrieval. 2016 ACM.},
author = {Liu, Kuan-Hsien and Chen, Ting-Yen and Chen, Chu-Song},
file = {:Users/hahosoya/Documents/Mendeley Desktop/7ca34de1817ef302b2fd0f34e38b848c52deac58.pdf:pdf},
isbn = {9781450343596},
journal = {6th ACM Int. Conf. Multimed. Retr.},
keywords = {Information retrieval},
pages = {313--316},
title = {{MVC: A dataset for view-invariant clothing retrieval and attribute prediction}},
year = {2016}
}
@article{Sato2013,
abstract = {There are two dominant models for the functional organization of brain regions underlying object recognition. One model postulates category-specific modules while the other proposes a distributed representation of objects with generic visual features. Functional imaging techniques relying on metabolic signals, such as fMRI and optical intrinsic signal imaging (OISI), have been used to support both models, but due to the indirect nature of the measurements in these techniques, the existing data for one model cannot be used to support the other model. Here, we used large-scale multielectrode recordings over a large surface of anterior inferior temporal (IT) cortex, and densely mapped stimulus-evoked neuronal responses. We found that IT cortex is subdivided into distinct domains characterized by similar patterns of responses to the objects in our stimulus set. Each domain spanned several millimeters on the cortex. Some of these domains represented faces ("face" domains) or monkey bodies ("monkey-body" domains). We also identified domains with low responsiveness to faces ("anti-face" domains). Meanwhile, the recording sites within domains that displayed category selectivity showed heterogeneous tuning profiles to different exemplars within each category. This local heterogeneity was consistent with the stimulus-evoked feature columns revealed by OISI. Taken together, our study revealed that regions with common functional properties (domains) consist of a finer functional structure (columns) in anterior IT cortex. The "domains" and previously proposed "patches" are rather like "mosaics" where a whole mosaic is characterized by overall similarity in stimulus responses and pieces of the mosaic correspond to feature columns.},
address = {RIKEN Brain Science Institute, Wako, Saitama 351-0198, Japan, Helen Wills Neuroscience Institute, University of California at Berkeley, Berkeley, California 94720, Department of Complexity Science and Engineering, Graduate School of Frontier Sciences, The},
author = {Sato, Takayuki and Uchida, Go and Lescroart, Mark D and Kitazono, Jun and Okada, Masato and Tanifuji, Manabu},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2013 Sato.pdf:pdf},
journal = {J. Neurosci.},
month = {oct},
number = {42},
pages = {16642--16656},
title = {{Object representation in inferior temporal cortex is organized hierarchically in a mosaic-like structure.}},
volume = {33},
year = {2013}
}
@inproceedings{Schuldt2004,
author = {Sch{\"{u}}ldt, Christian and Laptev, Ivan and Caputo, Barbara},
booktitle = {Int. Conf. Pattern Recognit.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/60D623CA-CC4F-4E37-A1AD-C0A494ADFA16.pdf:pdf},
isbn = {0-7695-2128-2},
month = {jan},
pages = {32--36 Vol.3},
title = {{Recognizing Human Actions - A Local SVM Approach.}},
year = {2004}
}
@article{Atencio2008,
abstract = {Cortical receptive fields represent the signal preferences of sensory neurons. Receptive fields are thought to provide a representation of sensory experience from which the cerebral cortex may make interpretations. While it is essential to determine a neuron's receptive field, it remains unclear which features of the acoustic environment are specifically represented by neurons in the primary auditory cortex (AI). We characterized cat AI spectrotemporal receptive fields (STRFs) by finding both the spike-triggered average (STA) and stimulus dimensions that maximized the mutual information between response and stimulus. We derived a nonlinearity relating spiking to stimulus projection onto two maximally informative dimensions (MIDs). The STA was highly correlated with the first MID. Generally, the nonlinearity for the first MID was asymmetric and often monotonic in shape, while the second MID nonlinearity was symmetric and nonmonotonic. The joint nonlinearity for both MIDs revealed that most first and second MIDs were synergistic and thus should be considered conjointly. The difference between the nonlinearities suggests different possible roles for the MIDs in auditory processing.},
address = {University of California, San Francisco/University of California, Berkeley Bioengineering Graduate Group, University of California, San Francisco, San Francisco, CA 94143, USA.},
author = {Atencio, Craig A and Sharpee, Tatyana O and Schreiner, Christoph E},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neuron 2008 Atencio.pdf:pdf},
journal = {Neuron},
month = {jun},
number = {6},
pages = {956--966},
title = {{Cooperative nonlinearities in auditory cortical neurons.}},
volume = {58},
year = {2008}
}
@article{Hamrick2019,
abstract = {Mental simulation — the capacity to imagine what will or what could be — is a salient feature of human cognition, playing a key role in a wide range of cognitive abilities. In artificial intelligence, the last few years have seen the development of methods which are analogous to mental models and mental simulation. This paper outlines recent methods in deep learning for constructing such models from data and learning to use them via reinforcement learning, and compares such approaches to human mental simulation. Model-based methods in deep learning can serve as powerful tools for building and scaling cognitive models. However, a number of challenges remain in matching the capacity of human mental simulation for efficiency, compositionality, generalization, and creativity.},
author = {Hamrick, Jessica B.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/1-s2.0-S2352154618301670-main.pdf:pdf},
journal = {Curr. Opin. Behav. Sci.},
pages = {8--16},
title = {{Analogues of mental simulation and imagination in deep learning}},
volume = {29},
year = {2019}
}
@inproceedings{LeCun2004a,
abstract = {Abstract We assess the applicability of several popular learning methods for the problem of recognizing generic visual categories with invariance to pose , lighting , and surrounding clutter. A large dataset comprising stereo image pairs of 50 uniform-colored toys under 36 ... 
},
author = {LeCun, Y and Huang, Fu Jie and Bottou, L},
booktitle = {Proc. 2004 IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognition, 2004. CVPR 2004.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2004. CVPR 2004. 2004 LeCun.pdf:pdf},
isbn = {1063-6919},
month = {jan},
pages = {97--104},
title = {{Learning methods for generic object recognition with invariance to pose and lighting}},
volume = {2},
year = {2004}
}
@article{Yang2019a,
abstract = {The brain has the ability to flexibly perform many tasks, but the underlying mechanism cannot be elucidated in traditional experimental and modeling studies designed for one task at a time. Here, we trained single network models to perform 20 cognitive tasks that depend on working memory, decision making, categorization, and inhibitory control. We found that after training, recurrent units can develop into clusters that are functionally specialized for different cognitive processes, and we introduce a simple yet effective measure to quantify relationships between single-unit neural representations of tasks. Learning often gives rise to compositionality of task representations, a critical feature for cognitive flexibility, whereby one task can be performed by recombining instructions for other tasks. Finally, networks developed mixed task selectivity similar to recorded prefrontal neurons after learning multiple tasks sequentially with a continual-learning technique. This work provides a computational platform to investigate neural representations of many cognitive tasks.},
author = {Yang, Guangyu Robert and Joglekar, Madhura R. and Song, H. Francis and Newsome, William T. and Wang, Xiao Jing},
file = {:Users/hahosoya/Documents/Mendeley Desktop/s41593-018-0310-2.pdf:pdf},
journal = {Nat. Neurosci.},
number = {2},
pages = {297--306},
pmid = {30643294},
title = {{Task representations in neural networks trained to perform many cognitive tasks}},
volume = {22},
year = {2019}
}
@article{Merolla2014,
abstract = {Inspired by the brain's structure, we have developed an efficient, scalable, and flexible non-von Neumann architecture that leverages contemporary silicon technology. To demonstrate, we built a 5.4-billion-transistor chip with 4096 neurosynaptic cores interconnected via an intrachip network that integrates 1 million programmable spiking neurons and 256 million configurable synapses. Chips can be tiled in two dimensions via an interchip communication interface, seamlessly scaling the architecture to a cortexlike sheet of arbitrary size. The architecture is well suited to many applications that use complex neural networks in real time, for example, multiobject detection and classification. With 400-pixel-by-240-pixel video input at 30 frames per second, the chip consumes 63 milliwatts.},
address = {IBM Research-Almaden, 650 Harry Road, San Jose, CA 95120, USA.},
author = {Merolla, Paul A and Arthur, John V and Alvarez-Icaza, Rodrigo and Cassidy, Andrew S and Sawada, Jun and Akopyan, Filipp and Jackson, Bryan L and Imam, Nabil and Guo, Chen and Nakamura, Yutaka and Brezzo, Bernard and Vo, Ivan and Esser, Steven K and Appuswamy, Rathinakumar and Taba, Brian and Amir, Arnon and Flickner, Myron D and Risk, William P and Manohar, Rajit and Modha, Dharmendra S},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Science 2014 Merolla.pdf:pdf},
journal = {Science},
month = {aug},
number = {6197},
pages = {668--673},
title = {{Artificial brains. A million spiking-neuron integrated circuit with a scalable communication network and interface.}},
volume = {345},
year = {2014}
}
@article{Karklin2003,
abstract = {The theoretical principles that underlie the representation and computation of higher-order structure in natural images are poorly understood. Recently, there has been considerable interest in using information theoretic techniques, such as independent component analysis, to derive representations for natural images that are optimal in the sense of coding efficiency. Although these approaches have been successful in explaining properties of neural representations in the early visual pathway and visual cortex, because they are based on a linear model, the types of image structure that can be represented are very limited. Here, we present a hierarchical probabilistic model for learning higher-order statistical regularities in natural images. This non-linear model learns an efficient code that describes variations in the underlying probabilistic density. When applied to natural images the algorithm yields coarse-coded, sparse-distributed representations of abstract image properties such as object location, scale and texture. This model offers a novel description of higher-order image structure and could provide theoretical insight into the response properties and computational functions of lower level cortical visual areas.},
address = {Computer Science Department and Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PA 15213, USA.},
author = {Karklin, Yan and Lewicki, Michael S},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Network Computation in Neural Systems 2003 Karklin.pdf:pdf},
journal = {Netw. Comput. Neural Syst.},
month = {aug},
number = {3},
pages = {483--499},
title = {{Learning higher-order structures in natural images.}},
volume = {14},
year = {2003}
}
@article{Zhao2003,
abstract = {Abstract As one of the most successful applications of image analysis and understanding, face recognition has recently received significant attention, especially during the past several years. At least two reasons account for this trend: the first is the wide range of ... 
},
author = {Zhao, W and Chellappa, R and Phillips, P J and Rosenfeld, A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/ACM Comput. Surv. 2003 Zhao.pdf:pdf},
journal = {ACM Comput. Surv.},
month = {dec},
number = {4},
pages = {399--458},
title = {{Face recognition}},
volume = {35},
year = {2003}
}
@article{Zetzsche2001,
abstract = {The neural mechanisms of early vision can be explained in terms of an information-theoretic optimization of the neural processing with respect to the statistical properties of the natural environment. Recent applications of this approach have been successful in the prediction of the linear filtering properties of ganglion cells and simple cells, but the relations between the environmental statistics and cortical nonlinearities, like those of end-stopped or complex cells, are not yet fully understood. Here we present extensions of our previous investigations of the exploitation of higher-order statistics by nonlinear neurons. We use multivariate wavelet statistics to demonstrate that a strictly linear processing would inevitably leave substantial statistical dependencies between the outputs of the units. We then consider how the basic nonlinearities of cortical neurons - gain control and ON/OFF half-wave rectification - can exploit these higher-order statistical dependencies. We first show that gain contro...},
author = {Zetzsche, C and Rhrbein, F},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Network Computation in Neural Systems 2001 Zetzsche.pdf:pdf},
journal = {Netw. Comput. Neural Syst.},
month = {jan},
number = {3},
pages = {331--350},
title = {{Nonlinear and extra-classical receptive field properties and the statistics of natural scenes}},
volume = {12},
year = {2001}
}
@article{Leopold1999,
author = {Leopold, David A and Logothetis, Nikos K},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Trends in cognitive Sciences 1999 Leopold.pdf:pdf},
journal = {Trends Cogn. Sci.},
month = {jan},
number = {7},
pages = {254--264},
title = {{Multistable phenomena: changing views in perception}},
volume = {3},
year = {1999}
}
@article{Dai2017,
author = {Dai, Hang and Pears, Nick and Smith, William and Duncan, Christian},
file = {:Users/hahosoya/Documents/Mendeley Desktop/5F650CF6-4278-46EF-905F-E8C9ECF7384E.pdf:pdf},
journal = {ICCV},
month = {jan},
pages = {3104--3112},
title = {{A 3D Morphable Model of Craniofacial Shape and Texture Variation.}},
year = {2017}
}
@article{Honkela2005,
author = {Honkela, A and Valpola, H},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2005 Honkela.pdf:pdf},
month = {jan},
title = {{Unsupervised variational Bayesian learning of nonlinear models}},
year = {2005}
}
@article{Atkins1998,
abstract = {In applications ranging from DNA sequencing through archeological dating to sparse matrix reordering, a recurrent problem is the sequencing of elements in such a way that highly correlated pairs of elements are near each other. That is, given a correlation function f ...
},
author = {Atkins, J E and Boman, E G and Hendrickson, B},
file = {:Users/hahosoya/Documents/Mendeley Desktop/1998 Atkins.pdf:pdf},
journal = {SIAM J. Comput.},
month = {jan},
title = {{A spectral algorithm for seriation and the consecutive ones problem}},
year = {1998}
}
@article{Locatello2020a,
abstract = {Learning object-centric representations of complex scenes is a promising step towards enabling efficient abstract reasoning from low-level perceptual features. Yet, most deep learning approaches learn distributed representations that do not capture the compositional properties of natural scenes. In this paper, we present the Slot Attention module, an architectural component that interfaces with perceptual representations such as the output of a convolutional neural network and produces a set of task-dependent abstract representations which we call slots. These slots are exchangeable and can bind to any object in the input by specializing through a competitive procedure over multiple rounds of attention. We empirically demonstrate that Slot Attention can extract object-centric representations that enable generalization to unseen compositions when trained on unsupervised object discovery and supervised property prediction tasks.},
archivePrefix = {arXiv},
arxivId = {2006.15055},
author = {Locatello, Francesco and Weissenborn, Dirk and Unterthiner, Thomas and Mahendran, Aravindh and Heigold, Georg and Uszkoreit, Jakob and Dosovitskiy, Alexey and Kipf, Thomas},
eprint = {2006.15055},
file = {:Users/hahosoya/Documents/Mendeley Desktop/5156381d63bb3e873533b08f203cb56c2d79b6c9.pdf:pdf},
pages = {1--24},
title = {{Object-Centric Learning with Slot Attention}},
year = {2020}
}
@article{Xie2016a,
abstract = {Abstract We show that a generative random field model, which we call generative ConvNet, can be derived from the commonly used discriminative ConvNet, by assuming a ConvNet for multicategory classification and assuming one of the categories is a base category generated by a reference distribution. If we further assume that the non-linearity in the ConvNet is Rectified Linear Unit (ReLU) and the reference distribution is Gaussian white ...
},
author = {Xie, J and Lu, Y and Zhu, S C and Wu, Y N},
file = {:Users/hahosoya/Documents/Mendeley Desktop/arXiv 2016 Xie.pdf:pdf},
journal = {arXiv.org},
month = {jan},
title = {{A theory of generative convnet}},
year = {2016}
}
@article{OHerron2013,
author = {O'Herron, P and von der Heydt, R},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2013 O'Herron.pdf:pdf},
journal = {J. Neurosci.},
month = {jan},
number = {5},
pages = {1964--1974},
title = {{Remapping of Border Ownership in the Visual Cortex}},
volume = {33},
year = {2013}
}
@article{Nardini2010,
abstract = {Abstract Human adults can go beyond the limits of individual sensory systems' resolutions by integrating multiple estimates (eg, vision and touch) to reduce uncertainty. Little is known about how this ability develops. Although some multisensory abilities are present from  ...},
author = {Nardini, M and Bedford, R and Mareschal, D},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proceedings of the National Academy of Sciences USA 2010 Nardini.pdf:pdf},
journal = {Proc. Natl. Acad. Sci. USA},
month = {jan},
pages = {17041--17046},
title = {{Fusion of visual cues is not mandatory in children}},
volume = {107},
year = {2010}
}
@article{Carreira-Perpinan2005,
abstract = {Maximum-likelihood (ML) learning of Markov random fields is challenging because it requires 
estimates of averages that have an exponential number of terms. Markov chain Monte Carlo
methods typically take a long time to converge on unbiased estimates, but Hinton (2002)  ...},
author = {Carreira-Perpinan, Miguel and Hinton, Geoffrey E},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Artificial Intelligence and Statistics 2005 Carreira-Perpinan.pdf:pdf},
journal = {Artif. Intell. Stat.},
month = {jan},
title = {{On contrastive divergence learning}},
year = {2005}
}
@article{Mnih2016,
abstract = {Nature 518, 529 (2015). doi:10.1038/nature14236},
author = {Mnih, Volodymyr and Kavukcuoglu, Koray and Silver, David and Rusu, Andrei A and Veness, Joel and Bellemare, Marc G and Graves, Alex and Riedmiller, Martin and Fidjeland, Andreas K and Ostrovski, Georg and Petersen, Stig and Beattie, Charles and Sadik, Amir and Antonoglou, Ioannis and King, Helen and Kumaran, Dharshan and Wierstra, Daan and Legg, Shane and Hassabis, Demis},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature 2016 Mnih.pdf:pdf},
journal = {Nature},
month = {apr},
number = {7540},
pages = {529--533},
title = {{Human-level control through deep reinforcement learning}},
volume = {518},
year = {2016}
}
@article{Arcizet2008,
abstract = {Natural texture of an object is an important cue for recognition. In real conditions, the incidence angle of light on natural textures leads to a complex pattern of micro-shading that modifies 3D rendering of surfaces. Little is known about visual processing of material properties. The present work aims to study the coding of natural textures by the neurons of area V4 of the awake macaque monkey. We used patches of natural textures issued from the CURET database and illuminated with two or three different angles with their corresponding controls (scrambled Fourier phase). We recorded the responses of V4 neurons to stimuli flashed in their receptive fields (RFs) while the macaques performed a simple fixation task. We show that a large majority of V4 neurons responded to texture patches with a strong modulation across stimuli. The analysis of those responses indicate that V4 neurons integrate first and second order parameters in the image (mean luminance, SNR, and energy), which may be used to achieve texture clustering in a multidimensional space. This clustering was comparable to that of a pyramid of Gabor filters and was not affected by illumination angles. Altogether, these results suggest that the V4 neuronal population acts as a set of filters able to classify textures independently of illumination angle. We conclude that area V4 contains mechanisms that are sensitive to the aspect of textured surfaces, even in an environment where illumination changes continuously.},
address = {Facult{\'{e}} de M{\'{e}}decine de Rangueil, Universit{\'{e}} de Toulouse, CerCo, UPS, CNRS, UMR5549, 31062 Toulouse, France. farcizet@mednet.ucla.edu},
author = {Arcizet, F and Jouffrais, C and Girard, P},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Exp Brain Res 2008 Arcizet.pdf:pdf},
journal = {Exp. brain Res.},
month = {may},
number = {1},
pages = {109--120},
title = {{Natural textures classification in area V4 of the macaque monkey}},
volume = {189},
year = {2008}
}
@article{Jiang2006a,
abstract = {Understanding the neural mechanisms underlying object recognition is one of the fundamental challenges of visual neuroscience. While neurophysiology experiments have provided evidence for a "simple-to-complex" processing model based on a hierarchy of increasingly complex image features, behavioral and fMRI studies of face processing have been interpreted as incompatible with this account. We present a neurophysiologically plausible, feature-based model that quantitatively accounts for face discrimination characteristics, including face inversion and "configural" effects. The model predicts that face discrimination is based on a sparse representation of units selective for face shapes, without the need to postulate additional, "face-specific" mechanisms. We derive and test predictions that quantitatively link model FFA face neuron tuning, neural adaptation measured in an fMRI rapid adaptation paradigm, and face discrimination performance. The experimental data are in excellent agreement with the model prediction that discrimination performance should asymptote as faces become dissimilar enough to activate different neuronal populations.},
address = {Department of Neuroscience, Georgetown University Medical Center, Washington, District of Columbia 20007, USA.},
author = {Jiang, Xiong and Rosen, Ezra and Zeffiro, Thomas and Vanmeter, John and Blanz, Volker and Riesenhuber, Maximilian},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neuron 2006 Jiang.pdf:pdf},
journal = {Neuron},
month = {apr},
number = {1},
pages = {159--172},
title = {{Evaluation of a shape-based model of human face discrimination using FMRI and behavioral techniques.}},
volume = {50},
year = {2006}
}
@article{Lake2018,
abstract = {Humans can understand and produce new utterances effortlessly, thanks to their compositional skills. Once a person learns the meaning of a new verb "dax," he or she can immediately understand the meaning of "dax twice" or "sing and dax." In this paper, we introduce the SCAN domain, consisting of a set of simple compositional navigation commands paired with the corresponding action sequences. We then test the zero-shot generalization capabilities of a variety of recurrent neural networks (RNNs) trained on SCAN with sequence-to-sequence methods. We find that RNNs can make successful zero-shot generaliza-tions when the differences between training and test commands are small, so that they can apply "mix-and-match" strategies to solve the task. However, when generalization requires systematic compositional skills (as in the "dax" example above), RNNs fail spectacularly. We conclude with a proof-of-concept experiment in neural machine translation, suggesting that lack of systematicity might be partially responsible for neural networks' notorious training data thirst.},
archivePrefix = {arXiv},
arxivId = {1711.00350},
author = {Lake, Brenden and Baroni, Marco},
eprint = {1711.00350},
file = {:Users/hahosoya/Documents/Mendeley Desktop/5e0c749bdeb2daf20857b5e145da04e2acf2cc66.pdf:pdf},
isbn = {9781510867963},
journal = {35th Int. Conf. Mach. Learn. ICML 2018},
pages = {4487--4499},
title = {{Generalization without systematicity: On the compositional skills of sequence-to-sequence recurrent networks}},
volume = {7},
year = {2018}
}
@article{Chang2017a,
abstract = {An important question about color vision is how does the brain represent the color of an object? The recent discovery of "color patches" in macaque inferotemporal (IT) cortex, the part of the brain responsible for object recognition, makes this problem experimentally tractable. Here we recorded neurons in three color patches, middle color patch CLC (central lateral color patch), and two anterior color patches ALC (anterior lateral color patch) and AMC (anterior medial color patch), while presenting images of objects systematically varied in hue. We found that all three patches contain high concentrations of hue-selective cells, and that the three patches use distinct computational strategies to represent colored objects: while all three patches multiplex hue and shape information, shape-invariant hue information is much stronger in anterior color patches ALC/AMC than CLC. Furthermore, hue and object shape specifically for primate faces/bodies are over-represented in AMC, but not in the other two patches.},
author = {Chang, Le and Bao, Pinglei and Tsao, Doris Y.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/s41467-017-01912-7.pdf:pdf},
journal = {Nat. Commun.},
number = {1},
pages = {1--14},
title = {{The representation of colored objects in macaque color patches}},
volume = {8},
year = {2017}
}
@article{Li2018,
author = {Li, Yingzhen and Mandt, Stephan},
file = {:Users/hahosoya/Documents/Mendeley Desktop/E85A6DE1-2686-4B38-B177-1AA410ABFA47.pdf:pdf},
journal = {ICML},
month = {jan},
title = {{Disentangled Sequential Autoencoder.}},
year = {2018}
}
@article{Nieh2021,
