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: Stimulus (psychology); Neural coding; ENCODE

Neural populations encode information about their stimulus in a collective fashion, by joint activity patterns of spiking and silence. A full account this mapping from to neural is given the conditional probability distribution over codewords sensory input. For large populations, direct sampling these distributions impossible, so we must rely on constructing appropriate models. We show here that population 100 retinal ganglion cells salamander retina responding temporal white-noise stimuli, dependencies between play an important encoding role. introduce stimulus-dependent maximum entropy (SDME) model—a minimal extension canonical linear-nonlinear model single neuron, pairwise-coupled population. find SDME gives more accurate cell responses particular significantly outperforms uncoupled models reproducing emitted response stimulus. how model, conjunction with static vocabulary, can be used estimate information-theoretic quantities like average surprise transmission