Stimulus-dependent Maximum Entropy Models of Neural Population Codes

• DOI: 10.1371/journal.pcbi.1002922
• Publication Date: 2013-03-14T23:40:16Z
• Authors: Einat Granot-Atedgi; Gašper Tkačik; Ronen Segev; Elad Schneidman
• Keywords: Retinal Ganglion Cells; 0301 basic medicine; 0303 health sciences; QH301-705.5; Entropy; Models, Neurological; Action Potentials; Computational Biology; FOS: Physical sciences; Ambystoma; Retina; Electrophysiology; 03 medical and health sciences; Nonlinear Dynamics; Biological Physics (physics.bio-ph); Quantitative Biology - Neurons and Cognition; FOS: Biological sciences; Linear Models; Animals; Cluster Analysis; Neurons and Cognition (q-bio.NC); Physics - Biological Physics; Biology (General); Photic Stimulation; Research Article

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/>