abstract = {Hippocampal neurons encode physical variables1–7 such as space1 or auditory frequency6 in cognitive maps8. In addition, functional magnetic resonance imaging studies in humans have shown that the hippocampus can also encode more abstract, learned variables9–11. However, their integration into existing neural representations of physical variables12,13 is unknown. Here, using two-photon calcium imaging, we show that individual neurons in the dorsal hippocampus jointly encode accumulated evidence with spatial position in mice performing a decision-making task in virtual reality14–16. Nonlinear dimensionality reduction13 showed that population activity was well-described by approximately four to six latent variables, which suggests that neural activity is constrained to a low-dimensional manifold. Within this low-dimensional space, both physical and abstract variables were jointly mapped in an orderly manner, creating a geometric representation that we show is similar across mice. The existence of conjoined cognitive maps suggests that the hippocampus performs a general computation—the creation of task-specific low-dimensional manifolds that contain a geometric representation of learned knowledge.},
author = {Nieh, Edward H. and Schottdorf, Manuel and Freeman, Nicolas W. and Low, Ryan J. and Lewallen, Sam and Koay, Sue Ann and Pinto, Lucas and Gauthier, Jeffrey L. and Brody, Carlos D. and Tank, David W.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/s41586-021-03652-7.pdf:pdf},
journal = {Nature},
number = {7865},
pages = {80--84},
pmid = {34135512},
title = {{Geometry of abstract learned knowledge in the hippocampus}},
volume = {595},
year = {2021}
}
@article{Denton2017,
abstract = {We present a new model DrNET that learns disentangled image representations from video. Our approach leverages the temporal coherence of video and a novel adversarial loss to learn a representation that factorizes each frame into a stationary part and a temporally varying component. The disentangled representation can be used for a range of tasks. For example, applying a standard LSTM to the time-vary components enables prediction of future frames. We evaluate our approach on a range of synthetic and real videos, demonstrating the ability to coherently generate hundreds of steps into the future.},
author = {Denton, Emily and Birodkar, Vighnesh},
file = {:Users/hahosoya/Documents/Mendeley Desktop/EEC1E42D-3F82-4119-93E7-80CA9A79B537.pdf:pdf},
journal = {Adv. Neural Inf. Process. Syst.},
month = {may},
title = {{Unsupervised Learning of Disentangled Representations from Video}},
year = {2017}
}
@article{Bobier2014,
author = {Bobier, Bruce and Stewart, Terrence C and Eliasmith, Chris},
editor = {Einh{\"{a}}user, Wolfgang},
file = {:Users/hahosoya/Documents/Mendeley Desktop/PLoS Comp Biol 2014 Bobier.pdf:pdf},
journal = {PLoS Comput. Biol.},
month = {jun},
number = {6},
pages = {e1003577},
title = {{A Unifying Mechanistic Model of Selective Attention in Spiking Neurons}},
volume = {10},
year = {2014}
}
@article{Sawangjit2018,
abstract = {Nature, doi:10.1038/s41586-018-0716-8},
author = {Sawangjit, Anuck and Oyanedel, Carlos N and Niethard, Niels and Salazar, Carolina and Born, Jan and Inostroza, Marion},
file = {:Users/hahosoya/Documents/Mendeley Desktop/D66A6029-5BF8-4687-ABF6-95BEAFBD0B44.pdf:pdf},
journal = {Nature},
month = {nov},
number = {7734},
pages = {1--18},
title = {{The hippocampus is crucial for forming non- hippocampal long-term memory during sleep}},
volume = {564},
year = {2018}
}
@article{Chen2017a,
abstract = {Neuron, Corrected proof. doi:10.1016/j.neuron.2017.11.004},
author = {Chen, Rujia and Wang, Feng and Liang, Hualou and Li, Wu},
file = {:Users/hahosoya/Documents/Mendeley Desktop/59CD2502-7A1F-4F73-A4A9-BCB6479F62A9.pdf:pdf},
journal = {Neuron},
month = {nov},
number = {6},
pages = {1--20},
title = {{Synergistic Processing of Visual Contours across Cortical Layers in V1 and V2}},
volume = {96},
year = {2017}
}
@article{Ringach2004,
abstract = {... 2002). A more efficient approach that is yielding interesting results is to study the spike - triggered covariance of the stimulus in response to spatio-temporal Gaussian white-noise. ... Figure 4. The spike - triggered  covariance method A, results from a complex cell in cat area 17. ...},
author = {Ringach, D L},
file = {:Users/hahosoya/Documents/Mendeley Desktop/The Journal of Physiology 2004 Ringach.pdf:pdf},
journal = {J. Physiol.},
month = {jun},
number = {3},
pages = {717--728},
title = {{Mapping receptive fields in primary visual cortex}},
volume = {558},
year = {2004}
}
@article{Smith2009,
author = {Smith, Stephen M and Fox, Peter T and Miller, Karla L and Glahn, David C and Fox, P Mickle and Mackay, Clare E and Filippini, Nicola and Watkins, Kate E and Toro, Roberto and Laird, Angela R},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proc. Natl. Acad. Sci. U.S.A. 2009 Smith.pdf:pdf},
journal = {Proc. Natl. Acad. Sci. U. S. A.},
month = {jan},
number = {31},
pages = {13040--13045},
title = {{Correspondence of the brain's functional architecture during activation and rest}},
volume = {106},
year = {2009}
}
@article{Salakhutdinov2007,
abstract = {Most of the existing approaches to collab- orative filtering cannot handle very large data 
sets. In this paper we show how a class of two-layer undirected graphical mod- els, called Restricted
Boltzmann Machines (RBM's), can be used to model tabular data, such as user's ratings  ...},
author = {Salakhutdinov, R and et Al},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proceedings of the 24th {\ldots} 2007 Salakhutdinov.pdf:pdf},
journal = {Proc. 24th  {\ldots}},
month = {jan},
title = {{Restricted Boltzmann machines for collaborative filtering}},
year = {2007}
}
@article{Kaiser2017a,
author = {Kaiser, {\L}ukasz and Roy, Aurko},
file = {:Users/hahosoya/Documents/Mendeley Desktop/71d3ff7a209d5683ea151c62b331cecb2e491f36.pdf:pdf},
journal = {ICLR},
pages = {1--10},
title = {{Learning To Remember Rare Events}},
year = {2017}
}
@article{Riesenhuber2009,
author = {Riesenhuber, Maximilian and Wolff, Brian S},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Acta Psychologica 2009 Riesenhuber.pdf:pdf},
journal = {Acta Psychol. (Amst).},
month = {nov},
number = {3},
pages = {286--292},
title = {{Task effects, performance levels, features, configurations, and holistic face processing: A reply to Rossion}},
volume = {132},
year = {2009}
}
@article{Gilbert1977,
abstract = {... In primary visual cortex, the afferents from each of the layers of the lateral geniculate 
nucleus have distinct laminar patterns of termination, both in the monkey ( Hubel & Wiesel , 1972) and in the cat (LeVay & Gilbert, 1976). Since ...
},
author = {Gilbert, C D},
file = {:Users/hahosoya/Documents/Mendeley Desktop/The Journal of Physiology 1977 Gilbert.pdf:pdf},
journal = {J. Physiol.},
month = {jan},
title = {{Laminar differences in receptive field properties of cells in cat primary visual cortex}},
year = {1977}
}
@article{Gerwinn2008,
abstract = {Abstract Generalized linear models are the most commonly used tools to describe the stimulus selectivity of sensory neurons . Here we present a Bayesian treatment of such models . Using the expectation propagation algorithm, we are able to approximate the full  ...},
author = {Gerwinn, S and Macke, J and et Al},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Advances in neural {\ldots} 2008 Gerwinn.pdf:pdf},
journal = {Adv. neural  {\ldots}},
month = {jan},
title = {{Bayesian inference for spiking neuron models with a sparsity prior}},
year = {2008}
}
@article{Sincich2005,
abstract = {Primary and secondary visual cortex (V1 and V2) form the foundation of the cortical visual system. V1 transforms information received from the lateral geniculate nucleus (LGN) and distributes it to separate domains in V2 for transmission to higher visual areas. During the  ...},
author = {Sincich, L C and Horton, J C},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Annu. Rev. Neurosci. 2005 Sincich.pdf:pdf},
journal = {Annu. Rev. Neurosci.},
month = {jan},
title = {{The circuitry of V1 and V2: integration of color, form, and motion}},
year = {2005}
}
@article{Sasaki2007,
author = {Sasaki, K S and Ohzawa, I},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neurophysiology 2007 Sasaki.pdf:pdf},
journal = {J. Neurophysiol.},
month = {sep},
number = {3},
pages = {1194--1212},
title = {{Internal Spatial Organization of Receptive Fields of Complex Cells in the Early Visual Cortex}},
volume = {98},
year = {2007}
}
@article{Snell2017,
author = {Snell, Jake and Swersky, Kevin and Zemel, Richard S},
file = {:Users/hahosoya/Documents/Mendeley Desktop/8DDC5ABE-9111-4981-83BF-D24554205C6A.pdf:pdf},
journal = {NIPS},
month = {jan},
title = {{Prototypical Networks for Few-shot Learning.}},
year = {2017}
}
@article{Pouget2013,
abstract = {... Journal name: Nature Neuroscience Year published: (2013) DOI: doi: 10.1038 / nn . 3495 Received 15 May 2013 Accepted 13 July 2013 Published online 18 August 2013 ... 
},
author = {Pouget, A and Beck, J M and Ma, W J and Latham, P E},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature neuroscience 2013 Pouget-1.pdf:pdf},
journal = {Nat. Neurosci.},
month = {jan},
title = {{Probabilistic brains: knowns and unknowns}},
year = {2013}
}
@article{Hosoya2013,
abstract = {Our visual system can robustly detect an important feature in a scene, such as the identity of an object, even when its retinal image can vary substantially. Neural basis for such invariant recognition is thought to be the common property in visual cortex where a cell retains its ... 
},
author = {Hosoya, H and Sasaki, K S and Ohzawa, I},
file = {:Users/hahosoya/Documents/Mendeley Desktop/BMC neuroscience 2013 Hosoya.pdf:pdf},
journal = {BMC Neurosci.},
month = {jan},
pages = {302},
title = {{BMC Neuroscience | Full text | Estimating invariant dimensions in V2}},
year = {2013}
}
@article{Pecevski2011,
author = {Pecevski, Dejan and Buesing, Lars and Maass, Wolfgang},
editor = {Sporns, Olaf},
file = {:Users/hahosoya/Documents/Mendeley Desktop/PLoS Comp Biol 2011 Pecevski.pdf:pdf},
journal = {PLoS Comput. Biol.},
month = {dec},
number = {12},
pages = {e1002294},
title = {{Probabilistic Inference in General Graphical Models through Sampling in Stochastic Networks of Spiking Neurons}},
volume = {7},
year = {2011}
}
@article{Silson2021,
abstract = {Behavioral and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam 1012 WX, The Netherlands, and 5Spinoza Centre for NeuroImaging, Royal Dutch Academy of Sciences 1012 WX, Amsterdam, The Netherlands The initial encoding of visual information primarily from the contralateral visual field is a fundamental organizing principle of the primate visual system. Recently, the presence of such retinotopic sensitivity has been shown to extend well beyond early visual cortex to regions not historically considered retinotopically sensitive. In particular, human scene-selective regions in parahippocampal and medial parietal cortex exhibit prominent biases for the contralateral visual field. Here, we used fMRI to test the hypothesis that the human hippocampus, which is thought to be anatomically connected with these scene-selective regions, would also exhibit a biased representation of contralateral visual space. First, population receptive field (pRF) mapping with scene stimuli revealed strong biases for the contralateral visual field in bilateral hippocampus. Second, the distribution of retinotopic sensitivity suggested a more prominent representation in anterior medial portions of the hippocampus. Finally, the contralateral bias was confirmed in independent data taken from the Human Connectome Project (HCP) initiative. The presence of contralateral biases in the hippocampus, a structure considered by many as the apex of the visual hierarchy, highlights the truly pervasive influence of retinotopy. Moreover, this finding has important implications for understanding how visual information relates to the allocentric global spatial representations known to be encoded therein.},
author = {Silson, Edward H. and Zeidman, Peter and Knapen, Tomas and Baker, Chris I.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2382.full.pdf:pdf},
journal = {J. Neurosci.},
keywords = {Contralateral,Fmri,Hippocampus,Retinotopy,Visual field biases},
number = {11},
pages = {2382--2392},
pmid = {33500275},
title = {{Representation of Contralateral Visual Space in the Human Hippocampus}},
volume = {41},
year = {2021}
}
@article{Morito2008,
abstract = {Perceptual experience consists of an enormous number of possible states. Previous fMRI studies 
have predicted a perceptual state by classifying brain activity into prespecified categories.
Constraint-free visual image reconstruction is more challenging, as it is impractical to  ...},
author = {Morito, Y and Tanabe, H C and Sadato, N and Kamitani, Y},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neuron 2008 Morito.pdf:pdf},
journal = {Neuron},
month = {jan},
title = {{Visual image reconstruction from human brain activity using a combination of multiscale local image decoders}},
year = {2008}
}
@article{Petkov1997,
abstract = {Page 1. Biol. Cybern. 76, 83–96 (1997) Biological Cybernetics c Springer-Verlag 1997 Computational  models of visual  neurons  specialised in the detection of periodic and aperiodic oriented  visual  stimuli : bar and grating  cells N. Petkov, P. Kruizinga ...},
author = {Petkov, N and Kruizinga, P},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Biological Cybernetics 1997 Petkov.pdf:pdf},
journal = {Biol. Cybern.},
month = {feb},
number = {2},
pages = {83--96},
title = {{Computational models of visual neurons specialised in the detection of periodic and aperiodic oriented visual stimuli: bar and grating cells}},
volume = {76},
year = {1997}
}
@article{Sharma2000,
abstract = {Abstract Modules of neurons sharing a common property are a basic organizational feature of mammalian sensory cortex. Primary visual cortex (V1) is characterized by orientation modules|[mdash]| groups of cells that share a preferred stimulus orientation|[mdash]|  ...},
author = {Sharma, J and Angelucci, A and Sur, M},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature 2000 Sharma.pdf:pdf},
journal = {Nature},
month = {jan},
title = {{Induction of visual orientation modules in auditory cortex}},
year = {2000}
}
@article{Einhauser2002,
author = {Einh{\"{a}}user, Wolfgang and Kayser, Christoph and K{\"{o}}nig, Peter and Kording, Konrad P},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Eur J Neurosci 2002 Einh{\"{a}}user.pdf:pdf},
journal = {Eur. J. Neurosci.},
month = {feb},
number = {3},
pages = {475--486},
title = {{Learning the invariance properties of complex cells from their responses to natural stimuli}},
volume = {15},
year = {2002}
}
@article{Livingstone2017,
abstract = {Nucleoporins have been reported to regulate pluripotent biology, but how they do so remains partially characterized. This study examined the effects of nup155 gene disruption on mouse embryonic stem cells to gain insights into possible mechanisms by which nucleoporins regulate pluripotency in a pro-arrhythmogenic stem cell line. Embryonic stem cells with gene-trapped nup155 exhibited aberrant colony morphology underscored by abnormal transcriptome remodeling. Bioinformatic analysis of whole transcriptome data from nup155+/− embryonic stem cells revealed changes in a variety of non-coding RNA elements, with significant under expression of miR291a, miR291b, miR293, and miR294. These miRNAs are members of the larger regulatory miR290–295 cluster that regulates pluripotency and are controlled by the canonical stem cell-related factors SOX2, OCT4, and NANOG. Expression analysis of these factors revealed downregulation in all three, supported by biochemical profiling and image analysis. These data implicate disruption of the miR-SOX2/OCT4/NANOG regulatory circuit occurs downstream of nup155 gene lesion.},
author = {Livingstone, Margaret S. and Vincent, Justin L. and Arcaro, Michael J. and Srihasam, Krishna and Schade, Peter F. and Savage, Tristram},
file = {:Users/hahosoya/Documents/Mendeley Desktop/801f42df5010665e1392612b2b80d7607d43c650.pdf:pdf},
journal = {Nat. Commun.},
month = {apr},
number = {1},
pages = {14897},
title = {{Development of the macaque face-patch system}},
volume = {8},
year = {2017}
}
@article{Murphy2013,
author = {Murphy, A P and Ban, H and Welchman, A E},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neurophysiology 2013 Murphy.pdf:pdf},
journal = {J. Neurophysiol.},
month = {jul},
number = {1},
pages = {190--203},
title = {{Integration of texture and disparity cues to surface slant in dorsal visual cortex}},
volume = {110},
year = {2013}
}
@article{Bahdanau2015,
abstract = {Neural machine translation is a recently proposed approach to machine translation. Unlike the traditional statistical machine translation, the neural machine translation aims at building a single neural network that can be jointly tuned to maximize the translation performance. The models proposed recently for neural machine translation often belong to a family of encoder–decoders and encode a source sentence into a fixed-length vector from which a decoder generates a translation. In this paper, we conjecture that the use of a fixed-length vector is a bottleneck in improving the performance of this basic encoder–decoder architecture, and propose to extend this by allowing a model to automatically (soft-)search for parts of a source sentence that are relevant to predicting a target word, without having to form these parts as a hard segment explicitly. With this new approach, we achieve a translation performance comparable to the existing state-of-the-art phrase-based system on the task of English-to-French translation. Furthermore, qualitative analysis reveals that the (soft-)alignments found by the model agree well with our intuition.},
archivePrefix = {arXiv},
arxivId = {1409.0473},
author = {Bahdanau, Dzmitry and Cho, Kyung Hyun and Bengio, Yoshua},
eprint = {1409.0473},
file = {:Users/hahosoya/Documents/Mendeley Desktop/071b16f25117fb6133480c6259227d54fc2a5ea0.pdf:pdf},
journal = {3rd Int. Conf. Learn. Represent. ICLR 2015 - Conf. Track Proc.},
pages = {1--15},
title = {{Neural machine translation by jointly learning to align and translate}},
year = {2015}
}
@article{Neafsey1986,
abstract = {... 49. The pres- ent study was undertaken to provide a complete, de- tailed stimulation 
map of the rat ' motor  cortex ' corre- lated with cortical cytoarchitectonic and functional
borders. 2. MATERIALS AND METHODS 2.1. Animal ...
},
author = {Neafsey, E J and Bold, E L and et Al},
journal = {Brain Res.  {\ldots}},
month = {jan},
title = {{The organization of the rat motor cortex: a microstimulation mapping study}},
year = {1986}
}
@article{Angelucci2003,
abstract = {It is commonly assumed that the orientation-selective surround field of neurons in primary visual 
cortex (V1) is due to interactions provided solely by intrinsic long-range horizontal
connections. We review evidence for and against this proposition and conclude that  ...},
author = {Angelucci, A and Bullier, J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Physiology-Paris 2003 Angelucci.pdf:pdf},
journal = {J. Physiol.},
month = {jan},
title = {{Reaching beyond the classical receptive field of V1 neurons: horizontal or feedback axons?}},
year = {2003}
}
@article{Hyvarinen2005,
author = {Hyv{\"{a}}rinen, Aapo},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2005 Hyv{\"{a}}rinen.pdf:pdf},
journal = {J. Mach. Learn. Res.},
month = {apr},
pages = {695--709},
title = {{Estimation of Non-Normalized Statistical Models by Score Matching}},
volume = {6},
year = {2005}
}
@article{Chen2010,
abstract = {We present a novel hierarchical and distributed model for learning invariant spatio- temporal 
features from video . Our approach builds on previous deep learning methods and uses the Convolutional
Restricted Boltzmann machine (CRBM) as a building block. Our model, called the Space- ...},
author = {Chen, B},
file = {:Users/hahosoya/Documents/Mendeley Desktop/circle.ubc.ca 2010 Chen.pdf:pdf},
journal = {circle.ubc.ca},
month = {jan},
title = {{Deep learning of invariant spatio-temporal features from video}},
year = {2010}
}
@article{Verhoef2015,
author = {Verhoef, B E and Bohon, K S and Conway, B R},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2015 Verhoef.pdf:pdf},
journal = {J. Neurosci.},
month = {apr},
number = {17},
pages = {6952--6968},
title = {{Functional Architecture for Disparity in Macaque Inferior Temporal Cortex and Its Relationship to the Architecture for Faces, Color, Scenes, and Visual Field}},
volume = {35},
year = {2015}
}
@article{Bao2020,
abstract = {The inferotemporal (IT) cortex is responsible for object recognition, but it is unclear how the representation of visual objects is organized in this part of the brain. Areas that are selective for categories such as faces, bodies, and scenes have been found1–5, but large parts of IT cortex lack any known specialization, raising the question of what general principle governs IT organization. Here we used functional MRI, microstimulation, electrophysiology, and deep networks to investigate the organization of macaque IT cortex. We built a low-dimensional object space to describe general objects using a feedforward deep neural network trained on object classification6. Responses of IT cells to a large set of objects revealed that single IT cells project incoming objects onto specific axes of this space. Anatomically, cells were clustered into four networks according to the first two components of their preferred axes, forming a map of object space. This map was repeated across three hierarchical stages of increasing view invariance, and cells that comprised these maps collectively harboured sufficient coding capacity to approximately reconstruct objects. These results provide a unified picture of IT organization in which category-selective regions are part of a coarse map of object space whose dimensions can be extracted from a deep network.},
author = {Bao, Pinglei and She, Liang and McGill, Mason and Tsao, Doris Y.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/s41586-020-2350-5.pdf:pdf},
journal = {Nature},
month = {jul},
number = {7814},
pages = {103--108},
title = {{A map of object space in primate inferotemporal cortex}},
volume = {583},
year = {2020}
}
@article{DiOleggioCastello2021,
abstract = {Processes evoked by seeing a personally familiar face encompass recognition of visual appearance and activation of social and person knowledge. Whereas visual appearance is the same for all viewers, social and person knowledge may be more idiosyncratic. Using between-subject multivariate decoding of hyperaligned functional magnetic resonance imaging data, we investigated whether representations of personally familiar faces in different parts of the distributed neural system for face perception are shared across individuals who know the same people. We found that the identities of both personally familiar and merely visually familiar faces were decoded accurately across brains in the core system for visual processing, but only the identities of personally familiar faces could be decoded across brains in the extended system for processing nonvisual information associated with faces. Our results show that personal interactions with the same individuals lead to shared neural representations of both the seen and unseen features that distinguish their identities.},
author = {{Di Oleggio Castello}, Matteo Visconti and Haxby, James V. and {Ida Gobbini}, M.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/e2110474118.full.pdf:pdf},
journal = {Proc. Natl. Acad. Sci. U. S. A.},
keywords = {Brain decoding,Face recognition,Familiar face processing,Person knowledge,Social perception},
number = {45},
pages = {1--8},
pmid = {34732577},
title = {{Shared neural codes for visual and semantic information about familiar faces in a common representational space}},
volume = {118},
year = {2021}
}
@article{Liu2017,
abstract = {Infants understand that people pursue goals, but how do they learn which goals people prefer? We tested whether infants solve this problem by inverting a mental model of action planning, trading off the costs of acting against the rewards actions bring. After seeing an agent attain two goals equally often at varying costs, infants expected the agent to prefer the goal it attained through costlier actions. These expectations held across three experiments that conveyed cost through different physical path features (height, width, and incline angle), suggesting that an abstract variable-such as "force," "work," or "effort"-supported infants' inferences. We modeled infants' expectations as Bayesian inferences over utility-theoretic calculations, providing a bridge to recent quantitative accounts of action understanding in older children and adults.},
address = {Department of Psychology, Harvard University, Cambridge, MA, USA. shariliu01@g.harvard.edu.},
author = {Liu, Shari and Ullman, Tomer D and Tenenbaum, Joshua B and Spelke, Elizabeth S},
file = {:Users/hahosoya/Documents/Mendeley Desktop/92EB5E3A-F374-4B55-B28C-DEB2AB6F980B.pdf:pdf},
journal = {Science},
month = {nov},
number = {6366},
pages = {1038--1041},
title = {{Ten-month-old infants infer the value of goals from the costs of actions.}},
volume = {358},
year = {2017}
}
@article{VanEssen2007,
abstract = {We compared aspects of shape representation in extrastriate visual areas V2 and V4, which are both implicated in shape processing and belong to different hierarchical levels. We recorded responses of cells in awake, fixating monkeys to matched sets of contour and grating ...},
author = {{Van Essen}, D C},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Cerebral Cortex 2007 Van Essen.pdf:pdf},
journal = {Cereb. Cortex},
month = {jan},
title = {{A Comparative Study of Shape Representation in Macaque Visual Areas V2 and V4}},
year = {2007}
}
@article{Skottun1991,
abstract = {Hubel and Wiesel (1962; Journal of Physiology, London, 160, 106–154) introduced the classification 
of cortical neurons as simple and complex on the basis of four tests of their receptive field
structure. These tests are partly subjective and no one of them unequivocally places  ...},
author = {Skottun, Bernt C and {De Valois}, Russell L and Grosof, David H and Movshon, J Anthony and Albrecht, Duane G and Bonds, A B},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Vision Research 1991 Skottun.pdf:pdf},
journal = {Vision Res.},
month = {jan},
number = {7-8},
pages = {1078--1086},
title = {{Classifying simple and complex cells on the basis of response modulation}},
volume = {31},
year = {1991}
}
@article{Rasmus2015,
abstract = {Abstract We combine supervised learning with unsupervised learning in deep neural networks. The proposed model is trained to simultaneously minimize the sum of supervised and unsupervised cost functions by backpropagation, avoiding the need for layer-wise pre- training. Our work builds on top of the Ladder network proposed by Valpola (2015) which we extend by combining the model with supervision. We show that the resulting model ...
