Credit-based self organizing maps: training deep topographic networks with minimal performance degradation

Amirozhan Dehghani, Xinyu Qian, Asa Farahani, Pouya Bashivan

Keywords: Computer vision, Neuroscience, Convolutional Networks, topographical organization, self-organizing maps, functional organization

TL;DR: We developed a new topographical neural network model that replicates the functional organization of the visual ventral stream while retaining high object recognition performance

Abstract: In the primate neocortex, neurons with similar function are often found to be spatially close. Kohonen's self-organizing map (SOM) has been one of the most influential approaches for simulating brain-like topographical organization in artificial neural network models. However, integrating these maps into deep neural networks with multitude of layers has been challenging, with self-organized deep neural networks suffering from substantially diminished capacity to perform visual recognition. We identified a key factor leading to the performance degradation in self-organized topographical neural network models: the discord between predominantly bottom-up learning updates in the self-organizing maps, and those derived from top-down, credit-based learning approaches. To address this, we propose an alternative self organization algorithm, tailored to align with the top-down learning processes in deep neural networks. This model not only emulates critical aspects of cortical topography but also significantly narrows the performance gap between non-topographical and topographical models. This advancement underscores the substantial importance of top-down assigned credits in shaping topographical organization. Our findings are a step in reconciling topographical modeling with the functional efficacy of neural network models, paving the way for more brain-like neural architectures.

Primary Area: applications to neuroscience & cognitive science

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Submission Number: 1964