Stimulus-dependent Maximum Entropy Models of Neural Population Codes

Einat Granot-Atedgi, Gasper Tkacik, Ronen Segev, Elad Schneidman

Abstract: Author Summary In the sensory periphery, stimuli are represented by patterns of spikes and silences across a population of sensory neurons. Because the neurons form an interconnected network, the code cannot be understood by looking at single cells alone. Recent recordings in the retina have enabled us to study populations of a hundred or more neurons that carry the visual information into the brain, and thus build probabilistic models of the neural code. Here we present a minimal (maximum entropy) yet powerful extension of well-known linear/nonlinear models for independent neurons, to an interacting population. This model reproduces the behavior of single cells as well as the structure of correlations in neural spiking. Our model predicts much better the complete set of patterns of spiking and silence across a population of cells, allowing us to explore the properties of the stimulus-response mapping, and estimate the information transmission, in bits per second, that the population carries about the stimulus. Our results show that to understand the code, we need to shift our focus from reproducing single-cell properties (such as firing rates) towards understanding the total “vocabulary” of patterns emitted by the population, and that network correlations play a central role in shaping the code of large neural populations.