},
author = {Rasmus, A and Berglund, M and Honkala, M},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Advances in neural {\ldots} 2015 Rasmus.pdf:pdf},
journal = {Adv. neural  {\ldots}},
month = {jan},
title = {{Semi-supervised learning with ladder networks}},
year = {2015}
}
@article{Portilla2000,
author = {Portilla, Javier and Simoncelli, Eero P},
file = {:Users/hahosoya/Documents/Mendeley Desktop/International journal on machine learning 2000 Portilla.pdf:pdf},
journal = {Int. J. Comput. Vis.},
month = {jan},
number = {1},
pages = {49--70},
title = {{A parametric texture model based on joint statistics of complex wavelet coefficients}},
volume = {40},
year = {2000}
}
@article{Cox2008,
author = {Cox, D D and DiCarlo, J J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/8A1B5E56-212A-492B-BB09-14EF82777261.pdf:pdf},
journal = {J. Neurosci.},
month = {oct},
number = {40},
pages = {10045--10055},
title = {{Does Learned Shape Selectivity in Inferior Temporal Cortex Automatically Generalize Across Retinal Position?}},
volume = {28},
year = {2008}
}
@article{Connor1997,
abstract = {Focal visual attention typically produces enhanced perceptual processing at the psychological level and relatively stronger neural responses at the physiological level. A longstanding mechanistic question is whether these attentional effects pertain specifically to the attended (target) object or to the region of space it occupies. We show here that attentional response enhancement in macaque area V4 extends to behaviorally irrelevant objects in the vicinity of the target object, indicating that focal attention has a strong spatial component at the physiological level. In addition, we find that spatial attention effects typically show a striking directional asymmetry. The direction of the asymmetry varies between cells, so that some cells respond best when attention is directed to the left of the stimulus, some when attention is directed to the right, etc. Thus, attention involves not only enhanced responses to behavioral targets but also a complex modulation of responses to other stimuli in the surrounding visual space.},
address = {Department of Anatomy and Neurobiology, Washington University School of Medicine, St. Louis, Missouri 63110, USA.},
author = {Connor, C E and Preddie, D C and Gallant, J L and {Van Essen}, D C},
file = {:Users/hahosoya/Documents/Mendeley Desktop/The Journal of neuroscience 1997 Connor.pdf:pdf},
journal = {J. Neurosci.},
month = {may},
number = {9},
pages = {3201--3214},
title = {{Spatial attention effects in macaque area V4.}},
volume = {17},
year = {1997}
}
@article{Pernice2018,
author = {Pernice, Volker and da Silveira, Rava Azeredo},
editor = {Doiron, Brent},
file = {:Users/hahosoya/Documents/Mendeley Desktop/C1AB0F42-4BF3-4E1D-A60D-1F8050F4CDCF.pdf:pdf},
journal = {PLoS Comput. Biol.},
month = {feb},
number = {2},
pages = {e1005979--26},
title = {{Interpretation of correlated neural variability from models of feed-forward and recurrent circuits}},
volume = {14},
year = {2018}
}
@article{Jaderberg2015,
abstract = {Abstract Convolutional Neural Networks define an exceptionallypowerful class of model, but are still limited by the lack of abilityto be spatially invariant to the input data in a computationally and parameterefficient manner. In this work we introduce a new learnable 
},
author = {Jaderberg, M and Simonyan, K and Zisserman, A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Advances in neural {\ldots} 2015 Jaderberg.pdf:pdf},
journal = {Adv. Neural Inf. Process. Syst.},
month = {jan},
title = {{Spatial transformer networks}},
year = {2015}
}
@article{Gray1989a,
abstract = {In areas 17 and 18 of the cat visual cortex the firing probability of neurons, in response to the presentation of optimally aligned light bars within their receptive field, oscillates with a peak frequency near 40 Hz. The neuronal firing pattern is tightly correlated with the phase and amplitude of an oscillatory local field potential recorded through the same electrode. The amplitude of the local field-potential oscillations are maximal in response to stimuli that match the orientation and direction preference of the local cluster of neurons. Single and multiunit recordings from the dorsal lateral geniculate nucleus of the thalamus showed no evidence of oscillations of the neuronal firing probability in the range of 20-70 Hz. The results demonstrate that local neuronal populations in the visual cortex engage in stimulus-specific synchronous oscillations resulting from an intracortical mechanism. The oscillatory responses may provide a general mechanism by which activity patterns in spatially separate regions of the cortex are temporally coordinated.},
address = {Max Planck Institute for Brain Research, Frankfurt am Main, Federal Republic of Germany.},
author = {Gray, C M and Singer, W},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proc. Natl. Acad. Sci. U.S.A. 1989 Gray.pdf:pdf},
journal = {Proc. Natl. Acad. Sci. U. S. A.},
month = {mar},
number = {5},
pages = {1698--1702},
title = {{Stimulus-specific neuronal oscillations in orientation columns of cat visual cortex.}},
volume = {86},
year = {1989}
}
@article{Plitt2021,
abstract = {The hippocampus contains neural representations capable of supporting declarative memory. Hippocampal place cells are one such representation, firing in one or few locations in a given environment. Between environments, place cell firing fields remap (turning on/off or moving to a new location) to provide a population-wide code for distinct contexts. However, the manner by which contextual features combine to drive hippocampal remapping remains a matter of debate. Using large-scale in vivo two-photon intracellular calcium recordings in mice during virtual navigation, we show that remapping in the hippocampal region CA1 is driven by prior experience regarding the frequency of certain contexts and that remapping approximates an optimal estimate of the identity of the current context. A simple associative-learning mechanism reproduces these results. Together, our findings demonstrate that place cell remapping allows an animal to simultaneously identify its physical location and optimally estimate the identity of the environment.},
author = {Plitt, Mark H. and Giocomo, Lisa M.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/s41593-021-00816-6.pdf:pdf},
journal = {Nat. Neurosci.},
number = {5},
pages = {705--714},
pmid = {33753945},
title = {{Experience-dependent contextual codes in the hippocampus}},
volume = {24},
year = {2021}
}
@article{Deco2013,
abstract = {Trends in Neurosciences. 10.1016/j.tins.2013.03.001},
author = {Deco, Gustavo and Jirsa, Viktor K and McIntosh, Anthony R},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Trends in Neurosciences 2013 Deco-2.pdf:pdf},
journal = {Trends Neurosci.},
month = {mar},
pages = {1--7},
title = {{Resting brains never rest: computational insights into potential cognitive architectures}},
year = {2013}
}
@article{Pooresmaeili2014,
author = {Pooresmaeili, A and Poort, J and Roelfsema, P R},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proceedings of the National Academy of Sciences 2014 Pooresmaeili.pdf:pdf},
journal = {Proc. Natl. Acad. Sci.},
month = {apr},
number = {17},
pages = {6467--6472},
title = {{Simultaneous selection by object-based attention in visual and frontal cortex}},
volume = {111},
year = {2014}
}
@article{Ringach2002b,
abstract = {Abstract I present measurements of the spatial structure of simple-cell receptive fields in macaque primary visual cortex (area V1). Similar to previous findings in cat area 17, the spatial profile of simple-cell receptive fields in the macaque is well described by two- ...
},
author = {Ringach, D L},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neurophysiology 2002 Ringach-2.pdf:pdf},
journal = {J. Neurophysiol.},
month = {jan},
title = {{Spatial structure and symmetry of simple-cell receptive fields in macaque primary visual cortex}},
year = {2002}
}
@article{Bonds1989,
abstract = {... to define more com- pletely the nature of intracortical inhibition and its role in con ... reductions did 
not match either in shape or amplitude the ex- citatory orientation -tuning function . These cells
may therefore rely to some extent on synaptic inhibition to refine orientation  selectivity  ...
},
author = {Bonds, A B},
journal = {Vis. Neurosci.},
month = {jan},
title = {{Role of inhibition in the specification of orientation selectivity of cells in the cat striate cortex}},
year = {1989}
}
@article{Li2016,
abstract = {Memory units have been widely used to enrich the capabilities of deep networks on capturing long-term dependencies in reasoning and prediction tasks, but little investigation exists on deep generative models (DGMs) which are good at inferring high-level invariant representations from unlabeled data. This paper presents a deep generative model with a possibly large external memory and an attention mechanism to capture the local detail information that is often lost in the bottom-up abstraction process in representation learning. By adopting a smooth attention model, the whole network is trained end-to-end by optimizing a variational bound of data likelihood via auto-encoding variational Bayesian methods, where an asymmetric recognition network is learnt jointly to infer high-level invariant representations. The asymmetric architecture can reduce the competition between bottom-up invariant feature extraction and top-down generation of instance details. Our experiments on several datasets demonstrate that memory can significantly boost the performance of DGMs on various tasks, including density estimation, image generation, and missing value imputation, and DGMs with memory can achieve state-ofthe-art quantitative results.},
archivePrefix = {arXiv},
arxivId = {1602.07416},
author = {Li, Chongxuan and Zhu, Jun and Zhang, Bo},
eprint = {1602.07416},
file = {:Users/hahosoya/Documents/Mendeley Desktop/437bf03e20f6e2fa76d8589880aacb2c6e8800fe.pdf:pdf},
isbn = {9781510829008},
journal = {33rd Int. Conf. Mach. Learn. ICML 2016},
pages = {1811--1822},
title = {{Learning to generate with memory}},
volume = {3},
year = {2016}
}
@article{Estebanez2012,
author = {Estebanez, Luc and Boustani, Sami El and Destexhe, Alain and Shulz, Daniel E},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature Publishing Group 2012 Estebanez.pdf:pdf},
journal = {Nat. Neurosci.},
month = {nov},
number = {12},
pages = {1691--1699},
title = {{Correlated input reveals coexisting coding schemes in a sensory cortex}},
volume = {15},
year = {2012}
}
@article{Yu2004,
abstract = {Abstract We study the synthesis of neural coding, selective attention and perceptual decision making. A hierarchical neural architecture is proposed, which implements Bayesian integration of noisy sensory input and topdown attentional priors, leading to sound ...
},
author = {Yu, Angela J and Dayan, Peter},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Advances in neural information {\ldots} 2004 Yu.pdf:pdf},
journal = {Adv. Neural Inf. {\ldots}},
month = {jan},
title = {{Inference, attention, and decision in a Bayesian neural architecture}},
year = {2004}
}
@article{Radford2015,
abstract = {Abstract: In recent years, supervised learning with convolutional networks (CNNs) has seen huge adoption in computer vision applications. Comparatively, unsupervised learning with CNNs has received less attention. In this work we hope to help bridge the gap between the success of CNNs for supervised learning and unsupervised learning. We introduce a class of CNNs called deep convolutional generative adversarial networks (DCGANs), that have ...
},
author = {Radford, A and Metz, L and Chintala, S},
file = {:Users/hahosoya/Documents/Mendeley Desktop/arXiv 2015 Radford.pdf:pdf},
journal = {arXiv.org},
month = {jan},
title = {{Unsupervised representation learning with deep convolutional generative adversarial networks}},
year = {2015}
}
@article{Movshon1978a,
abstract = {Abstract 1. We have examined the responses of simple cells in the cat's atriate cortex to visual patterns that were designed to reveal the extent to which these cells may be considered to sum light-evoked influences linearly across their receptive fields . We used ... 
},
author = {Movshon, J A and Thompson, I D and Tolhurst, D J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/1978 Movshon.pdf:pdf},
month = {jan},
title = {{Spatial summation in the receptive fields of simple cells in the cat's striate cortex.}},
year = {1978}
}
@article{Yildirim2015,
abstract = {Abstract A glance at an object is often sufficient to recognize it and recover fine details of its shape and appearance, even under highly variable viewpoint and lighting conditions. How can vision be so rich, but at the same time robust and fast? The analysis-by-synthesis approach to vision offers an account of the richness of our percepts, but it is typically considered too fragile to apply robustly to real scenes, and too slow to explain perception ...
},
author = {Yildirim, I and Kulkarni, T D and Freiwald, W A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Annual Conference of the Cognitive Science Society. 2015 Yildirim.pdf:pdf},
journal = {Annu. Conf. Cogn. Sci. Soc.},
month = {jan},
title = {{Efficient and robust analysis-by-synthesis in vision: A computational framework, behavioral tests, and modeling neuronal representations}},
year = {2015}
}
@article{Pachitariu2012,
abstract = {Abstract We present a dynamic nonlinear generative model for visual motion based on a latent representation of binary-gated Gaussian variables. Trained on sequences of images, the model learns to represent different movement directions in different variables. We use ... 
},
author = {Pachitariu, M and Sahani, M},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2012 Pachitariu.pdf:pdf},
journal = {{\ldots} Neural Inf. Process. Syst. {\ldots}},
month = {jan},
title = {{Learning visual motion in recurrent neural networks}},
year = {2012}
}
@article{Theves2020,
abstract = {The hippocampal formation encodes maps of space and a key question in neuroscience is whether its spatial coding principles also provide a universal metric for the organization of nonspatial, conceptual information. Previous work demonstrated directional coding during navigation through a continuous stimulus feature space as well as mapping of distances in a feature space that was relevant for concept learning. Here we provide the first unambiguous evidence for a hippocampal representation of the actual concept space, by showing that the hippocampal distance signal selectively reflects the mapping of specifically conceptually relevant rather than of all feature dimensions. During fMRI scanning of 32 human participants (21 females), we presented everyday objects, which had beforehand been associated with specific values on three continuous feature dimensions. Crucially, only two dimensions were relevant to prior concept learning. We find that hippocampal responses to the objects reflect their relative distances in a space defined along conceptually relevant dimensions compared with distances in a space defined along all feature dimensions. These findings suggest that the hippocampus supports knowledge acquisition by dynamically encoding information in a space spanned along dimensions that are relevant in relation to define concepts.},
author = {Theves, Stephanie and Fern{\'{a}}ndez, Guill{\'{e}}n and Doeller, Christian F.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/7318.full.pdf:pdf},
journal = {J. Neurosci.},
keywords = {Conceptual knowledge,FMRI,Hippocampus,Learning,Spatial coding},
number = {38},
pages = {7318--7325},
pmid = {32826311},
title = {{The hippocampus maps concept space, not feature space}},
volume = {40},
year = {2020}
}
@article{Yedidia2001,
abstract = {... such as those introduced by Pearl are guaranteed to converge to the correct marginal posterior 
probabilities ... 2 Belief  propagation fixed-points are zero gradient points of the Bethe free energy ...
The state of each node i is denoted by xi, and the joint probability distribution function ...
},
author = {Yedidia, J S and et Al},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Advances in neural {\ldots} 2001 Yedidia.pdf:pdf},
journal = {Adv. neural  {\ldots}},
month = {jan},
title = {{Generalized belief propagation}},
year = {2001}
}
@article{Khuvis2018,
abstract = {Properties of face-responsive individual neurons in the human ventral temporal cortex (VTC) have yet to be studied, and their role in conscious perception remains unknown. To explore this, we implanted microelectrodes into the VTCs of eight human subjects undergoing invasive epilepsy monitoring. Most (26 of 33) category-selective units showed specificity for face stimuli, with a range of response profiles. Different face exemplars evoked consistent and discriminable responses in the population of units sampled. During a free recall task, face-selective units selectively reactivated in the absence of visual stimulation during the 2-second window prior to face recall events. Furthermore, the identity of the recalled face could be predicted by comparing activity preceding recall events to activity evoked by visual stimulation.},
author = {Khuvis, Simon and Yeagle, Erin M. and Norman, Yitzhak and Grossman, Shany and Malach, Rafael and Mehta, Ashesh D.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/3386.full.pdf:pdf},
journal = {bioRxiv},
keywords = {areas is poorly understood,detailed single-unit survey of,endogenous reactivation of native,face,free recall,fusiform face area,humans,imagery,in,in human subjects,representations in higher sensory,showing,significance statement,single unit,the role of,top-down,ventral temporal cortex,vision,vtc,we conducted the first},
number = {15},
pages = {3386--3399},
title = {{Face-selective units in human ventral temporal cortex reactivate during free recall}},
volume = {41},
year = {2018}
}
@article{Ohzawa1990,
abstract = {The possibility has been explored that a subset of physiologically identiﬁable cells in the visual cortex is especially suited for the processing of stereoscopic depth information. First, characteristics of a disparity detector that would be useful for such processing were outlined. Then, a method was devised by which detailed binocular response data were obtained from cortical cells. In addition, a model of the disparity detector was developed that includes a ...
},
author = {Ohzawa, I and DeAngelis, G C and Freeman, R D},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Science 1990 Ohzawa-2.pdf:pdf},
journal = {Science},
month = {jan},
title = {{Stereoscopic depth discrimination in the visual cortex: neurons ideally suited as disparity detectors}},
year = {1990}
}
@article{Hosseini-Asl2016,
abstract = {We demonstrate a new deep learning autoencoder network, trained by a nonnegativity constraint algorithm (nonnegativity-constrained autoencoder), that learns features that show part-based representation of data. The learning algorithm is based on constraining negative weights. The performance of the algorithm is assessed based on decomposing data into parts and its prediction performance is tested on three standard image data sets and one text data set. The results indicate that the nonnegativity constraint forces the autoencoder to learn features that amount to a part-based representation of data, while improving sparsity and reconstruction quality in comparison with the traditional sparse autoencoder and nonnegative matrix factorization. It is also shown that this newly acquired representation improves the prediction performance of a deep neural network.},
archivePrefix = {arXiv},
arxivId = {1601.02733},
author = {Hosseini-Asl, Ehsan and Zurada, Jacek M. and Nasraoui, Olfa},
eprint = {1601.02733},
file = {:Users/hahosoya/Documents/Mendeley Desktop/1601.02733.pdf:pdf},
journal = {IEEE Trans. Neural Networks Learn. Syst.},
keywords = {Autoencoder,deep architectures,feature learning,nonnegativity constraints,part-based representation},
number = {12},
pages = {2486--2498},
title = {{Deep Learning of Part-Based Representation of Data Using Sparse Autoencoders with Nonnegativity Constraints}},
volume = {27},
year = {2016}
}
@article{Ahmad2013,
abstract = {Sensory inference under conditions of uncertainty is a major problem in both
machine learning and computational neuroscience. An important but poorly
understood aspect of sensory processing is the role of active sensing. Here, we
present a Bayes-optimal inference and control framework for active sensing,
C-DAC (Context-Dependent Active Controller). Unlike previously proposed
algorithms that optimize abstract statistical objectives such as information
maximization (Infomax) [Butko & Movellan, 2010] or one-step look-ahead accuracy
[Najemnik & Geisler, 2005], our active sensing model directly minimizes a
combination of behavioral costs, such as temporal delay, response error, and
effort. We simulate these algorithms on a simple visual search task to
illustrate scenarios in which context-sensitivity is particularly beneficial
and optimization with respect to generic statistical objectives particularly
inadequate. Motivated by the geometric properties of the C-DAC policy, we
present both parametric and non-parametric approximations, which retain
context-sensitivity while significantly reducing computational complexity.
