Stimulus-dependent Maximum Entropy Models of Neural Population Codes

Retinal Ganglion Cells QH301-705.5 Entropy Models, Neurological Action Potentials FOS: Physical sciences 000 Computer science, knowledge & systems Ambystoma Retina 03 medical and health sciences Animals Cluster Analysis Physics - Biological Physics Biology (General) 0303 health sciences Computational Biology Electrophysiology Nonlinear Dynamics Biological Physics (physics.bio-ph) Quantitative Biology - Neurons and Cognition FOS: Biological sciences Linear Models 570 Life sciences; biology Neurons and Cognition (q-bio.NC) Photic Stimulation Research Article
DOI: 10.1371/journal.pcbi.1002922 Publication Date: 2013-03-14T23:40:16Z
ABSTRACT
Neural populations encode information about their stimulus in a collective fashion, by joint activity patterns of spiking and silence. A full account of this mapping from stimulus to neural activity is given by the conditional probability distribution over neural codewords given the sensory input. To be able to infer a model for this distribution from large-scale neural recordings, we introduce a stimulus-dependent maximum entropy (SDME) model---a minimal extension of the canonical linear-nonlinear model of a single neuron, to a pairwise-coupled neural population. The model is able to capture the single-cell response properties as well as the correlations in neural spiking due to shared stimulus and due to effective neuron-to-neuron connections. Here we show that in a population of 100 retinal ganglion cells in the salamander retina responding to temporal white-noise stimuli, dependencies between cells play an important encoding role. As a result, the SDME model gives a more accurate account of single cell responses and in particular outperforms uncoupled models in reproducing the distributions of codewords emitted in response to a stimulus. We show how the SDME model, in conjunction with static maximum entropy models of population vocabulary, can be used to estimate information-theoretic quantities like surprise and information transmission in a neural population.<br/>11 pages, 7 figures<br/>
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