These approximations enable us to investigate the more complex problem
involving peripheral vision, and we notice that the difference between C-DAC
and statistical policies becomes even more evident in this scenario.},
author = {Ahmad, Sheeraz and Yu, Angela J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/CORD Conference Proceedings 2013 Ahmad.pdf:pdf},
journal = {Audio Electroacoust. Newsletter, IEEE},
month = {may},
title = {{Active Sensing as Bayes-Optimal Sequential Decision Making}},
year = {2013}
}
@article{Murray2004,
abstract = {Visual perception involves the grouping of individual elements into coherent patterns, such as object representations, that reduce the descriptive complexity of a visual scene. The computational and physiological bases of this perceptual remain poorly understood. We discuss recent ...},
author = {Murray, S O and Schrater, P},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2004 Murray.pdf:pdf},
journal = {Neural Networks},
month = {jan},
title = {{Perceptual grouping and the interactions between visual cortical areas}},
year = {2004}
}
@article{Dayan1998,
abstract = {Binocular rivalry is the alternating percept that can result when the two eyes see different scenes. Recent psychophysical evidence supports the notion that some aspects of binocular rivalry bear functional similarities to other bistable percepts. We build a model based on the hypothesis (Logothetis & Schall, 1989; Leopold & Logothetis, 1996; Logothetis, Leopold & Sheinberg, 1996) that alternation can be generated by competition between top-down cortical explanations for the inputs, rather than by direct competition between the inputs. Recent neurophysiological evidence shows that some binocular neurons are modulated with the changing percept; others are not, even if they are selective between the stimuli presented to the eyes. We extend our model to a hierarchy to address these effects.},
address = {Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge 02139, USA.},
author = {Dayan, P},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neural Computation 1998 Dayan.pdf:pdf},
journal = {Neural Comput.},
month = {jul},
number = {5},
pages = {1119--1135},
title = {{A hierarchical model of binocular rivalry.}},
volume = {10},
year = {1998}
}
@article{Beck2008,
author = {Beck, Jeffrey M and Ma, Wei Ji and Kiani, Roozbeh and Hanks, Tim and Churchland, Anne K and Roitman, Jamie and Shadlen, Michael N and Latham, Peter E and Pouget, Alexandre},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neuron 2008 Beck.pdf:pdf},
journal = {Neuron},
month = {dec},
number = {6},
pages = {1142--1152},
title = {{Probabilistic Population Codes for Bayesian Decision Making}},
volume = {60},
year = {2008}
}
@article{Cohen2011,
author = {Cohen, Michael A and Dennett, Daniel C},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Trends in cognitive Sciences 2011 Cohen.pdf:pdf},
journal = {Trends Cogn. Sci.},
month = {aug},
number = {8},
pages = {358--364},
title = {{Consciousness cannot be separated from function}},
volume = {15},
year = {2011}
}
@article{Huang2018a,
abstract = {Neurons in macaque inferotemporal cortex (ITC) respond less strongly to familiar than to novel images. It is commonly assumed that this effect arises within ITC because its neurons respond selectively to complex images and thus encode in an explicit form information sufficient for identifying a particular image as familiar. However, no prior study has examined whether neurons in low-order visual areas selective for local features also exhibit familiarity suppression. To address this issue, we recorded from neurons in macaque area V2 with semichronic microelectrode arrays while monkeys repeatedly viewed a set of large complex natural images. We report here that V2 neurons exhibit familiarity suppression. The effect develops over several days with a trajectory well fitted by an exponential function with a rate constant of ∼100 exposures. Suppression occurs in V2 at a latency following image onset shorter than its reported latency in ITC.SIGNIFICANCE STATEMENTFamiliarity suppression, the tendency for neurons to respond less strongly to familiar than novel images, is well known in monkey inferotemporal cortex. Suppression has been thought to arise in inferotemporal cortex because its neurons respond selectively to large complex images and thus explicitly to encode information sufficient for identifying a particular image as familiar. No previous study has explored the possibility that familiarity suppression occurs even in early-stage visual areas where neurons are selective for simple features in confined receptive fields. We now report that neurons in area V2 exhibit familiarity suppression. This finding challenges our current understanding of information processing in V2 as well as our understanding of the mechanisms that underlie familiarity suppression.},
address = {Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213.},
author = {Huang, Ge and Ramachandran, Suchitra and Lee, Tai Sing and Olson, Carl R},
file = {:Users/hahosoya/Documents/Mendeley Desktop/8619E515-499F-4A97-98F1-0410E55E9572.pdf:pdf},
journal = {J. Neurosci.},
month = {oct},
number = {42},
pages = {8967--8975},
title = {{Neural Correlate of Visual Familiarity in Macaque Area V2.}},
volume = {38},
year = {2018}
}
@article{Shibata2011,
author = {Shibata, K and Watanabe, T and Sasaki, Y and Kawato, M},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Science 2011 Shibata.pdf:pdf},
journal = {Science},
month = {dec},
number = {6061},
pages = {1413--1415},
title = {{Perceptual Learning Incepted by Decoded fMRI Neurofeedback Without Stimulus Presentation}},
volume = {334},
year = {2011}
}
@article{Sukhbaatar2015,
abstract = {We introduce a neural network with a recurrent attention model over a possibly large external memory. The architecture is a form of Memory Network [23] but unlike the model in that work, it is trained end-to-end, and hence requires significantly less supervision during training, making it more generally applicable in realistic settings. It can also be seen as an extension of RNNsearch [2] to the case where multiple computational steps (hops) are performed per output symbol. The flexibility of the model allows us to apply it to tasks as diverse as (synthetic) question answering [22] and to language modeling. For the former our approach is competitive with Memory Networks, but with less supervision. For the latter, on the Penn TreeBank and Text8 datasets our approach demonstrates comparable performance to RNNs and LSTMs. In both cases we show that the key concept of multiple computational hops yields improved results.},
archivePrefix = {arXiv},
arxivId = {1503.08895},
author = {Sukhbaatar, Sainbayar and Szlam, Arthur and Weston, Jason and Fergus, Rob},
eprint = {1503.08895},
file = {:Users/hahosoya/Documents/Mendeley Desktop/6542c59a42a19f5496c226299a6a178ef0fd1175.pdf:pdf},
journal = {Adv. Neural Inf. Process. Syst.},
pages = {2440--2448},
title = {{End-to-end memory networks}},
volume = {2015-Janua},
year = {2015}
}
@article{Hyvarinen2005a,
abstract = {BACKGROUND:It has been shown that the classical receptive fields of simple and complex cells in the primary visual cortex emerge from the statistical properties of natural images by forcing the cell responses to be maximally sparse or independent. We investigate how to learn features beyond the primary visual cortex from the statistical properties of modelled complex-cell outputs. In previous work, we showed that a new model, non-negative sparse coding, led to the emergence of features which code for contours of a given spatial frequency band.

RESULTS:We applied ordinary independent component analysis to modelled outputs of complex cells that span different frequency bands. The analysis led to the emergence of features which pool spatially coherent across-frequency activity in the modelled primary visual cortex. Thus, the statistically optimal way of processing complex-cell outputs abandons separate frequency channels, while preserving and even enhancing orientation tuning and spatial localization. As a technical aside, we found that the non-negativity constraint is not necessary: ordinary independent component analysis produces essentially the same results as our previous work.

CONCLUSION:We propose that the pooling that emerges allows the features to code for realistic low-level image features related to step edges. Further, the results prove the viability of statistical modelling of natural images as a framework that produces quantitative predictions of visual processing.},
address = {HIIT Basic Research Unit and Dept of Computer Science, University of Helsinki, Finland. ahyvarin@cc.helsinki.fi},
author = {Hyv{\"{a}}rinen, Aapo and Gutmann, Michael and Hoyer, Patrik O},
file = {:Users/hahosoya/Documents/Mendeley Desktop/BMC neuroscience 2005 Hyv{\"{a}}rinen.pdf:pdf},
journal = {BMC Neurosci.},
month = {jan},
pages = {12},
title = {{Statistical model of natural stimuli predicts edge-like pooling of spatial frequency channels in V2.}},
volume = {6},
year = {2005}
}
@article{Vinken2018,
author = {Vinken, Kasper and {Op de Beeck}, Hans and Vogels, Rufin},
file = {:Users/hahosoya/Documents/Mendeley Desktop/32FFAA3B-30B5-4D38-95E4-D5F291ED14C3.pdf:pdf},
journal = {J. Neurosci.},
month = {jul},
pages = {413--462},
title = {{Face Repetition Probability Does Not Affect Repetition Suppression in Macaque Inferotemporal Cortex}},
year = {2018}
}
@article{Terashima2012,
abstract = {... In NIPS 2011. [18] H. Terashima and H. Hosoya . Sparse codes of harmonic natural sounds and their modulatory interactions. Network: Computation in Neural Systems, 20(4):253–267, 2009. [19] H. Terashima , H. Hosoya , T. Tani, N. Ichinohe, and M. Okada. ... 
},
author = {Terashima, H and Okada, M},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2012 Terashima.pdf:pdf},
journal = {{\ldots} Neural Inf. Process. Syst. 25},
month = {jan},
title = {{The topographic unsupervised learning of natural sounds in the auditory cortex}},
year = {2012}
}
@article{Sharpee2004,
abstract = {We propose a method that allows for a rigorous statistical analysis of neural responses to natural stimuli that are nongaussian and exhibit strong correlations. We have in mind a model in which neurons are selective for a small number of stimulus dimensions out of a high-dimensional stimulus space, but within this subspace the responses can be arbitrarily nonlinear. Existing analysis methods are based on correlation functions between stimuli and responses, but these methods are guaranteed to work only in the case of gaussian stimulus ensembles. As an alternative to correlation functions, we maximize the mutual information between the neural responses and projections of the stimulus onto low-dimensional subspaces. The procedure can be done iteratively by increasing the dimensionality of this subspace. Those dimensions that allow the recovery of all of the information between spikes and the full unprojected stimuli describe the relevant subspace. If the dimensionality of the relevant subspace indeed is small, it becomes feasible to map the neuron's input-output function even under fully natural stimulus conditions. These ideas are illustrated in simulations on model visual and auditory neurons responding to natural scenes and sounds, respectively.},
address = {Sloan-Swartz Center for Theoretical Neurobiology, Department of Physiology, University of California at San Francisco CA 94143, USA. sharpee@phy.ucsf.edu},
author = {Sharpee, Tatyana and Rust, Nicole C and Bialek, William},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neural Computation 2004 Sharpee.pdf:pdf},
journal = {Neural Comput.},
month = {feb},
number = {2},
pages = {223--250},
title = {{Analyzing neural responses to natural signals: maximally informative dimensions.}},
volume = {16},
year = {2004}
}
@article{Tarr2000,
abstract = {Because extreme values for these two dimensions are typically associated with face recognition, any comparison between faces and non-face objects confounds these factors unless they are independently manipulated. When we do such manipulations, it is the ...
},
author = {Tarr, M J and Gauthier, I},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature neuroscience 2000 Tarr-1.pdf:pdf},
journal = {Nat. Neurosci.},
month = {jan},
title = {{FFA: a flexible fusiform area for subordinate-level visual processing automatized by expertise}},
year = {2000}
}
@inproceedings{Le2012,
author = {Le, Quoc V and Ranzato, Marc'Aurelio and Monga, Rajat and Devin, Matthieu and Corrado, Greg and Chen, Kai and Dean, Jeffrey and Ng, Andrew Y},
booktitle = {Proc. 29th Int. Conf. Mach. Learn.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proceedings of the 29th International Conference on Machine Learning (ICML) 2012 Le.pdf:pdf},
month = {jan},
title = {{Building high-level features using large scale unsupervised learning}},
year = {2012}
}
@article{Eslami2018,
abstract = {Scene representation-the process of converting visual sensory data into concise descriptions-is a requirement for intelligent behavior. Recent work has shown that neural networks excel at this task when provided with large, labeled datasets. However, removing the reliance on human labeling remains an important open problem. To this end, we introduce the Generative Query Network (GQN), a framework within which machines learn to represent scenes using only their own sensors. The GQN takes as input images of a scene taken from different viewpoints, constructs an internal representation, and uses this representation to predict the appearance of that scene from previously unobserved viewpoints. The GQN demonstrates representation learning without human labels or domain knowledge, paving the way toward machines that autonomously learn to understand the world around them.},
address = {DeepMind, 5 New Street Square, London EC4A 3TW, UK. aeslami@google.com.},
author = {Eslami, S M Ali and {Jimenez Rezende}, Danilo and Besse, Frederic and Viola, Fabio and Morcos, Ari S and Garnelo, Marta and Ruderman, Avraham and Rusu, Andrei A and Danihelka, Ivo and Gregor, Karol and Reichert, David P and Buesing, Lars and Weber, Theophane and Vinyals, Oriol and Rosenbaum, Dan and Rabinowitz, Neil and King, Helen and Hillier, Chloe and Botvinick, Matt and Wierstra, Daan and Kavukcuoglu, Koray and Hassabis, Demis},
file = {:Users/hahosoya/Documents/Mendeley Desktop/76D03CCB-DDAB-4481-8467-5E27D463988F.pdf:pdf},
journal = {Science},
month = {jun},
number = {6394},
pages = {1204--1210},
title = {{Neural scene representation and rendering.}},
volume = {360},
year = {2018}
}
@article{Yamins2014,
abstract = {The ventral visual stream underlies key human visual object recognition abilities. However, neural encoding in the higher areas of the ventral stream remains poorly understood. Here, we describe a modeling approach that yields a quantitatively accurate model of inferior temporal (IT) cortex, the highest ventral cortical area. Using high-throughput computational techniques, we discovered that, within a class of biologically plausible hierarchical neural network models, there is a strong correlation between a model's categorization performance and its ability to predict individual IT neural unit response data. To pursue this idea, we then identified a high-performing neural network that matches human performance on a range of recognition tasks. Critically, even though we did not constrain this model to match neural data, its top output layer turns out to be highly predictive of IT spiking responses to complex naturalistic images at both the single site and population levels. Moreover, the model's intermediate layers are highly predictive of neural responses in the V4 cortex, a midlevel visual area that provides the dominant cortical input to IT. These results show that performance optimization--applied in a biologically appropriate model class--can be used to build quantitative predictive models of neural processing.},
address = {Department of Brain and Cognitive Sciences and McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139; and.},
author = {Yamins, Daniel L K and Hong, Ha and Cadieu, Charles F and Solomon, Ethan A and Seibert, Darren and DiCarlo, James J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proceedings of the National Academy of Sciences 2014 Yamins.pdf:pdf},
journal = {Proc. Natl. Acad. Sci.},
month = {jun},
number = {23},
pages = {8619--8624},
title = {{Performance-optimized hierarchical models predict neural responses in higher visual cortex.}},
volume = {111},
year = {2014}
}
@article{Poort2016,
author = {Poort, Jasper and Self, Matthew W and van Vugt, Bram and Malkki, Hemi and Roelfsema, Pieter R},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Cerebral Cortex 2016 Poort.pdf:pdf},
journal = {Cereb. Cortex},
month = {sep},
number = {10},
pages = {3964--3976},
title = {{Texture Segregation Causes Early Figure Enhancement and Later Ground Suppression in Areas V1 and V4 of Visual Cortex}},
volume = {26},
year = {2016}
}
@article{Palmer1987a,
abstract = {...  Receptive Fields in Cat Striate Cortex JUDSON P. JONES AND LARRY A. PALMER Department ofAnatomy ... I. A reverse correlation (6, 8, 25, 35) method is developed that allows quantitative determination of visual receptive - field struc- ture in two spatial dimensions. ...},
author = {Palmer, L A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neurophysiology 1987 Palmer.pdf:pdf},
journal = {J. Neurophysiol.},
month = {jan},
title = {{The two-dimensional spatial structure of simple receptive fields in cat striate cortex}},
year = {1987}
}
@incollection{NIPS2017_7181,
author = {Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N and Kaiser, {\L}ukasz and Polosukhin, Illia},
booktitle = {Adv. Neural Inf. Process. Syst. 30},
editor = {Guyon, I and Luxburg, U V and Bengio, S and Wallach, H and Fergus, R and Vishwanathan, S and Garnett, R},
file = {:Users/hahosoya/Documents/Mendeley Desktop/7181-attention-is-all-you-need.pdf:pdf},
pages = {5998--6008},
title = {{Attention is All you Need}},
year = {2017}
}
@article{Bengio2013,
abstract = {The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the different explanatory factors of variation behind the data. Although specific domain knowledge can be used to help design representations, learning with generic priors can also be used, and the quest for AI is motivating the design of more powerful representation-learning algorithms implementing such priors. This paper reviews recent work in the area of unsupervised feature learning and deep learning, covering advances in probabilistic models, autoencoders, manifold learning, and deep networks. This motivates longer term unanswered questions about the appropriate objectives for learning good representations, for computing representations (i.e., inference), and the geometrical connections between representation learning, density estimation, and manifold learning.},
author = {Bengio, Yoshua and Courville, Aaron and Vincent, Pascal},
file = {:Users/hahosoya/Documents/Mendeley Desktop/06472238.pdf:pdf},
journal = {IEEE Trans. Pattern Anal. Mach. Intell.},
keywords = {Boltzmann machine,Deep learning,autoencoder,feature learning,neural nets,representation learning,unsupervised learning},
number = {8},
pages = {1798--1828},
title = {{Representation learning: A review and new perspectives}},
volume = {35},
year = {2013}
}
@article{Karklin2008,
author = {Karklin, Yan and Lewicki, Michael S},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature 2008 Karklin.pdf:pdf},
journal = {Nature},
month = {nov},
number = {7225},
pages = {83--86},
title = {{Emergence of complex cell properties by learning to generalize in natural scenes}},
volume = {457},
year = {2008}
}
@article{Poort2015,
abstract = {Neuron, 86 (2015) 1478-1490. doi:10.1016/j.neuron.2015.05.037},
author = {Poort, Jasper and Khan, Adil G and Pachitariu, Marius and Nemri, Abdellatif and Orsolic, Ivana and Krupic, Julija and Bauza, Marius and Sahani, Maneesh and Keller, Georg B and Mrsic-Flogel, Thomas D and Hofer, Sonja B},
file = {:Users/hahosoya/Documents/Mendeley Desktop/00B942FC-1C45-4231-B916-EC0A5670DAD6.pdf:pdf},
journal = {Neuron},
month = {jun},
number = {6},
pages = {1478--1490},
title = {{Learning Enhances Sensory and Multiple Non- sensory Representations in Primary Visual Cortex}},
volume = {86},
year = {2015}
}
@article{Chen2019,
author = {Chen, Nihong and Shin, Kilho and Millin, Rachel and Song, Yongqian and Kwon, MiYoung and Tjan, Bosco S},
file = {:Users/hahosoya/Documents/Mendeley Desktop/C5AFAABE-990A-4954-B98F-530C8E7EBB68.pdf:pdf},
journal = {J. Neurosci.},
month = {feb},
pages = {2126--2128},
title = {{Cortical reorganization of peripheral vision induced by simulated central vision loss}},
year = {2019}
}
@article{Le2010,
abstract = {Abstract Convolutional neural networks (CNNs) have been successfully applied to many tasks such as digit and object recognition. Using convolutional (tied) weights significantly reduces the number of parameters that have to be learned, and also allows translational  ...},
author = {Le, Q V and Ngiam, J and Chen, Z and et Al},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Advances in neural {\ldots} 2010 Le.pdf:pdf},
journal = {Adv. neural  {\ldots}},
month = {jan},
title = {{Tiled convolutional neural networks}},
year = {2010}
}
@article{Paninski2004,
abstract = {Recent work has examined the estimation of models of stimulus-driven neural activity in which some linear filtering process is followed by a nonlinear, probabilistic spiking stage. We analyze the estimation of one such model for which this nonlinear step is implemented  ...},
author = {Paninski, L},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Network Computation in Neural Systems 2004 Paninski.pdf:pdf},
journal = {Netw. Comput. Neural Syst.},
month = {jan},
title = {{Maximum likelihood estimation of cascade point-process neural encoding models}},
year = {2004}
}
@article{Bichot2015,
abstract = {Neuron, 88 (2015) 832-844. doi:10.1016/j.neuron.2015.10.001},
author = {Bichot, Narcisse P and Heard, Matthew T and DeGennaro, Ellen M and Desimone, Robert},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neuron 2015 Bichot.pdf:pdf},
journal = {Neuron},
month = {nov},
number = {4},
pages = {832--844},
title = {{A Source for Feature-Based Attention in the Prefrontal Cortex}},
volume = {88},
year = {2015}
}
@article{Kumar2016,
abstract = {Most tasks in natural language processing can be cast into question answering (QA) problems over language input. We introduce the dynamic memory network (DMN), a neural network architecture which processes input sequences and questions, forms episodic memories, and generates relevant answers. Questions trigger an iterative attention process which allows the model to condition its attention on the inputs and the result of previous iterations. These results are then reasoned over in a hierarchical recurrent sequence model to generate answers. The DMN can be trained end-to-end and obtains state-of-the-art results on several types of tasks and datasets: question answering (Facebook's bAbl dataset), text classification for sentiment analysis (Stanford Sentiment Treebank) and sequence modeling for part-of-speech tagging (WSJ-PTB). The training for these different tasks relies exclusively on trained word vector representations and input-question-answer triplets.},
archivePrefix = {arXiv},
arxivId = {1506.07285},
author = {Kumar, Ankit and Irsoy, Ozan and Ondruska, Peter and Iyyer, Mohit and Bradbury, James and Gulrajani, Ishaan and Zhong, Victor and Paulus, Romain and Socher, Richard},
eprint = {1506.07285},
file = {:Users/hahosoya/Documents/Mendeley Desktop/78e7edb4f051d9af41d566e31d57539c9a163144.pdf:pdf},
isbn = {9781510829008},
journal = {33rd Int. Conf. Mach. Learn. ICML 2016},
pages = {2068--2078},
title = {{Ask me anything: Dynamic memory networks for natural language processing}},
volume = {3},
year = {2016}
}
@article{Diedrichsen2011,
abstract = {In recent years there has been growing interest in multivariate analyses of neuroimaging data, which can be used to detect distributed patterns of activity that encode an experimental factor of interest. In this setting, it has become common practice to study the correlations between patterns to make inferences about the way a brain region represents stimuli or tasks (known as representational similarity analysis). Although it would be of great interest to compare these correlations from different regions, direct comparisons are currently not possible. This is because sample correlations are strongly influenced by voxel-selection, fMRI noise, and nonspecific activation patterns, all of which can differ widely between regions. Here, we present a multivariate modeling framework in which the measured patterns are decomposed into their constituent parts. The model is based on a standard linear mixed model, in which pattern components are considered to be randomly distributed over voxels. The model allows one to estimate the true correlations of the underlying neuronal pattern components, thereby enabling comparisons between different regions or individuals. The pattern estimates also allow us to make inferences about the spatial structure of different response components. Thus, the new model provides a theoretical and analytical framework to study the structure of distributed neural representations. {\textcopyright} 2011 Elsevier Inc.},
author = {Diedrichsen, J{\"{o}}rn and Ridgway, Gerard R. and Friston, Karl J. and Wiestler, Tobias},
file = {:Users/hahosoya/Documents/Mendeley Desktop/88feeb08af0aa84e984fe42cf8d084929883acdb.pdf:pdf},
journal = {Neuroimage},
number = {4},
pages = {1665--1678},
title = {{Comparing the similarity and spatial structure of neural representations: A pattern-component model}},
volume = {55},
year = {2011}
}
@article{Lasserre2006,
abstract = {Abstract: When labelled training data is plentiful, discriminative techniques are widely used since they give excellent generalization performance. However, for large-scale applications such as object recognition, hand labelling of data is expensive, and there is much interest in semi-supervised techniques based on generative models in which the majority of the training data is unlabelled. Although the generalization performance of generative models ...
},
author = {Lasserre, J A and Bishop, C M},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2006 Lasserre.pdf:pdf},
journal = {Comput. Vis. {\ldots}},
month = {jan},
title = {{Principled hybrids of generative and discriminative models}},
year = {2006}
}
@book{Rojas1996,
abstract = {Neural networks are a computing paradigm that is finding increasing attention among computer scientists. In this book, theoretical laws and models previously scattered in the literature are brought together into a general theory of artificial neural nets. Always with a ...
},
author = {Rojas, R},
file = {:Users/hahosoya/Documents/Mendeley Desktop/1996 Rojas.pdf:pdf},
month = {jan},
title = {{No Title}},
year = {1996}
}
@article{Tanaka2018,
abstract = {Episodic memories are encoded by a sparse population of hippocampal neurons. In mice, optogenetic manipulation of this memory engram established that these neurons are indispensable and inducing for memory recall. However, little is known about their in vivo activity or precise role in memory. We found that during memory encoding, only a fraction of CA1 place cells function as engram neurons, distinguished by firing repetitive bursts paced at the theta frequency. During memory recall, these neurons remained highly context specific, yet demonstrated preferential remapping of their place fields. These data demonstrate a dissociation of precise spatial coding and contextual indexing by distinct hippocampal ensembles and suggest that the hippocampal engram serves as an index of memory content.},
address = {Laboratory for Circuit and Behavioral Physiology, RIKEN Center for Brain Science, 2-1 Hirosawa, Wakoshi, Saitama, Japan. kazumasa.tanaka@riken.jp thomas.mchugh@riken.jp.},
author = {Tanaka, Kazumasa Z and He, Hongshen and Tomar, Anupratap and Niisato, Kazue and Huang, Arthur J Y and McHugh, Thomas J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/49310B79-70B6-40BC-88BC-2B27262F9854.pdf:pdf},
journal = {Science},
month = {jul},
number = {6400},
pages = {392--397},
title = {{The hippocampal engram maps experience but not place.}},
volume = {361},
year = {2018}
}
@article{Matsumoto2005,
abstract = {Although there is no retinal input within the blind spot, it is filled with the same visual attributes as its surround. Earlier studies showed that neural responses are evoked at the retinotopic representation of the blind spot in the primary visual cortex (V1) when perceptual filling-in of a surface or ...},
author = {Matsumoto, M},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neurophysiology 2005 Matsumoto.pdf:pdf},
journal = {J. Neurophysiol.},
month = {jan},
title = {{Neural responses in the macaque V1 to bar stimuli with various lengths presented on the blind spot}},
year = {2005}
}
@article{Hung2012,
abstract = {Neuron, 74 (2012) 1099-1113. doi:10.1016/j.neuron.2012.04.029},
author = {Hung, Chia-Chun and Carlson, Eric T and Connor, Charles E},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neuron 2012 Hung.pdf:pdf},
journal = {Neuron},
month = {jun},
number = {6},
pages = {1099--1113},
title = {{Medial Axis Shape Coding in Macaque Inferotemporal Cortex}},
volume = {74},
year = {2012}
}
@article{Kalfas2018,
author = {Kalfas, Ioannis and Vinken, Kasper and Vogels, Rufin},
editor = {Kriegeskorte, Nikolaus},
file = {:Users/hahosoya/Documents/Mendeley Desktop/77079BF3-2132-4303-B8A6-DBA8A0C21741.pdf:pdf},
journal = {PLoS Comput. Biol.},
month = {oct},
number = {10},
pages = {e1006557--26},
title = {{Representations of regular and irregular shapes by deep Convolutional Neural Networks, monkey inferotemporal neurons and human judgments}},
volume = {14},
year = {2018}
}
@article{Viinikanoja2010,
abstract = {We study the problem of extracting statistical dependencies between multivariate signals, to be used for exploratory analysis of complicated natural phenomena. In particular, we develop generative models for extracting the dependencies, made possible by the ... 
},
author = {Viinikanoja, J and Klami, A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2010 Viinikanoja.pdf:pdf},
journal = {Mach. Learn. Knowl. {\ldots}},
month = {jan},
title = {{Variational Bayesian mixture of robust CCA models}},
year = {2010}
}
@article{Leopold1996,
author = {Leopold, David A and Logothetis, Nikos K},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature 1996 Leopold.pdf:pdf},
journal = {Nature},
month = {jan},
pages = {9},
title = {{Activity changes in early visual cortex reflect monkeys' percepts during binocular rivalry}},
volume = {3},
year = {1996}
}
@article{Keller2018,
abstract = {This perspective describes predictive processing as a computational framework for understanding cortical function in the context of emerging evidence, with a focus on sensory processing. We discuss how the predictive processing framework may be implemented at the level of cortical circuits and how its implementation could be falsified experimentally. Lastly, we summarize the general implications of predictive processing on cortical function in healthy and diseased states. In this perspective, Keller and Mrsic-Flogel describe the advantages of predictive processing as a computational framework for understanding cortical function in the context of emerging evidence with a focus on sensory processing.},
author = {Keller, Georg B. and Mrsic-Flogel, Thomas D.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/1-s2.0-S0896627318308572-main.pdf:pdf},
journal = {Neuron},
keywords = {canonical microcircuit,cortex,predictive coding,predictive processing,sensory processing},
number = {2},
pages = {424--435},
pmid = {30359606},
title = {{Predictive Processing: A Canonical Cortical Computation}},
volume = {100},
year = {2018}
}
@article{David2004,
abstract = {Studies of the primary visual cortex (V1) have produced models that account for neuronal responses to synthetic stimuli such as sinusoidal gratings. Little is known about how these models generalize to activity during natural vision. We recorded neural responses in area V1 of awake macaques to a stimulus with natural spatiotemporal statistics and to a dynamic grating sequence stimulus. We fit nonlinear receptive field models using each of these data sets and compared how well they predicted time-varying responses to a novel natural visual stimulus. On average, the model fit using the natural stimulus predicted natural visual responses more than twice as accurately as the model fit to the synthetic stimulus. The natural vision model produced better predictions in >75% of the neurons studied. This large difference in predictive power suggests that natural spatiotemporal stimulus statistics activate nonlinear response properties in a different manner than the grating stimulus. To characterize this modulation, we compared the temporal and spatial response properties of the model fits. During natural stimulation, temporal responses often showed a stronger late inhibitory component, indicating an effect of nonlinear temporal summation during natural vision. In addition, spatial tuning underwent complex shifts, primarily in the inhibitory, rather than excitatory, elements of the response profile. These differences in late and spatially tuned inhibition accounted fully for the difference in predictive power between the two models. Both the spatial and temporal statistics of the natural stimulus contributed to the modulatory effects.},
address = {Program in Bioengineering, University of California Berkeley, Berkeley, California 94720-1650, USA.},
author = {David, Stephen V and Vinje, William E and Gallant, Jack L},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2004 David.pdf:pdf},
journal = {J. Neurosci.},
month = {aug},
number = {31},
pages = {6991--7006},
title = {{Natural stimulus statistics alter the receptive field structure of v1 neurons.}},
volume = {24},
year = {2004}
}
@article{Burne1984,
abstract = {Response properties of neurons in the visual cortex, area 17, of Long Evans pigmented rats were investigated quantitatively with computer-controlled stimuli. Ninety percent of the cells recorded (296/327) were responsive to visual stimulation. The majority (95%, 281/296) responded to moving images and were classified as complex (44%), simple (27%), hypercomplex (13%) and non-oriented (16%) according to criteria previously established for cortical cells in the cat and monkey. The remaining 5% of the neurons responded only to stationary stimuli flashed on-off in their receptive field. Results of this study indicate that neurons of the rat visual cortex have properties similar to those of cells in the striate cortex of more 'visual' mammals.},
author = {Burne, R A and Parnavelas, J G and Lin, C S},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Exp Brain Res 1984 Burne.pdf:pdf},
journal = {Exp. brain Res.},
month = {jan},
number = {2},
pages = {374--383},
title = {{Response properties of neurons in the visual cortex of the rat.}},
volume = {53},
year = {1984}
}
@article{Hosoya2012b,
author = {Hosoya, Haruo},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2012 Hosoya-2.pdf:pdf},
month = {sep},
pages = {1},
title = {{Recovering V2 recep.ve fields by Bayesian spike-‐triggered covariance analysis}},
year = {2012}
}
@article{Bishop1999,
abstract = {Abstract The technique of principal component analysis ( PCA ) has recently been expressed as the maximum likelihood solution for a generative latent variable model. In this paper we use this probabilistic reformulation as the basis for a Bayesian treatment of PCA . Our key  ...},
author = {Bishop, C M},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Advances in neural information processing {\ldots} 1999 Bishop.pdf:pdf},
journal = {Adv. Neural Inf. Process. Syst.},
month = {jan},
title = {{Bayesian pca}},
year = {1999}
}
@article{Ferster2012,
abstract = {Neuron, 75 (2012) 194-208. doi:10.1016/j.neuron.2012.06.011},
author = {Ferster, NicholasnbspJ Priebe David and Ferster, David},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neuron 2012 Ferster.pdf:pdf},
journal = {Neuron},
month = {jul},
number = {2},
pages = {194--208},
title = {{Mechanisms of Neuronal Computation in Mammalian Visual Cortex}},
volume = {75},
year = {2012}
}
@article{Stemmann2019,
abstract = {From incoming sensory information, our brains make selections according to current behavioral goals. This process, selective attention, is controlled by parietal and frontal areas. Here, we show that another brain area, posterior inferotemporal cortex (PITd), also exhibits the defining properties of attentional control. We discovered this area with functional magnetic resonance imaging (fMRI) during an attentive motion discrimination task. Single-cell recordings from PITd revealed strong attentional modulation across 3 attention tasks yet no tuning to task-relevant stimulus features, like motion direction or color. Instead, PITd neurons closely tracked the subject's attention state and predicted upcoming errors of attentional selection. Furthermore, artificial electrical PITd stimulation controlled the location of attentional selection without altering feature discrimination. These are the defining properties of a feature-blind priority map encoding the locus of attention. Together, these results suggest area PITd, located strategically to gather information about object properties, as an attentional priority map.},
author = {Stemmann, Heiko and Freiwald, Winrich A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/23797.full.pdf:pdf},
journal = {Proc. Natl. Acad. Sci. U. S. A.},
pmid = {31685625},
title = {{Evidence for an attentional priority map in inferotemporal cortex.}},
year = {2019}
}
@article{Beal2006,
author = {Beal, M J and Ghahramani, Z},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2006 Beal.pdf:pdf},
journal = {Bayesian Anal.},
month = {jan},
number = {4},
pages = {793--832},
title = {{Variational Bayesian learning of directed graphical models with hidden variables}},
volume = {1},
year = {2006}
}
@article{Ding2020,
abstract = {Communication lays the foundation for human cooperation. It is also crucial for multi-agent cooperation. However, existing work focuses on broadcast communication, which is not only impractical but also leads to information redundancy that could even impair the learning process. To tackle these difficulties, we propose Individually Inferred Communication (I2C), a simple yet effective model to enable agents to learn a prior for agent-agent communication. The prior knowledge is learned via causal inference and realized by a feed-forward neural network that maps the agent's local observation to a belief about who to communicate with. The influence of one agent on another is inferred via the joint action-value function in multi-agent reinforcement learning and quantified to label the necessity of agent-agent communication. Furthermore, the agent policy is regularized to better exploit communicated messages. Empirically, we show that I2C can not only reduce communication overhead but also improve the performance in a variety of multi-agent cooperative scenarios, comparing to existing methods.},
archivePrefix = {arXiv},
arxivId = {2006.06455},
author = {Ding, Ziluo and Huang, Tiejun and Lu, Zongqing},
eprint = {2006.06455},
file = {:Users/hahosoya/Documents/Mendeley Desktop/9fd6a64b9e55941e591c84d6c74b731c6beba1d5.pdf:pdf},
journal = {arXiv},
title = {{Learning Individually Inferred Communication for Multi-Agent Cooperation}},
year = {2020}
}
@article{Tanigawa2010,
abstract = {Visual area V4 in the macaque monkey is a cortical area that is strongly involved in color and shape perception. However, fundamental questions about V4 are still debated. V4 was initially characterized as a color -processing area, but subsequent studies revealed that it  ...},
author = {Tanigawa, H and Lu, H D and et Al},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature neuroscience 2010 Tanigawa-1.pdf:pdf},
journal = {Nat. Neurosci.},
month = {jan},
title = {{Functional organization for color and orientation in macaque V4}},
year = {2010}
}
@article{Pasupathy1999,
author = {Pasupathy, Anitha and Connor, Charles E},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neurophysiology 1999 Pasupathy.pdf:pdf},
journal = {J. Neurophysiol.},
month = {jan},
number = {5},
pages = {2490--2502},
title = {{Responses to contour features in macaque area V4}},
volume = {82},
year = {1999}
}
@article{Koch2018,
author = {Koch, Erin and Baig, Famya and Zaidi, Qasim},
file = {:Users/hahosoya/Documents/Mendeley Desktop/831CB1D5-1219-4B11-97B4-F81F5AD9101E.pdf:pdf},
journal = {Proc. Natl. Acad. Sci. U. S. A.},
month = {jul},
number = {30},
pages = {7807--7812},
title = {{Picture perception reveals mental geometry of 3D scene inferences}},
volume = {115},
year = {2018}
}
@article{Sharpee2013,
author = {Sharpee, T O and Kouh, M and Reynolds, J H},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proceedings of the National Academy of Sciences 2013 Sharpee.pdf:pdf},
journal = {Proc. Natl. Acad. Sci.},
month = {jul},
number = {28},
pages = {11618--11623},
title = {{Trade-off between curvature tuning and position invariance in visual area V4}},
volume = {110},
year = {2013}
}
@article{Dilks2011,
abstract = {Abstract Electrophysiological and behavioral studies in many species have demonstrated mirror - image confusion for objects, perhaps because many objects are vertically symmetric (eg, a cup is the same cup when seen in left or right profile). In contrast, the navigability of ... 
},
author = {Dilks, D D and Julian, J B and Kubilius, J and Spelke, E S and Kanwisher, N},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2011 Dilks.pdf:pdf},
journal = {J. Neurosci.},
month = {aug},
number = {31},
pages = {11305--11312},
title = {{Mirror-Image Sensitivity and Invariance in Object and Scene Processing Pathways}},
volume = {31},
year = {2011}
}
@article{Markram1997,
abstract = {Activity-driven modifications in synaptic connections between neurons in the neocortex may occur during development and learning. In dual whole-cell voltage recordings from pyramidal neurons, the coincidence of postsynaptic action potentials (APs) and unitary excitatory postsynaptic potentials (EPSPs) was found to induce changes in EPSPs. Their average amplitudes were differentially up- or down-regulated, depending on the precise timing of postsynaptic APs relative to EPSPs. These observations suggest that APs propagating back into dendrites serve to modify single active synaptic connections, depending on the pattern of electrical activity in the pre- and postsynaptic neurons.},
address = {Max-Planck-Institut f{\"{u}}r Medizinische Forschung, Abteilung Zellphysiologie, Jahnstrasse 29, D-69120 Heidelberg, Germany. bnmark@weizmann.weizmann.ac.il},
author = {Markram, H and L{\"{u}}bke, J and Frotscher, M and Sakmann, B},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Science 1997 Markram.pdf:pdf},
journal = {Science},
month = {jan},
number = {5297},
pages = {213--215},
title = {{Regulation of synaptic efficacy by coincidence of postsynaptic APs and EPSPs.}},
volume = {275},
year = {1997}
}
@article{Cooper2015,
author = {Cooper, Emily A and Norcia, Anthony M},
editor = {Einh{\"{a}}user, Wolfgang},
file = {:Users/hahosoya/Documents/Mendeley Desktop/PLoS Comp Biol 2015 Cooper.pdf:pdf},
journal = {PLoS Comput. Biol.},
month = {may},
number = {5},
pages = {e1004268},
title = {{Predicting Cortical Dark/Bright Asymmetries from Natural Image Statistics and Early Visual Transforms}},
volume = {11},
year = {2015}
}
@article{Doi2011,
abstract = {Robust coding has been proposed as a solution to the problem of minimizing decoding error in the presence of neural noise. Many real-world problems, however, have degradation in the input signal, not just in neural representations. This generalized problem is more relevant to biological sensory coding where internal noise arises from limited neural precision and external noise from distortion of sensory signal such as blurring and phototransduction noise. In this note, we show that the optimal linear encoder for this problem can be decomposed exactly into two serial processes that can be optimized separately. One is Wiener filtering, which optimally compensates for input degradation. The other is robust coding, which best uses the available representational capacity for signal transmission with a noisy population of linear neurons. We also present spectral analysis of the decomposition that characterizes how the reconstruction error is minimized under different input signal spectra, types and amounts of degradation, degrees of neural precision, and neural population sizes.},
address = {Center for Neural Science, New York University, New York, NY 10003, USA. edoi@cns.nyu.edu},
author = {Doi, Eizaburo and Lewicki, Michael S},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neural Computation 2011 Doi.pdf:pdf},
journal = {Neural Comput.},
month = {oct},
number = {10},
pages = {2498--2510},
title = {{Characterization of minimum error linear coding with sensory and neural noise.}},
volume = {23},
year = {2011}
}
@article{Gregor2013,
abstract = {... The complexity of sampling from a fully autoregressive single stochastic hidden layer DARN is O((nh + nx)2). If we omit the ... greater as each part of the decoder need only be com- puted once for DARN per datum, wheras deeper NADE- like ...  Deep  AutoRegressive  Networks  ... 
},
author = {Gregor, K and Danihelka, I and Mnih, A and Blundell, C},
file = {:Users/hahosoya/Documents/Mendeley Desktop/arXiv 2013 Gregor.pdf:pdf},
journal = {arXiv.org},
month = {jan},
title = {{Deep autoregressive networks}},
year = {2013}
}
@article{Chalk2018,
abstract = {A central goal in theoretical neuroscience is to predict the response properties of sensory neurons from first principles. To this end, "efficient coding" posits that sensory neurons encode maximal information about their inputs given internal constraints. There exist, however, many variants of efficient coding (e.g., redundancy reduction, different formulations of predictive coding, robust coding, sparse coding, etc.), differing in their regimes of applicability, in the relevance of signals to be encoded, and in the choice of constraints. It is unclear how these types of efficient coding relate or what is expected when different coding objectives are combined. Here we present a unified framework that encompasses previously proposed efficient coding models and extends to unique regimes. We show that optimizing neural responses to encode predictive information can lead them to either correlate or decorrelate their inputs, depending on the stimulus statistics; in contrast, at low noise, efficiently encoding the past always predicts decorrelation. Later, we investigate coding of naturalistic movies and show that qualitatively different types of visual motion tuning and levels of response sparsity are predicted, depending on whether the objective is to recover the past or predict the future. Our approach promises a way to explain the observed diversity of sensory neural responses, as due to multiple functional goals and constraints fulfilled by different cell types and/or circuits.},
address = {Department of Physical Sciences, Institute of Science and Technology Austria, 3400 Klosterneuburg, Austria; matthewjchalk@gmail.com.},
author = {Chalk, Matthew and Marre, Olivier and Tka{\v{c}}ik, Ga{\v{s}}per},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2EEEF24A-8A35-4713-AD6C-EED11B85563B.pdf:pdf},
journal = {Proc. Natl. Acad. Sci.},
month = {jan},
number = {1},
pages = {186--191},
title = {{Toward a unified theory of efficient, predictive, and sparse coding.}},
volume = {115},
year = {2018}
}
@article{Bi1998,
abstract = {In cultures of dissociated rat hippocampal neurons, persistent potentiation and depression of glutamatergic synapses were induced by correlated spiking of presynaptic and postsynaptic neurons. The relative timing between the presynaptic and postsynaptic spiking determined the direction and the extent of synaptic changes. Repetitive postsynaptic spiking within a time window of 20 msec after presynaptic activation resulted in long-term potentiation (LTP), whereas postsynaptic spiking within a window of 20 msec before the repetitive presynaptic activation led to long-term depression (LTD). Significant LTP occurred only at synapses with relatively low initial strength, whereas the extent of LTD did not show obvious dependence on the initial synaptic strength. Both LTP and LTD depended on the activation of NMDA receptors and were absent in cases in which the postsynaptic neurons were GABAergic in nature. Blockade of L-type calcium channels with nimodipine abolished the induction of LTD and reduced the extent of LTP. These results underscore the importance of precise spike timing, synaptic strength, and postsynaptic cell type in the activity-induced modification of central synapses and suggest that Hebb's rule may need to incorporate a quantitative consideration of spike timing that reflects the narrow and asymmetric window for the induction of synaptic modification.},
address = {Department of Biology, University of California at San Diego, La Jolla, California 92093, USA.},
author = {Bi, G Q and Poo, M M},
file = {:Users/hahosoya/Documents/Mendeley Desktop/The Journal of neuroscience 1998 Bi.pdf:pdf},
journal = {J. Neurosci.},
month = {dec},
number = {24},
pages = {10464--10472},
title = {{Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type.}},
volume = {18},
year = {1998}
}
@article{Barzelay2017,
author = {Barzelay, Oded and Furst, Miriam and Barak, Omri},
editor = {Bethge, Matthias},
file = {:Users/hahosoya/Documents/Mendeley Desktop/PLoS Comp Biol 2017 Barzelay.pdf:pdf},
journal = {PLoS Comput. Biol.},
month = {jan},
number = {1},
pages = {e1005338},
title = {{A New Approach to Model Pitch Perception Using Sparse Coding}},
volume = {13},
year = {2017}
}
@article{Toker2019,
author = {Toker, Daniel and Sommer, Friedrich T},
editor = {Marinazzo, Daniele},
file = {:Users/hahosoya/Documents/Mendeley Desktop/55528258-69A4-4D36-9C52-F4AF003C3DD5.pdf:pdf},
journal = {PLoS Comput. Biol.},
month = {feb},
number = {2},
pages = {e1006807--26},
title = {{Information integration in large brain networks}},
volume = {15},
year = {2019}
}
@article{Eslami2016,
abstract = {Abstract We present a framework for efficient inference in structured image models that explicitly reason about objects. We achieve this by performing probabilistic inference using a recurrent neural network that attends to scene elements and processes them one at a time. Crucially, the model itself learns to choose the appropriate number of inference steps. We use this scheme to learn to perform inference in partially specified 2D models (variable- ...
},
author = {Eslami, S M A and Heess, N and Weber, T and Tassa, Y},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Advances in neural {\ldots} 2016 Eslami.pdf:pdf},
journal = {Adv. neural  {\ldots}},
month = {jan},
title = {{Attend, infer, repeat: Fast scene understanding with generative models}},
year = {2016}
}
@article{Slonim2021,
abstract = {Artificial intelligence (AI) is defined as the ability of machines to perform tasks that are usually associated with intelligent beings. Argument and debate are fundamental capabilities of human intelligence, essential for a wide range of human activities, and common to all human societies. The development of computational argumentation technologies is therefore an important emerging discipline in AI research1. Here we present Project Debater, an autonomous debating system that can engage in a competitive debate with humans. We provide a complete description of the system's architecture, a thorough and systematic evaluation of its operation across a wide range of debate topics, and a detailed account of the system's performance in its public debut against three expert human debaters. We also highlight the fundamental differences between debating with humans as opposed to challenging humans in game competitions, the latter being the focus of classical ‘grand challenges' pursued by the AI research community over the past few decades. We suggest that such challenges lie in the ‘comfort zone' of AI, whereas debating with humans lies in a different territory, in which humans still prevail, and for which novel paradigms are required to make substantial progress.},
author = {Slonim, Noam and Bilu, Yonatan and Alzate, Carlos and Bar-Haim, Roy and Bogin, Ben and Bonin, Francesca and Choshen, Leshem and Cohen-Karlik, Edo and Dankin, Lena and Edelstein, Lilach and Ein-Dor, Liat and Friedman-Melamed, Roni and Gavron, Assaf and Gera, Ariel and Gleize, Martin and Gretz, Shai and Gutfreund, Dan and Halfon, Alon and Hershcovich, Daniel and Hoory, Ron and Hou, Yufang and Hummel, Shay and Jacovi, Michal and Jochim, Charles and Kantor, Yoav and Katz, Yoav and Konopnicki, David and Kons, Zvi and Kotlerman, Lili and Krieger, Dalia and Lahav, Dan and Lavee, Tamar and Levy, Ran and Liberman, Naftali and Mass, Yosi and Menczel, Amir and Mirkin, Shachar and Moshkowich, Guy and Ofek-Koifman, Shila and Orbach, Matan and Rabinovich, Ella and Rinott, Ruty and Shechtman, Slava and Sheinwald, Dafna and Shnarch, Eyal and Shnayderman, Ilya and Soffer, Aya and Spector, Artem and Sznajder, Benjamin and Toledo, Assaf and Toledo-Ronen, Orith and Venezian, Elad and Aharonov, Ranit},
file = {:Users/hahosoya/Documents/Mendeley Desktop/s41586-021-03215-w.pdf:pdf},
journal = {Nature},
number = {7850},
pages = {379--384},
pmid = {33731946},
title = {{An autonomous debating system}},
volume = {591},
year = {2021}
}
@article{Pillow2008,
author = {Pillow, Jonathan W and Shlens, Jonathon and Paninski, Liam and Sher, Alexander and Litke, Alan M and Chichilnisky, E J and Simoncelli, Eero P},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature 2008 Pillow.pdf:pdf},
journal = {Nature},
month = {jul},
number = {7207},
pages = {995--999},
title = {{Spatio-temporal correlations and visual signalling in a complete neuronal population}},
volume = {454},
year = {2008}
}
@article{Turner2019,
abstract = {Our knowledge of sensory processing has advanced dramatically in the last few decades, but this understanding remains far from complete, especially for stimuli with the large dynamic range and strong temporal and spatial correlations characteristic of natural visual inputs. Here we describe some of the issues that make understanding the encoding of natural images a challenge. We highlight two broad strategies for approaching this problem: a stimulus-oriented framework and a goal-oriented one. Different contexts can call for one framework or the other. Looking forward, recent advances, particularly those based in machine learning, show promise in borrowing key strengths of both frameworks and by doing so illuminating a path to a more comprehensive understanding of the encoding of natural stimuli.},
address = {Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA.},
author = {Turner, Maxwell H and {Sanchez Giraldo}, Luis Gonzalo and Schwartz, Odelia and Rieke, Fred},
file = {:Users/hahosoya/Documents/Mendeley Desktop/B624E243-8A1A-4581-9011-7FF275C07776.pdf:pdf},
journal = {Nat. Neurosci. Rev.},
month = {jan},
number = {1},
pages = {15--24},
title = {{Stimulus- and goal-oriented frameworks for understanding natural vision.}},
volume = {22},
year = {2019}
}
@article{Sedigh-Sarvestani2021,
abstract = {Retinotopic maps of many visual areas are thought to follow the fundamental principles described for the primary visual cortex (V1), where nearby points on the retina map to nearby points on the surface of V1, and orthogonal axes of the retinal surface are represented along orthogonal axes of the cortical surface. Here we demonstrate a striking departure from this mapping in the secondary visual area (V2) of the tree shrew best described as a sinusoidal transformation of the visual field. This sinusoidal topography is ideal for achieving uniform coverage in an elongated area like V2, as predicted by mathematical models designed for wiring minimization, and provides a novel explanation for periodic banded patterns of intra-cortical connections and functional response properties in V2 of tree shrews as well as several other species. Our findings suggest that cortical circuits flexibly implement solutions to sensory surface representation, with dramatic consequences for large-scale cortical organization.},
author = {Sedigh-Sarvestani, Madineh and Lee, Kuo-Sheng and Jaepel, Juliane and Satterfield, Rachel and Shultz, Nicole and Fitzpatrick, David},
file = {:Users/hahosoya/Documents/Mendeley Desktop/1-s2.0-S0896627321007261-main.pdf:pdf},
journal = {Neuron},
number = {24},
pages = {4068--4079.e6},
pmid = {34687665},
title = {{A sinusoidal transformation of the visual field is the basis for periodic maps in area V2}},
volume = {109},
year = {2021}
}
@article{Khaligh-Razavi2014,
author = {Khaligh-Razavi, Seyed-Mahdi and Kriegeskorte, Nikolaus},
file = {:Users/hahosoya/Documents/Mendeley Desktop/PLoS Comp Biol 2014 Khaligh-Razavi.pdf:pdf},
journal = {PLoS Comput. Biol.},
month = {nov},
number = {11},
pages = {e1003915},
title = {{Deep Supervised, but Not Unsupervised, Models May Explain IT Cortical Representation}},
volume = {10},
year = {2014}
}
@article{Logothetis1996,
author = {Logothetis, Nikos K and Leopold, David A and Sheinberg, David L},
file = {:Users/hahosoya/Documents/Mendeley Desktop/1996 Logothetis.pdf:pdf},
month = {jan},
title = {{What is rivalling during binocular rivalry?}},
year = {1996}
}
@article{Zhen2020,
abstract = {In this paper, we introduce variational semantic memory into meta-learning to acquire long-term knowledge for few-shot learning. The variational semantic memory accrues and stores semantic information for the probabilistic inference of class prototypes in a hierarchical Bayesian framework. The semantic memory is grown from scratch and gradually consolidated by absorbing information from tasks it experiences. By doing so, it is able to accumulate long-term, general knowledge that enables it to learn new concepts of objects. We formulate memory recall as the variational inference of a latent memory variable from addressed contents, which offers a principled way to adapt the knowledge to individual tasks. Our variational semantic memory, as a new long-term memory module, confers principled recall and update mechanisms that enable semantic information to be efficiently accrued and adapted for few-shot learning. Experiments demonstrate that the probabilistic modelling of prototypes achieves a more informative representation of object classes compared to deterministic vectors. The consistent new state-of-the-art performance on four benchmarks shows the benefit of variational semantic memory in boosting few-shot recognition.},
archivePrefix = {arXiv},
arxivId = {2010.10341},
author = {Zhen, Xiantong and Du, Yingjun and Xiong, Huan and Qiu, Qiang and Snoek, Cees G. M. and Shao, Ling},
eprint = {2010.10341},
file = {:Users/hahosoya/Documents/Mendeley Desktop/fe11961f18d3fbc6ed7eaff821ce0c83d3466053.pdf:pdf},
journal = {NeurIPS},
month = {oct},
number = {NeurIPS},
pages = {1--13},
title = {{Learning to Learn Variational Semantic Memory}},
year = {2020}
}
@article{Chettih2019,
abstract = {Nature, doi:10.1038/s41586-019-0997-6},
author = {Chettih, Selmaan N and Harvey, Christopher D},
file = {:Users/hahosoya/Documents/Mendeley Desktop/42E56D1F-CDF6-4CA9-A6B7-CB1EC7499C2D.pdf:pdf},
journal = {Nature},
month = {mar},
pages = {1--29},
title = {{Single-neuron perturbations reveal feature-specific competition in V1}},
year = {2019}
}
@book{Diedrichsen2017,
abstract = {Representational models specify how activity patterns in
populations of neurons (or, more generally, in multivariate
brain-activity measurements) relate to sensory stimuli, motor
responses, or cognitive processes. In an experimental context,
representational models can be defined as hypotheses about the
distribution of activity profiles across experimental conditions.
Currently, three different methods are being used to test such
hypotheses: encoding analysis, pattern component modeling (PCM),
and representational similarity analysis (RSA). Here we develop a
common mathematical framework for understanding the relationship
of these three methods, which share one core commonality: all
three evaluate the second moment of the distribution of activity
profiles, which determines the representational geometry, and
thus how well any feature can be decoded from population
activity. Using simulated data for three different experimental
designs, we compare the power of the methods to adjudicate
between competing representational models. PCM implements a
likelihood-ratio test and therefore provides the most powerful
test if its assumptions hold. However, the other two
approaches-when conducted appropriately-can perform similarly. In
encoding analysis, the linear model needs to be appropriately
regularized, which effectively imposes a prior on the activity
profiles. With such a prior, an encoding model specifies a
well-defined distribution of activity profiles. In RSA, the
unequal variances and statistical dependencies of the
dissimilarity estimates need to be taken into account to reach
near-optimal power in inference. The three methods render
different aspects of the information explicit (e.g.
single-response tuning in encoding analysis and
population-response representational dissimilarity in RSA) and
have specific advantages in terms of computational demands, ease
of use, and extensibility. The three methods are properly
construed as complementary components of a single data-analytical
toolkit for understanding neural representations on the basis of
multivariate brain-activity data.},
author = {Diedrichsen, J{\"{o}}rn and Kriegeskorte, Nikolaus},
booktitle = {PLoS Comput. Biol.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/7f6deb94598e7dc580aba4678ecc06c070e75ed2.pdf:pdf},
isbn = {1111111111},
number = {4},
pages = {1--33},
title = {{Representational models: A common framework for understanding encoding,}},
volume = {13},
year = {2017}
}
@article{DiMattina2019,
author = {DiMattina, Christopher and Baker, Curtis L},
editor = {Einh{\"{a}}user, Wolfgang},
file = {:Users/hahosoya/Documents/Mendeley Desktop/FCBE8E32-48C6-45EB-B215-0488D2F57B11.pdf:pdf},
journal = {PLoS Comput. Biol.},
month = {mar},
number = {3},
pages = {e1006829--41},
title = {{Modeling second-order boundary perception: A machine learning approach}},
volume = {15},
year = {2019}
}
@article{Friston2002,
abstract = {Self-supervised models of how the brain represents and categorises the causes of its sensory input can be divided into two classes: those that minimise the mutual information (i.e. redundancy) among evoked responses and those that minimise the prediction error. Although these models have similar goals, the way they are attained, and the functional architectures employed, can be fundamentally different. This review describes the two classes of models and their implications for the functional anatomy of sensory cortical hierarchies in the brain. We then consider how empirical evidence can be used to disambiguate between architectures that are sufficient for perceptual learning and synthesis. Most models of representational learning require prior assumptions about the distribution of sensory causes. Using the notion of empirical Bayes, we show that these assumptions are not necessary and that priors can be learned in a hierarchical context. Furthermore, we try to show that learning can be implemented in a biologically plausible way. The main point made in this review is that backward connections, mediating internal or generative models of how sensory inputs are caused, are essential if the process generating inputs cannot be inverted. Because these processes are dynamical in nature, sensory inputs correspond to a non-invertible nonlinear convolution of causes. This enforces an explicit parameterisation of generative models (i.e. backward connections) to enable approximate recognition and suggests that feedforward architectures, on their own, are not sufficient. Moreover, nonlinearities in generative models, that induce a dependence on backward connections, require these connections to be modulatory; so that estimated causes in higher cortical levels can interact to predict responses in lower levels. This is important in relation to functional asymmetries in forward and backward connections that have been demonstrated empirically. To ascertain whether backward influences are expressed functionally requires measurements of functional integration among brain systems. This review summarises approaches to integration in terms of effective connectivity and proceeds to address the question posed by the theoretical considerations above. In short, it will be shown that functional neuroimaging can be used to test for interactions between bottom-up and top-down inputs to an area. The conclusion of these studies points toward the prevalence of top-down influences and the plausibility of generative models of sensory brain function.},
address = {The Wellcome Department of Imaging Neuroscience, Institute of Neurology, University College London, 12 Queen Square, London, UK. k.friston@fil.ion.ucl.ac.uk},
author = {Friston, Karl},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Prog. Neurobiol. 2002 Friston.pdf:pdf},
journal = {Prog. Neurobiol.},
month = {oct},
number = {2},
pages = {113--143},
title = {{Functional integration and inference in the brain.}},
volume = {68},
year = {2002}
}
@article{Tzikas2008,
abstract = {Thomas Bayes (1701–1761), shown in the upper left corner of Figure 1, first discovered Bayes' theorem in a paper that was published in 1764 three years after his death, as the name suggests. However, Bayes, in his theorem, used uniform priors [1]. Pierre-Simon Laplace ( ...},
author = {Tzikas, D G and Likas, A C},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Signal Processing 2008 Tzikas.pdf:pdf},
journal = {Signal Process. {\ldots}},
month = {jan},
title = {{The variational approximation for Bayesian inference}},
year = {2008}
}
@article{Lee2006,
author = {Lee, Honglak and Battle, Alexis and Raina, Rajat and Ng, Andrew},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2006 Lee.pdf:pdf},
journal = {Adv. Neural Inf. Process. Syst.},
month = {jan},
pages = {801--808},
title = {{Efficient sparse coding algorithms}},
year = {2006}
}
@article{Qiu2007,
author = {Qiu, Fangtu T and von der Heydt, R{\"{u}}diger},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature neuroscience 2007 Qiu.pdf:pdf},
journal = {Nat. Neurosci.},
month = {feb},
number = {3},
pages = {283--284},
title = {{Neural representation of transparent overlay}},
volume = {10},
year = {2007}
}
@article{Jang2017,
abstract = {Categorical variables are a natural choice for representing discrete structure in the world. However, stochastic neural networks rarely use categorical latent variables due to the inability to backpropagate through samples. In this work, we present an efficient gradient estimator that replaces the non-differentiable sample from a categorical distribution with a differentiable sample from a novel Gumbel-Softmax distribution. This distribution has the essential property that it can be smoothly annealed into a categorical distribution. We show that our Gumbel-Softmax estimator outperforms state-of-the-art gradient estimators on structured output prediction and unsupervised generative modeling tasks with categorical latent variables, and enables large speedups on semi-supervised classification.},
archivePrefix = {arXiv},
arxivId = {1611.01144},
author = {Jang, Eric and Gu, Shixiang and Poole, Ben},
eprint = {1611.01144},
file = {:Users/hahosoya/Documents/Mendeley Desktop/c65d4545f110a98a39e9a2de7b5f596a243eb3d2.pdf:pdf},
journal = {5th Int. Conf. Learn. Represent. ICLR 2017 - Conf. Track Proc.},
pages = {1--13},
title = {{Categorical reparameterization with gumbel-softmax}},
year = {2017}
}
@article{Olshausen2001,
abstract = {In previous work (Olshausen & Field, 1997), we described a model of V1 simple-cells in terms of a linear generative model of images ( gure 1a). According to this model, images are described in terms of a linear superposition of basis functions plus noise: ... I(x; y) = X i ai i(x; y) + (x; ...},
author = {Olshausen, B A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2001 Olshausen.pdf:pdf},
journal = {Probabilistic Model. brain Percept. {\ldots}},
month = {jan},
title = {{Sparse codes and spikes}},
year = {2001}
}
@article{Gava2021,
abstract = {By investigating the topology of neuronal co-activity, we found that mnemonic information spans multiple operational axes in the mouse hippocampus network. High-activity principal cells form the core of each memory along a first axis, segregating spatial contexts and novelty. Low-activity cells join co-activity motifs across behavioral events and enable their crosstalk along two other axes. This reveals an organizational principle for continuous integration and interaction of hippocampal memories.},
author = {Gava, Giuseppe P. and McHugh, Stephen B. and Lef{\`{e}}vre, Laura and Lopes-dos-Santos, V{\'{i}}tor and Trouche, St{\'{e}}phanie and El-Gaby, Mohamady and Schultz, Simon R. and Dupret, David},
file = {:Users/hahosoya/Documents/Mendeley Desktop/s41593-021-00804-w.pdf:pdf},
journal = {Nat. Neurosci.},
number = {3},
pages = {326--330},
pmid = {33603228},
title = {{Integrating new memories into the hippocampal network activity space}},
volume = {24},
year = {2021}
}
@article{Nishimoto2011a,
author = {Nishimoto, S and Gallant, J L},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2011 Nishimoto.pdf:pdf},
journal = {J. Neurosci.},
month = {oct},
number = {41},
pages = {14551--14564},
title = {{A Three-Dimensional Spatiotemporal Receptive Field Model Explains Responses of Area MT Neurons to Naturalistic Movies}},
volume = {31},
year = {2011}
}
@article{Kenet2003,
abstract = {Spontaneous cortical activity--ongoing activity in the absence of intentional sensory input--has been studied extensively, using methods ranging from EEG (electroencephalography), through voltage sensitive dye imaging, down to recordings from single neurons. Ongoing cortical activity has been shown to play a critical role in development, and must also be essential for processing sensory perception, because it modulates stimulus-evoked activity, and is correlated with behaviour. Yet its role in the processing of external information and its relationship to internal representations of sensory attributes remains unknown. Using voltage sensitive dye imaging, we previously established a close link between ongoing activity in the visual cortex of anaesthetized cats and the spontaneous firing of a single neuron. Here we report that such activity encompasses a set of dynamically switching cortical states, many of which correspond closely to orientation maps. When such an orientation state emerged spontaneously, it spanned several hypercolumns and was often followed by a state corresponding to a proximal orientation. We suggest that dynamically switching cortical states could represent the brain's internal context, and therefore reflect or influence memory, perception and behaviour.},
address = {Department of Neurobiology, The Weizmann Institute of Science, Rehovot 76100, Israel. (tal@phy.ucsf.edu},
author = {Kenet, Tal and Bibitchkov, Dmitri and Tsodyks, Misha and Grinvald, Amiram and Arieli, Amos},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature 2003 Kenet.pdf:pdf},
journal = {Nature},
month = {oct},
number = {6961},
pages = {954--956},
title = {{Spontaneously emerging cortical representations of visual attributes.}},
volume = {425},
year = {2003}
}
@article{Mormann2017,
author = {Mormann, Florian and Kornblith, Simon and Cerf, Moran and Ison, Matias J and Kraskov, Alexander and Tran, Michelle and Knieling, Simeon and {Quian Quiroga}, Rodrigo and Koch, Christof and Fried, Itzhak},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proceedings of the National Academy of Sciences 2017 Mormann.pdf:pdf},
journal = {Proc. Natl. Acad. Sci.},
month = {jan},
number = {5},
pages = {1153--1158},
title = {{Scene-selective coding by single neurons in the human parahippocampal cortex}},
volume = {114},
year = {2017}
}
@article{Memisevic2010,
abstract = {To allow the hidden units of a restricted Boltzmann machine to model the transformation between two successive images, Memisevic and Hinton (2007) introduced three-way multiplicative interactions that use the intensity of a pixel in the first image as a multiplicative gain on a learned, symmetric weight between a pixel in the second image and a hidden unit. This creates cubically many parameters, which form a three-dimensional interaction tensor. We describe a low-rank approximation to this interaction tensor that uses a sum of factors, each of which is a three-way outer product. This approximation allows efficient learning of transformations between larger image patches. Since each factor can be viewed as an image filter, the model as a whole learns optimal filter pairs for efficiently representing transformations. We demonstrate the learning of optimal filter pairs from various synthetic and real image sequences. We also show how learning about image transformations allows the model to perform a simple visual analogy task, and we show how a completely unsupervised network trained on transformations perceives multiple motions of transparent dot patterns in the same way as humans.},
address = {Department of Computer Science, University of Toronto, Toronto M5S 3G4, Canada. roland@cs.toronto.edu},
author = {Memisevic, Roland and Hinton, Geoffrey E},
file = {:Users/hahosoya/Documents/Mendeley Desktop/CF9B5DD6-4ECC-4919-96A0-5D007D83AFD6.pdf:pdf},
journal = {Neural Comput.},
month = {jan},
number = {6},
pages = {1473--1492},
title = {{Learning to Represent Spatial Transformations with Factored Higher-Order Boltzmann Machines.}},
volume = {22},
year = {2010}
}
@article{ShieMannor2006,
abstract = {We address the problem of automatically constructing basis functions for linear approximation of the value function of a Markov Decision Process (MDP). Our work builds on results by Bertsekas and Casta{\~{n}}on (1989) who proposed a method for automatically aggregating states to ...},
author = {{Shie Mannor}, Doina Precup},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2006 Shie Mannor.pdf:pdf},
month = {jan},
title = {{Automatic basis function construction for approximate dynamic programming and reinforcement learning}},
year = {2006}
}
@article{DAvossa2007,
abstract = {Many neurons in the monkey visual extrastriate cortex have receptive fields that are affected by gaze direction. In humans, psychophysical studies suggest that motion signals may be encoded in a spatiotopic fashion. Here we use functional magnetic resonance imaging to study spatial selectivity in the human middle temporal cortex (area MT or V5), an area that is clearly implicated in motion perception. The results show that the response of MT is modulated by gaze direction, generating a spatial selectivity based on screen rather than retinal coordinates. This area could be the neurophysiological substrate of the spatiotopic representation of motion signals.},
address = {Facolt{\`{a}} di Psicologia, Universit{\`{a}} Vita-Salute San Raffaele, Via Olgettina 58, 20132 Milan, Italy.},
author = {D'Avossa, Giovanni and Tosetti, Michela and Crespi, Sofia and Biagi, Laura and Burr, David C and Morrone, Maria Concetta},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature neuroscience 2007 d'Avossa-1.pdf:pdf},
journal = {Nat. Neurosci.},
month = {feb},
number = {2},
pages = {249--255},
title = {{Spatiotopic selectivity of BOLD responses to visual motion in human area MT.}},
volume = {10},
year = {2007}
}
@article{Hermann2017,
abstract = {We are increasingly surrounded by artificially intelligent technology that takes decisions and executes actions on our behalf. This creates a pressing need for general means to communicate with, instruct and guide artificial agents, with human language the most compelling means for such communication. To achieve this in a scalable fashion, agents must be able to relate language to the world and to actions; that is, their understanding of language must be grounded and embodied. However, learning grounded language is a notoriously challenging problem in artificial intelligence research. Here we present an agent that learns to interpret language in a simulated 3D environment where it is rewarded for the successful execution of written instructions. Trained via a combination of reinforcement and unsupervised learning, and beginning with minimal prior knowledge, the agent learns to relate linguistic symbols to emergent perceptual representations of its physical surroundings and to pertinent sequences of actions. The agent's comprehension of language extends beyond its prior experience, enabling it to apply familiar language to unfamiliar situations and to interpret entirely novel instructions. Moreover, the speed with which this agent learns new words increases as its semantic knowledge grows. This facility for generalising and bootstrapping semantic knowledge indicates the potential of the present approach for reconciling ambiguous natural language with the complexity of the physical world.},
archivePrefix = {arXiv},
arxivId = {1706.06551},
author = {Hermann, Karl Moritz and Hill, Felix and Green, Simon and Wang, Fumin and Faulkner, Ryan and Soyer, Hubert and Szepesvari, David and Czarnecki, Wojciech Marian and Jaderberg, Max and Teplyashin, Denis and Wainwright, Marcus and Apps, Chris and Hassabis, Demis and Blunsomy, Phil},
eprint = {1706.06551},
file = {:Users/hahosoya/Documents/Mendeley Desktop/bab3498d1c50b82b82c0c1bb962d488f0b3041fc.pdf:pdf},
journal = {arXiv},
pages = {1--22},
title = {{Grounded language learning in a simulated 3d world}},
year = {2017}
}
@article{Cox2015,
author = {Cox, P H and Riesenhuber, M},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2015 Cox.pdf:pdf},
journal = {J. Neurosci.},
month = {oct},
number = {42},
pages = {14148--14159},
title = {{There Is a "U" in Clutter: Evidence for Robust Sparse Codes Underlying Clutter Tolerance in Human Vision}},
volume = {35},
year = {2015}
}
@article{Cadieu2007,
author = {Cadieu, C and Kouh, M and Pasupathy, A and Connor, C E and Riesenhuber, M and Poggio, T},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neurophysiology 2007 Cadieu.pdf:pdf},
journal = {J. Neurophysiol.},
month = {sep},
number = {3},
pages = {1733--1750},
title = {{A Model of V4 Shape Selectivity and Invariance}},
volume = {98},
year = {2007}
}
@inproceedings{Huang2016,
abstract = {Abstract: We present a generative model of images based on layering, in which image layers are individually generated, then composited from front to back. We are thus able to factor the appearance of an image into the appearance of individual objects within the image---and additionally for each individual object, we can factor content from pose. Unlike prior work on layered models, we learn a shape prior for each object/layer, allowing the ...
},
author = {Huang, J and Murphy, K},
booktitle = {Int. Conf. Learn. Represent. Work.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/arXiv 2015 Huang.pdf:pdf},
month = {jan},
title = {{Efficient inference in occlusion-aware generative models of images}},
year = {2016}
}
@article{Lee2003a,
author = {Lee, Tai Sing and Mumford, David},
file = {:Users/hahosoya/Documents/Mendeley Desktop/J. Opt. Soc. Am. A 2003 Lee.pdf:pdf},
journal = {J. Opt. Soc. Am. A},
month = {jan},
number = {7},
pages = {1434},
title = {{Hierarchical Bayesian inference in the visual cortex}},
volume = {20},
year = {2003}
}
@article{Hermann2015,
author = {Hermann, P and Banko, E M and Gal, V and Vidnyanszky, Z},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2015 Hermann.pdf:pdf},
journal = {J. Neurosci.},
month = {may},
number = {18},
pages = {7165--7173},
title = {{Neural Basis of Identity Information Extraction from Noisy Face Images}},
volume = {35},
year = {2015}
}
@article{Desimone1987,
author = {Desimone, Robert and Schein, Stanley J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neurophysiology 1987 Desimone.pdf:pdf},
journal = {J. Neurophysiol.},
month = {jan},
number = {3},
pages = {835--868},
title = {{Visual properties of neurons in area V4 of the macaque: sensitivity to stimulus form}},
volume = {57},
year = {1987}
}
@article{Karklin2005,
abstract = {Capturing statistical regularities in complex, high-dimensional data is an important problem in 
machine learning and signal processing. Models such as principal component analysis
(PCA) and independent compo- nent analysis (ICA) make few assumptions about the  ...},
author = {Karklin, Yan and Lewicki, Michael S},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neural Computation 2005 Karklin.pdf:pdf},
journal = {Neural Comput.},
month = {jan},
title = {{A hierarchical Bayesian model for learning nonlinear statistical regularities in nonstationary natural signals}},
year = {2005}
}
@article{Lewis2011,
abstract = {Sleep enhances integration across multiple stimuli, abstraction of general rules, insight into hidden solutions and false memory formation. Newly learned information is better assimilated if compatible with an existing cognitive framework or schema. This article proposes a mechanism by which the reactivation of newly learned memories during sleep could actively underpin both schema formation and the addition of new knowledge to existing schemata. Under this model, the overlapping replay of related memories selectively strengthens shared elements. Repeated reactivation of memories in different combinations progressively builds schematic representations of the relationships between stimuli. We argue that this selective strengthening forms the basis of cognitive abstraction, and explain how it facilitates insight and false memory formation. {\textcopyright} 2011 Elsevier Ltd.},
author = {Lewis, Penelope A. and Durrant, Simon J.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/3a8cc04d591c9e1c69f8bc9df9bdc5d23cd13ae5.pdf:pdf},
journal = {Trends Cogn. Sci.},
number = {8},
pages = {343--351},
pmid = {21764357},
title = {{Overlapping memory replay during sleep builds cognitive schemata}},
volume = {15},
year = {2011}
}
@article{Gregor2015,
abstract = {Abstract: This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural network architecture for image generation. DRAW networks combine a novel spatial attention mechanism that mimics the foveation of the human eye, with a sequential variational auto- encoding framework that allows for the iterative construction of complex images. The system substantially improves on the state of the art for generative models on MNIST, and, when ...
},
author = {Gregor, K and Danihelka, I and Graves, A and Rezende, D J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/arXiv 2015 Gregor.pdf:pdf},
journal = {arXiv.org},
month = {jan},
title = {{DRAW: A recurrent neural network for image generation}},
year = {2015}
}
@article{VandenHurk2017,
author = {van den Hurk, Job and {Van Baelen}, Marc and {Op de Beeck}, Hans P},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proceedings of the National Academy of Sciences 2017 van den Hurk-2.pdf:pdf},
journal = {Proc. Natl. Acad. Sci.},
month = {may},
number = {22},
pages = {E4501--E4510},
title = {{Development of visual category selectivity in ventral visual cortex does not require visual experience}},
volume = {114},
year = {2017}
}
@article{Spratling2008,
abstract = {Attention acts, through cortical feedback pathways, to enhance the response of cells encoding expected or predicted information. Such observations are inconsistent with the predictive coding theory of cortical function which proposes that feedback acts to suppress information predicted by higher-level cortical regions. Despite this discrepancy, this article demonstrates that the predictive coding model can be used to simulate a number of the effects of attention. This is achieved via a simple mathematical rearrangement of the predictive coding model, which allows it to be interpreted as a form of biased competition model. Nonlinear extensions to the model are proposed that enable it to explain a wider range of data.},
address = {Division of Engineering, King's College, London, UK. michael.spratling@kcl.ac.uk <michael.spratling@kcl.ac.uk>},
author = {Spratling, M W},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Vision Research 2008 Spratling.pdf:pdf},
journal = {Vision Res.},
month = {jun},
number = {12},
pages = {1391--1408},
title = {{Predictive coding as a model of biased competition in visual attention.}},
volume = {48},
year = {2008}
}
@book{Poulter2021,
abstract = {Successfully navigating in physical or semantic space requires a neural representation of allocentric (map-based) vectors to boundaries, objects and goals. Cognitive processes such as path-planning and imagination entail the recall of vector representations, but evidence of neuron-level memory for allocentric vectors has been lacking. Here, we describe a novel neuron type, vector trace cell (VTC), whose firing generates a new vector field when a cue is encountered and a ‘trace' version of that field for hours after cue removal. VTCs are concentrated in subiculum, distal to CA1. Compared to non-trace cells, VTCs fire at further distances from cues and exhibit earlier-going shifts in preferred theta phase in response to newly introduced cues, which demonstrates a theta-linked neural substrate for memory encoding. VTCs suggest a vector-based model of computing spatial relationships between an agent and multiple spatial objects, or between different objects, freed from the constraints of direct perception of those objects.},
author = {Poulter, Steven and Lee, Sang Ah and Dachtler, James and Wills, Thomas J. and Lever, Colin},
booktitle = {Nat. Neurosci.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/s41593-020-00761-w.pdf:pdf},
isbn = {4159302000761},
number = {2},
pages = {266--275},
pmid = {33349710},
title = {{Vector trace cells in the subiculum of the hippocampal formation}},
volume = {24},
year = {2021}
}
@article{Petersen2006,
abstract = {What is this? These pages are a collection of facts (identities, approxima- tions, inequalities, 
relations, ...) about matrices and matters relating to them. It is collected in this form for the convenience
of anyone who wants a quick desktop reference .  ... Disclaimer: The identities,  ...},
author = {Petersen, K B and Pedersen, M S},
file = {:Users/hahosoya/Documents/Mendeley Desktop/orbit.dtu.dk 2006 Petersen.pdf:pdf},
journal = {orbit.dtu.dk},
month = {jan},
title = {{The matrix cookbook}},
year = {2006}
}
@article{Kobatake1994,
abstract = {Abstract 1. To infer relative roles of cortical areas at different stages of the ventral visual pathway, we quantitatively examined visual responses of cells in V2, V4, the posterior part of the inferotemporal cortex (posterior IT), and the anterior part of the inferotemporal cortex ( ...},
author = {Kobatake, E and et Al},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neurophysiology 1994 Kobatake.pdf:pdf},
journal = {J. Neurophysiol.},
month = {jan},
title = {{Neuronal selectivities to complex object features in the ventral visual pathway of the macaque cerebral cortex}},
year = {1994}
}
@article{Pasupathy2002,
author = {Pasupathy, Anitha and Connor, Charles E},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature neuroscience 2002 Pasupathy-1.pdf:pdf},
journal = {Nat. Neurosci.},
month = {nov},
number = {12},
pages = {1332--1338},
title = {{Population coding of shape in area V4}},
volume = {5},
year = {2002}
}
@article{Rochefort2011,
abstract = {Previous studies of the ferret visual cortex indicate that the development of direction selectivity requires visual experience. Here, we used two-photon calcium imaging to study the development of direction selectivity in layer 2/3 neurons of the mouse visual cortex in vivo. Surprisingly, just after eye opening nearly all orientation-selective neurons were also direction selective. During later development, the number of neurons responding to drifting gratings increased in parallel with the fraction of neurons that were orientation, but not direction, selective. Our experiments demonstrate that direction selectivity develops normally in dark-reared mice, indicating that the early development of direction selectivity is independent of visual experience. Furthermore, remarkable functional similarities exist between the development of direction selectivity in cortical neurons and the previously reported development of direction selectivity in the mouse retina. Together, these findings provide strong evidence that the development of orientation and direction selectivity in the mouse brain is distinctly different from that in ferrets.},
address = {Institute of Neuroscience and Center for Integrated Protein Science, Technical University Munich, Biedersteinerstrasse 29, 80802 Munich, Germany.},
author = {Rochefort, Nathalie L and Narushima, Madoka and Grienberger, Christine and Marandi, Nima and Hill, Daniel N and Konnerth, Arthur},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neuron 2011 Rochefort.pdf:pdf},
journal = {Neuron},
month = {aug},
number = {3},
pages = {425--432},
title = {{Development of direction selectivity in mouse cortical neurons.}},
volume = {71},
year = {2011}
}
@article{Evtimova2017,
abstract = {Inspired by previous work on emergent communication in referential games, we propose a novel multi-modal, multi-step referential game, where the sender and receiver have access to distinct modalities of an object, and their information exchange is bidirectional and of arbitrary duration. The multi-modal multi-step setting allows agents to develop an internal communication significantly closer to natural language, in that they share a single set of messages, and that the length of the conversation may vary according to the difficulty of the task. We examine these properties empirically using a dataset consisting of images and textual descriptions of mammals, where the agents are tasked with identifying the correct object. Our experiments indicate that a robust and efficient communication protocol emerges, where gradual information exchange informs better predictions and higher communication bandwidth improves generalization.},
archivePrefix = {arXiv},
arxivId = {1705.10369},
author = {Evtimova, Katrina and Drozdov, Andrew and Kiela, Douwe and Cho, Kyunghyun},
eprint = {1705.10369},
file = {:Users/hahosoya/Documents/Mendeley Desktop/d541a3a5c4a57b681a0056a0e9004cb160f6fad4.pdf:pdf},
journal = {arXiv},
pages = {1--13},
title = {{Emergent communication in a multi-modal, multi-step referential game}},
year = {2017}
}
@article{Pospisil2018,
abstract = {Deep networks provide a potentially rich interconnection between neuroscientific and artificial approaches to understanding visual intelligence, but the relationship between artificial and neural representations of complex visual form has not been elucidated at the level of single-unit selectivity. Taking the approach of an electrophysiologist to characterizing single CNN units, we found many units exhibit translation-invariant boundary curvature selectivity approaching that of exemplar neurons in the primate mid-level visual area V4. For some V4-like units, particularly in middle layers, the natural images that drove them best were qualitatively consistent with selectivity for object boundaries. Our results identify a novel image-computable model for V4 boundary curvature selectivity and suggest that such a representation may begin to emerge within an artificial network trained for image categorization, even though boundary information was not provided during training. This raises the possibility that single-unit selectivity in CNNs will become a guide for understanding sensory cortex.},
author = {Pospisil, Dean A. and Pasupathy, Anitha and Bair, Wyeth},
file = {:Users/hahosoya/Documents/Mendeley Desktop/6d3cceeabb05160a5632be3300861ac4bd3e3f5a.pdf:pdf},
journal = {Elife},
pages = {1--31},
title = {{'Artiphysiology' reveals V4-like shape tuning in a deep network trained for image classification}},
volume = {7},
year = {2018}
}
@article{Ko2011,
abstract = {Neuronal connectivity is fundamental to information processing in the brain. Therefore, understanding the mechanisms of sensory processing requires uncovering how connection patterns between neurons relate to their function. On a coarse scale, long-range ...},
author = {Ko, Ho and Hofer, Sonja B and Pichler, Bruno and Buchanan, Katherine A and Sj{\"{o}}str{\"{o}}m, P Jesper and Mrsic-Flogel, Thomas D},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature 2011 Ko.pdf:pdf},
journal = {Nature},
month = {apr},
number = {7345},
pages = {87--91},
title = {{Functional specificity of local synaptic connections in neocortical networks}},
volume = {473},
year = {2011}
}
@article{Weliky1999,
abstract = {The properties of spontaneous activity in the developing visual pathway beyond the retina are unknown. Multielectrode recordings in the lateral geniculate nucleus (LGN) of awake behaving ferrets, before eye opening, revealed patterns of spontaneous activity that reflect a ...},
author = {Weliky, M},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Science 1999 Weliky.pdf:pdf},
journal = {Science},
month = {jul},
number = {5427},
pages = {599--604},
title = {{Correlational Structure of Spontaneous Neuronal Activity in the Developing Lateral Geniculate Nucleus in Vivo}},
volume = {285},
year = {1999}
}
@article{Salinas2000,
abstract = {... This interpretation exemplifies one of the major theoretical advantages of gain fields, namely their ... In the case of gain - modulated neurons, equating a cortical area with a single ... Although we have discussed gain  modulation that is multiplicative, as in (1), computationally this is not ... 
},
author = {Salinas, E and Thier, P},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neuron 2000 Salinas.pdf:pdf},
journal = {Neuron},
month = {jan},
title = {{Gain modulation: a major computational principle of the central nervous system}},
year = {2000}
}
@article{Deneve2008a,
abstract = {Many perceptual and motor tasks performed by the central nervous system are probabilistic in 
nature, and can be described in a Bayesian framework (Kording & Wolpert, 2004; Knill &
Richards, 1996). According to this theory, one probability is assigned to all possible  ...},
author = {Deneve, S},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neural Computation 2008 Deneve-2.pdf:pdf},
journal = {Neural Comput.},
month = {jan},
title = {{Bayesian spiking neurons I: inference}},
year = {2008}
}
@article{Orban2016,
abstract = {Neuron, 92 (2016) 530-543. doi:10.1016/j.neuron.2016.09.038},
author = {Orb{\'{a}}n, Gergo and Berkes, Pietro and Fiser, J{\'{o}}zsef and Lengyel, M{\'{a}}t{\'{e}}},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neuron 2016 Orb{\'{a}}n.pdf:pdf},
journal = {Neuron},
month = {oct},
number = {2},
pages = {530--543},
title = {{Neural Variability and Sampling-Based Probabilistic Representations in the Visual Cortex}},
volume = {92},
year = {2016}
}
@article{Willmore2011,
abstract = {Theoretical studies of mammalian cortex argue that efficient neural codes should be sparse. However, theoretical and experimental studies have used different definitions of the term "sparse" leading to three assumptions about the nature of sparse codes. First, codes that have high lifetime sparseness require few action potentials. Second, lifetime-sparse codes are also population-sparse. Third, neural codes are optimized to maximize lifetime sparseness. Here, we examine these assumptions in detail and test their validity in primate visual cortex. We show that lifetime and population sparseness are not necessarily correlated and that a code may have high lifetime sparseness regardless of how many action potentials it uses. We measure lifetime sparseness during presentation of natural images in three areas of macaque visual cortex, V1, V2, and V4. We find that lifetime sparseness does not increase across the visual hierarchy. This suggests that the neural code is not simply optimized to maximize lifetime sparseness. We also find that firing rates during a challenging visual task are higher than theoretical values based on metabolic limits and that responses in V1, V2, and V4 are well-described by exponential distributions. These findings are consistent with the hypothesis that neurons are optimized to maximize information transmission subject to metabolic constraints on mean firing rate.},
address = {Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom. benjamin.willmore@dpag.ox.ac.uk},
author = {Willmore, Ben D B and Mazer, James A and Gallant, Jack L},
file = {:Users/hahosoya/Documents/Mendeley Desktop/D21B017C-0891-42FA-8033-E64E0DBEA025.pdf:pdf},
journal = {J. Neurophysiol.},
month = {jun},
number = {6},
pages = {2907--2919},
title = {{Sparse coding in striate and extrastriate visual cortex}},
volume = {105},
year = {2011}
}
@article{Greff2019,
abstract = {Human perception is structured around objects which form the basis for our higher-level cognition and impressive systematic generalization abilities. Yet most work on representation learning focuses on feature learning without even considering multiple objects, or treats segmentation as an (often supervised) preprocessing step. Instead, we argue for the importance of learning to segment and represent objects jointly. We demonstrate that, starting from the simple assumption that a scene is composed of multiple entities, it is possible to learn to segment images into interpretable objects with disentangled representations. Our method learns -- without supervision -- to inpaint occluded parts, and extrapolates to scenes with more objects and to unseen objects with novel feature combinations. We also show that, due to the use of iterative variational inference, our system is able to learn multi-modal posteriors for ambiguous inputs and extends naturally to sequences.},
archivePrefix = {arXiv},
arxivId = {1903.00450},
author = {Greff, Klaus and Kaufman, Rapha{\"{e}}l Lopez and Kabra, Rishabh and Watters, Nick and Burgess, Chris and Zoran, Daniel and Matthey, Loic and Botvinick, Matthew and Lerchner, Alexander},
eprint = {1903.00450},
file = {:Users/hahosoya/Documents/Mendeley Desktop/aed278da68523c6f58aeabc12ba0deac4d5f7398.pdf:pdf},
isbn = {9781510886988},
journal = {36th Int. Conf. Mach. Learn. ICML 2019},
month = {mar},
title = {{Multi-Object Representation Learning with Iterative Variational Inference}},
year = {2019}
}
@article{White1998,
abstract = {20. Fraser, AG et al. Functional genomic analysis of C. elegans chromosome I by systematic 
RNA interference. Nature 408, 325±330 (2000). 21. Smith, V., Chou, KN, Lashkari, D.,
Botstein, D. & Brown, PO Functional analysis of the genes of yeast chromosome V by  ...},
author = {White, L E and et Al},
file = {:Users/hahosoya/Documents/Mendeley Desktop/evolution 1998 White.pdf:pdf},
journal = {Evolution (N. Y).},
month = {jan},
title = {{The contribution of sensory experience to the maturation of orientation selectivity in ferret visual cortex}},
year = {1998}
}
@article{Wu2018,
abstract = {We present an end-to-end trained memory system that quickly adapts to new data and generates samples like them. Inspired by Kanerva's sparse distributed memory, it has a robust distributed reading and writing mechanism. The memory is analytically tractable, which enables optimal on-line compression via a Bayesian update-rule. We formulate it as a hierarchical conditional generative model, where memory provides a rich data-dependent prior distribution. Consequently, the top-down memory and bottom-up perception are combined to produce the code representing an observation. Empirically, we demonstrate that the adaptive memory significantly improves generative models trained on both the Omniglot and CIFAR datasets. Compared with the Differentiable Neural Computer (DNC) and its variants, our memory model has greater capacity and is significantly easier to train.},
archivePrefix = {arXiv},
arxivId = {1804.01756},
author = {Wu, Yan and Wayne, Greg and Graves, Alex and Lillicrap, Timothy},
eprint = {1804.01756},
file = {:Users/hahosoya/Documents/Mendeley Desktop/de04354aa7f2d32ebecbdb4b10b7d597c1f5afc8.pdf:pdf},
journal = {arXiv},
pages = {1--16},
title = {{The kanerva machine: A generative distributed memory}},
year = {2018}
}
@article{Geirhos2019,
author = {Geirhos, Robert and Rubisch, Patricia and Michaelis, Claudio and Bethge, Matthias and Wichmann, Felix A and Brendel, Wieland},
file = {:Users/hahosoya/Documents/Mendeley Desktop/99CDCE9B-0D96-41FB-8D4C-2C1DAE66D183.pdf:pdf},
journal = {ICLR},
month = {jan},
title = {{ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness.}},
year = {2019}
}
@article{Schmolesky1998,
abstract = {The onset latencies of single-unit responses evoked by flashing visual stimuli were measured in the parvocellular (P) and magnocellular (M) layers of the dorsal lateral geniculate nucleus (LGNd) and in cortical visual areas V1, V2, V3, V4, middle temporal area (MT), medial superior temporal area (MST), and in the frontal eye field (FEF) in individual anesthetized monkeys. Identical procedures were carried out to assess latencies in each area, often in the same monkey, thereby permitting direct comparisons of timing across areas. This study presents the visual flash-evoked latencies for cells in areas where such data are common (V1 and V2), and are therefore a good standard, and also in areas where such data are sparse (LGNd M and P layers, MT, V4) or entirely lacking (V3, MST, and FEF in anesthetized preparation). Visual-evoked onset latencies were, on average, 17 ms shorter in the LGNd M layers than in the LGNd P layers. Visual responses occurred in V1 before any other cortical area. The next wave of activation occurred concurrently in areas V3, MT, MST, and FEF. Visual response latencies in areas V2 and V4 were progressively later and more broadly distributed. These differences in the time course of activation across the dorsal and ventral streams provide important temporal constraints on theories of visual processing.},
address = {Department of Neurobiology and Anatomy, University of Utah College of Medicine, Salt Lake City, Utah 84132, USA.},
author = {Schmolesky, M T and Wang, Y and Hanes, D P and Thompson, K G and Leutgeb, S and Schall, J D and Leventhal, A G},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neurophysiology 1998 Schmolesky.pdf:pdf},
journal = {J. Neurophysiol.},
month = {jun},
number = {6},
pages = {3272--3278},
title = {{Signal timing across the macaque visual system.}},
volume = {79},
year = {1998}
}
@article{Kumar2017,
author = {Kumar, Satwant and Popivanov, Ivo D and Vogels, Rufin},
file = {:Users/hahosoya/Documents/Mendeley Desktop/E9CF8165-3F79-4F2F-ACB9-717FFB7AA573.pdf:pdf},
journal = {Cereb. Cortex},
month = {nov},
number = {Pt 2},
pages = {1--15},
title = {{Transformation of Visual Representations Across Ventral Stream Body-selective Patches}},
volume = {102},
year = {2017}
}
@inproceedings{Oizumi2016a,
abstract = {Abstract Assessment of causal influences is a ubiquitous and important subject across diverse research fields. Drawn from consciousness studies, integrated information is a measure that defines integration as the degree of causal influences among elements. Whereas pairwise causal influences between elements can be quantified with existing methods, quantifying multiple influences among many elements poses two major ...
},
author = {Oizumi, M and Tsuchiya, N and Amari, S},
booktitle = {Proc. {\ldots}},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2016 Oizumi.pdf:pdf},
month = {jan},
title = {{Unified framework for information integration based on information geometry}},
year = {2016}
}
@article{Chapman2000,
abstract = {Neuronal activity is necessary for the normal development of visual cortical cell receptive fields. When neuronal activity is blocked, cortical cells fail to develop normal ocular dominance and orientation selectivity. Patterned activity has been shown to play an instructive, rather than merely permissive, role in the segregation of geniculocortical afferents into ocular dominance columns. To test whether normal patterns of activity are necessary to instruct the development of cortical orientation selectivity, we studied ferrets raised without ON-center retinal ganglion cell activity. The ON-center blockade was produced by daily intravitreal injections of DL-2-amino-4-phosphonobutyric acid (APB). Effects of this treatment on the development of orientation selectivity in primary visual cortex were assessed using extracellular electrode recordings and optical imaging. In animals raised with an ON-center blockade starting after visual cortical cells are visually driven but still poorly tuned for orientation, cortical cell responsivity was maintained, but no maturation of orientation selectivity was seen. No recovery of orientation tuning was seen in animals treated with APB during the normal period of orientation development and then allowed several months of development without treatment. These results suggest that patterns of neuronal activity carried in the separate ON- and OFF-center visual pathways are necessary for the development of orientation selectivity in visual cortical neurons of the ferret and that there is a critical period for this development.},
address = {Center for Neuroscience, University of California, Davis, California 95616, USA. bxchapman@ucdavis.edu},
author = {Chapman, B and G{\"{o}}decke, I},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2000 Chapman.pdf:pdf},
journal = {J. Neurosci.},
month = {mar},
number = {5},
pages = {1922--1930},
title = {{Cortical cell orientation selectivity fails to develop in the absence of ON-center retinal ganglion cell activity.}},
volume = {20},
year = {2000}
}
@article{Wallis1997,
abstract = {Neurophysiological evidence is described, showing that some neurons in the macaque temporal cortical visual areas have responses that are invariant with respect to the position, size and view of faces and objects, and that these neurons show rapid processing and rapid learning. A theory is then described of how such invariant representations may be produced in a hierarchically organized set of visual cortical areas with convergent connectivity. The theory proposes that neurons in these visual areas use a modified Hebb synaptic modification rule with a short-term memory trace to capture whatever can be captured at each stage that is invariant about objects as the object changes in retinal position, size, rotation and view. Simulations are then described which explore the operation of the architecture. The simulations show that such a processing system can build invariant representations of objects.},
author = {Wallis, Guy and Rolls, Edmund T.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/pnb97.pdf:pdf},
journal = {Prog. Neurobiol.},
month = {feb},
number = {2},
pages = {167--194},
title = {{INVARIANT FACE AND OBJECT RECOGNITION IN THE VISUAL SYSTEM}},
volume = {51},
year = {1997}
}
@article{Balle2015,
abstract = {We introduce a parametric nonlinear transformation that is well-suited for Gaussianizing data from natural images. The data are linearly transformed, and each component is then normalized by a pooled activity measure, computed by exponentiating a weighted sum of rectified and exponentiated components and a constant. We optimize the parameters of the full transformation (linear transform, exponents, weights, constant) over a database of natural images, directly minimizing the negentropy of the responses. The optimized transformation substantially Gaussianizes the data, achieving a significantly smaller mutual information between transformed components than alternative methods including ICA and radial Gaussianization. The transformation is differentiable and can be efficiently inverted, and thus induces a density model on images. We show that samples of this model are visually similar to samples of natural image patches. We demonstrate the use of the model as a prior probability density that can be used to remove additive noise. Finally, we show that the transformation can be cascaded, with each layer optimized using the same Gaussianization objective, thus offering an unsupervised method of optimizing a deep network architecture.},
author = {Ball{\'{e}}, Johannes and Laparra, Valero and Simoncelli, Eero P},
file = {:Users/hahosoya/Documents/Mendeley Desktop/2016.pdf:pdf},
journal = {CoRR},
month = {jan},
title = {{Density Modeling of Images using a Generalized Normalization Transformation.}},
volume = {cs.LG},
year = {2015}
}
@article{Bhagavatula2019,
abstract = {Abductive reasoning is inference to the most plausible explanation. For example, if Jenny finds her house in a mess when she returns from work, and remembers that she left a window open, she can hypothesize that a thief broke into her house and caused the mess, as the most plausible explanation. While abduction has long been considered to be at the core of how people interpret and read between the lines in natural language Hobbs et al. (1988), there has been relatively little NLP research in support of abductive natural language inference. We present the first study that investigates the viability of language-based abductive reasoning. We conceptualize a new task of Abductive NLI and introduce a challenge dataset, ART, that consists of over 20k commonsense narrative contexts and 200k explanations, formulated as multiple choice questions for easy automatic evaluation. We establish comprehensive baseline performance on this task based on state-of-the-art NLI and language models, which leads to 68.9%accuracy, well below human performance (91.4%). Our analysis leads to new insights into the types of reasoning that deep pre-trained language models fail to perform-despite their strong performance on the related but fundamentally different task of entailment NLI-pointing to interesting avenues for future research.},
archivePrefix = {arXiv},
arxivId = {1908.05739},
author = {Bhagavatula, Chandra and Bras, Ronan Le and Malaviya, Chaitanya and Sakaguchi, Keisuke and Holtzman, Ari and Rashkin, Hannah and Downey, Doug and Yih, Scott Wen Tauu and Choi, Yejin},
eprint = {1908.05739},
file = {:Users/hahosoya/Documents/Mendeley Desktop/766d1f94f9fe05c786066ff62cb7b34d3742cec9.pdf:pdf},
journal = {arXiv},
pages = {1--18},
title = {{Abductive commonsense reasoning}},
year = {2019}
}
@article{Alonso1998,
abstract = {In the cat primary visual cortex, neurons are classified into the two main categories of simple cells and complex cells based on their response properties. According to the hierarchical model, complex receptive fields derive from convergent inputs of simple cells with similar orientation preferences. This model received strong support from anatomical studies showing that many complex cells lie within the range of layer IV simple-cell axons but outside the range of most thalamic axons. Physiological evidence for the model, however, has remained elusive. Here we demonstrate that layer IV simple cells and layer II and III complex cells show correlated firing consistent with monosynaptic connections. As expected from the hierarchical model, all connections were in the direction from the simple cell to the complex cell, most frequently between cells with similar orientation preferences.},
address = {Laboratory of Neurobiology, Rockefeller University, New York, New York 10021, USA. alonsoj@rockvax.rockefeller.edu},
author = {Alonso, J M and Martinez, L M},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Nature neuroscience 1998 Alonso-1.pdf:pdf},
journal = {Nat. Neurosci.},
month = {sep},
number = {5},
pages = {395--403},
title = {{Functional connectivity between simple cells and complex cells in cat striate cortex.}},
volume = {1},
year = {1998}
}
@article{Lin2016,
abstract = {Abstract: We show how the success of deep learning depends not only on mathematics but also on physics: although well-known mathematical theorems guarantee that neural networks can approximate arbitrary functions well, the class of functions of practical interest can be approximated through" cheap learning" with exponentially fewer parameters than generic ones, because they have simplifying properties tracing back to the laws of physics ...
},
author = {Lin, H W and Tegmark, M},
file = {:Users/hahosoya/Documents/Mendeley Desktop/arXiv 2016 Lin.pdf:pdf},
journal = {arXiv.org},
month = {jan},
title = {{Why does deep and cheap learning work so well?}},
year = {2016}
}
@article{McNamee2021a,
abstract = {Exploration, consolidation and planning depend on the generation of sequential state representations. However, these algorithms require disparate forms of sampling dynamics for optimal performance. We theorize how the brain should adapt internally generated sequences for particular cognitive functions and propose a neural mechanism by which this may be accomplished within the entorhinal–hippocampal circuit. Specifically, we demonstrate that the systematic modulation along the medial entorhinal cortex dorsoventral axis of grid population input into the hippocampus facilitates a flexible generative process that can interpolate between qualitatively distinct regimes of sequential hippocampal reactivations. By relating the emergent hippocampal activity patterns drawn from our model to empirical data, we explain and reconcile a diversity of recently observed, but apparently unrelated, phenomena such as generative cycling, diffusive hippocampal reactivations and jumping trajectory events.},
author = {McNamee, Daniel C. and Stachenfeld, Kimberly L. and Botvinick, Matthew M. and Gershman, Samuel J.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/s41593-021-00831-7(2).pdf:pdf},
journal = {Nat. Neurosci.},
number = {6},
pages = {851--862},
pmid = {33846626},
title = {{Flexible modulation of sequence generation in the entorhinal–hippocampal system}},
volume = {24},
year = {2021}
}
@article{Hashimoto2003,
abstract = {Several studies have succeeded in correlating natural image statistics with receptive field properties of neurons in the primary visual cortex. If we determine the parameters of linear transformations that make their output values as independent as possible when input data are natural images, we obtain parameter values that correspond to simple cell characteristics. It was also proved that, by making output values as temporally coherent as possible, simple cell characteristics also emerge. However, complex cell properties have not been fully explained by previous studies of natural image statistics. In this study, we examine whether we could reproduce complex cell properties by determining the parameters of two-layer networks that make their outputs as independent and sparse as possible or as temporally coherent as possible. Input-output functions of two-layer networks correspond to quadratic forms and they form a class of functions that includes complex cell responses and many other functions. Therefore, we employed two-layer networks as a framework for discussing complex cell properties as in previous studies. By maximizing the independence and sparseness of output values of two-layer networks without considering the temporal structure of input images, squared responses of simple cells are obtained and complex cell properties are not reproduced. On the other hand, by maximizing the temporal coherence of output, we obtain complex cell properties among other kinds of input-output functions. In previous studies, the measure of temporal coherence was the squared difference between the responses to two consecutive input images. We obtain two-layer networks that minimize this measure and show that some of them exhibit properties of complex cells but not clearly. We propose the sparseness of difference between responses to two consecutive inputs as an alternative measure of temporal coherence. We formulate an algorithm to maximize the sparseness of difference and show that complex cell properties emerge more clearly.},
address = {Laboratory for Advanced Brain Signal Processing, Brain Science Institute, RIKEN, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan. wakakoh@brain.riken.go.jp},
author = {Hashimoto, Wakako},
journal = {Netw. Comput. Neural Syst.},
month = {nov},
number = {4},
pages = {765--788},
title = {{Quadratic forms in natural images.}},
volume = {14},
year = {2003}
}
@inproceedings{Taigman2014,
abstract = {Abstract In modern face recognition, the conventional pipeline consists of four stages: detect=> align=> represent=> classify. We revisit both the alignment step and the representation step by employing explicit 3D face modeling in order to apply a piecewise affine transformation, and derive a face representation from a nine-layer deep neural network. This deep network involves more than 120 million parameters using several ...
},
author = {Taigman, Y and Yang, M and Ranzato, M A},
booktitle = {IEEE Conf. Comput. Vis. Pattern Recognit.},
file = {:Users/hahosoya/Documents/Mendeley Desktop/The IEEE Conference on Computer Vision and Pattern Recognition 2014 Taigman.pdf:pdf},
month = {jan},
pages = {1701--1708},
title = {{Deepface: Closing the gap to human-level performance in face verification}},
year = {2014}
}
@article{Welling2003,
abstract = {Abstract Principal components analysis (PCA) is one of the most widely used techniques in machine learning and data mining. Minor components analysis (MCA) is less well known, but can also play an important role in the presence of constraints on the data distribution.  ...},
author = {Welling, M and Agakov, F and et Al},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Advances in neural information {\ldots} 2003 Welling.pdf:pdf},
journal = {Adv. Neural Inf. {\ldots}},
month = {jan},
title = {{Extreme components analysis}},
year = {2003}
}
@article{Wallis2001,
abstract = {The influence of temporal association on the representation and recognition of objects was investigated. Observers were shown sequences of novel faces in which the identity of the face changed as the head rotated. As a result, observers showed a tendency to treat the views as if they were of the same person. Additional experiments revealed that this was only true if the training sequences depicted head rotations rather than jumbled views; in other words, the sequence had to be spatially as well as temporally smooth. Results suggest that we are continuously associating views of objects to support later recognition, and that we do so not only on the basis of the physical similarity, but also the correlated appearance in time of the objects.},
address = {Department of Human Movement Studies, University of Queensland, St. Lucia 4072, Queensland, Australia. gwallis@hms.uq.edu.au},
author = {Wallis, G and B{\"{u}}lthoff, H H},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proc. Natl. Acad. Sci. U.S.A. 2001 Wallis.pdf:pdf},
journal = {Proc. Natl. Acad. Sci. U. S. A.},
month = {apr},
number = {8},
pages = {4800--4804},
title = {{Effects of temporal association on recognition memory.}},
volume = {98},
year = {2001}
}
@article{Lies2014,
author = {Lies, J{\"{o}}rn-Philipp and H{\"{a}}fner, Ralf M and Bethge, Matthias},
editor = {Lucke, Jorg},
file = {:Users/hahosoya/Documents/Mendeley Desktop/PLoS Comp Biol 2014 Lies.pdf:pdf},
journal = {PLoS Comput. Biol.},
month = {mar},
number = {3},
pages = {e1003468},
title = {{Slowness and Sparseness Have Diverging Effects on Complex Cell Learning}},
volume = {10},
year = {2014}
}
@article{Shepherd2018,
abstract = {Neuron, 99 (2018) 413-423. doi:10.1016/j.neuron.2018.06.027},
author = {Shepherd, Stephen V and Freiwald, Winrich A},
file = {:Users/hahosoya/Documents/Mendeley Desktop/CAFEC7A6-B3FD-4A50-B752-BF95AAED16D1.pdf:pdf},
journal = {Neuron},
month = {jul},
number = {2},
pages = {413--420.e3},
title = {{Functional Networks for Social Communication in the Macaque Monkey}},
volume = {99},
year = {2018}
}
@article{Jones2002,
abstract = {We have explored the spatial organization of orientation contrast effects in primate V1. Our stimuli were either concentric patches of drifting grating of varying orientation and diameter or grating patches displaced in x–y coordinates around a central patch overlying the classical ...},
author = {Jones, H E and Wang, W},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neurophysiology 2002 Jones.pdf:pdf},
journal = {J. Neurophysiol.},
month = {jan},
title = {{Spatial Organization and Magnitude of Orientation Contrast Interactions in Primate V1}},
year = {2002}
}
@article{RUST2004,
abstract = {... We applied a spike  triggered  covariance analysis (STC) to data collected from isolated directionally tuned simple cells and complex cells within primary visual cortex (V1) of anesthetized, paralyzed macaque monkeys (experimental preparation details described by [4]). Stimuli ...},
author = {RUST, N and Schwartz, O and MOVSHON, J and SIMONCELLI, E},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Neurocomputing 2004 RUST.pdf:pdf},
journal = {Neurocomputing},
month = {jun},
pages = {793--799},
title = {{Spike-triggered characterization of excitatory and suppressive stimulus dimensions in monkey V1}},
volume = {58-60},
year = {2004}
}
@article{Ramirez2014,
abstract = {Humans recognize faces and objects with high speed and accuracy regardless of their orientation. Recent studies have proposed that orientation invariance in face recognition involves an intermediate representation where neural responses are similar for mirror-symmetric views. Here, we used fMRI, multivariate pattern analysis, and computational modeling to investigate the neural encoding of faces and vehicles at different rotational angles. Corroborating previous studies, we demonstrate a representation of face orientation in the fusiform face-selective area (FFA). We go beyond these studies by showing that this representation is category-selective and tolerant to retinal translation. Critically, by controlling for low-level confounds, we found the representation of orientation in FFA to be compatible with a linear angle code. Aspects of mirror-symmetric coding cannot be ruled out when FFA mean activity levels are considered as a dimension of coding. Finally, we used a parametric family of computational models, involving a biased sampling of view-tuned neuronal clusters, to compare different face angle encoding models. The best fitting model exhibited a predominance of neuronal clusters tuned to frontal views of faces. In sum, our findings suggest a category-selective and monotonic code of face orientation in the human FFA, in line with primate electrophysiology studies that observed mirror-symmetric tuning of neural responses at higher stages of the visual system, beyond the putative homolog of human FFA.},
address = {Bernstein Center for Computational Neuroscience, Charit{\'{e}}-Universit{\"{a}}tsmedizin Berlin, 10115 Berlin, Germany, Berlin School of Mind and Brain, Humboldt Universit{\"{a}}t zu Berlin, 10099 Berlin, Germany, and Berlin Center for Advanced Neuroimaging, Charit{\'{e}}-Univer},
author = {Ram{\'{i}}rez, Fernando M and Cichy, Radoslaw M and Allefeld, Carsten and Haynes, John-Dylan},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Journal of Neuroscience 2014 Ram{\'{i}}rez.pdf:pdf},
journal = {J. Neurosci.},
month = {sep},
number = {36},
pages = {12155--12167},
title = {{The neural code for face orientation in the human fusiform face area.}},
volume = {34},
year = {2014}
}
@article{Kersten2002,
abstract = {Visual perception involves the grouping of individual elements into coherent patterns that reduce 
the descriptive complexity of a visual scene. The physiological basis of this perceptual simplification
remains poorly understood. We used functional MRI to measure activity in a higher object  ...},
author = {Kersten, D and et Al},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Proceedings of the National Academy of Sciences USA 2002 Kersten.pdf:pdf},
journal = {Proc. Natl. Acad. Sci. USA},
month = {jan},
title = {{Shape perception reduces activity in human primary visual cortex}},
year = {2002}
}
@article{Klys2018,
abstract = {Variational autoencoders (VAEs) [10, 20] are widely used deep generative models capable of learning unsupervised latent representations of data. Such representations are often difficult to interpret or control. We consider the problem of unsupervised learning of features correlated to specific labels in a dataset. We propose a VAE-based generative model which we show is capable of extracting features correlated to binary labels in the data and structuring it in a latent subspace which is easy to interpret. Our model, the Conditional Subspace VAE (CSVAE), uses mutual information minimization to learn a low-dimensional latent subspace associated with each label that can easily be inspected and independently manipulated. We demonstrate the utility of the learned representations for attribute manipulation tasks on both the Toronto Face [23] and CelebA [15] datasets.},
archivePrefix = {arXiv},
arxivId = {1812.06190},
author = {Klys, Jack and Snell, Jake and Zemel, Richard},
eprint = {1812.06190},
file = {:Users/hahosoya/Documents/Mendeley Desktop/c87035f4b5cdb8597db20e9dc319c2a06d752197.pdf:pdf},
journal = {Adv. Neural Inf. Process. Syst.},
number = {Nips},
pages = {6444--6454},
title = {{Learning latent subspaces in variational autoencoders}},
volume = {2018-Decem},
year = {2018}
}
@article{Kulkarni2015,
abstract = {Abstract This paper presents the Deep Convolution Inverse Graphics Network (DC-IGN), a model that aims to learn an interpretable representation of images, disentangled with respect to three-dimensional scene structure and viewing transformations such as depth rotations and lighting variations. The DC-IGN model is composed of multiple layers of convolution and de-convolution operators and is trained using the Stochastic Gradient ...
},
author = {Kulkarni, T D and Whitney, W F and Kohli, P},
file = {:Users/hahosoya/Documents/Mendeley Desktop/Advances in neural {\ldots} 2015 Kulkarni.pdf:pdf},
journal = {Adv. neural  {\ldots}},
month = {jan},
title = {{Deep convolutional inverse graphics network}},
year = {2015}
}
@article{Graziano1994,
abstract = {Cells in the dorsal division of the medial superior temporal area (MSTd) have large receptive fields and respond to expansion/contraction, rotation, and translation motions. These same motions are generated as we move through the environment, leading investigators to suggest that area MSTd analyzes the optical flow. One influential idea suggests that navigation is achieved by decomposing the optical flow into the separate and discrete channels mentioned above, that is, expansion/contraction, rotation, and translation. We directly tested whether MSTd neurons perform such a decomposition by examining whether there are cells that are preferentially tuned to intermediate spiral motions, which combine both expansion/contraction and rotation components. The finding that many cells in MSTd are preferentially selective for spiral motions indicates that this simple three-channel decomposition hypothesis for MSTd does not appear to be correct. Instead, there is a continuum of patterns to which MSTd cells are selective. In addition, we find that MSTd cells maintain their selectivity when stimuli are moved to different locations in their large receptive fields. This position invariance indicates that MSTd cells selective for expansion cannot give precise information about the retinal location of the focus of expansion. Thus, individual MSTd neurons cannot code, in a precise fashion, the direction of heading by using the location of the focus of expansion. The only way this navigational information could be accurately derived from MSTd is through the use of a coarse, population encoding. Positional invariance and selectivity for a wide array of stimuli suggest that MSTd neurons encode patterns of motion per se, regardless of whether these motions are generated by moving objects or by motion induced by observer locomotion.},
address = {Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge 02139.},
author = {Graziano, M S and Andersen, R A and Snowden, R J},
file = {:Users/hahosoya/Documents/Mendeley Desktop/The Journal of neuroscience 1994 Graziano.pdf:pdf},
journal = {J. Neurosci.},
month = {jan},
number = {1},
pages = {54--67},
title = {{Tuning of MST neurons to spiral motions.}},
volume = {14},
year = {1994}
}
@article{Kumaran2012,
abstract = {Primates are remarkably adept at ranking each other within social hierarchies, a capacity that is critical to successful group living. Surprisingly little, however, is understood about the neurobiology underlying this quintessential aspect of primate cognition. In our experiment, participants first acquired knowledge about a social and a nonsocial hierarchy and then used this information to guide investment decisions. We found that neural activity in the amygdala tracked the development of knowledge about a social, but not a nonsocial, hierarchy. Further, structural variations in amygdala gray matter volume accounted for interindividual differences in social transitivity performance. Finally, the amygdala expressed a neural signal selectively coding for social rank, whose robustness predicted the influence of rank on participants@ investment decisions. In contrast, we observed that the linear structure of both social and nonsocial hierarchies was represented at a neural level in the hippocampus. Our study implicates the amygdala in the emergence and representation of knowledge about social hierarchies and distinguishes the domain-general contribution of the hippocampus.},
author = {Kumaran, Dharshan and Melo, Hans Ludwig and Duzel, Emrah},
file = {:Users/hahosoya/Documents/Mendeley Desktop/1-s2.0-S0896627312008896-main.pdf:pdf},
journal = {Neuron},
number = {3},
pages = {653--666},
pmid = {23141075},
title = {{The Emergence and Representation of Knowledge about Social and Nonsocial Hierarchies}},
volume = {76},
year = {2012}
}
