Stimulus-dependent maximum entropy models of neural population codes
Granot Atedgi E, Tkačik G, Segev R, Schneidman E. 2013. Stimulus-dependent maximum entropy models of neural population codes. PLoS Computational Biology. 9(3), e1002922.
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Author
Granot Atedgi, Einat;
Tkacik, GasperISTA ;
Segev, Ronen;
Schneidman, Elad
Department
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. For large populations, direct sampling of these distributions is impossible, and so we must rely on constructing appropriate models. We show here 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. We introduce the 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. We find that the SDME model gives a more accurate account of single cell responses and in particular significantly outperforms uncoupled models in reproducing the distributions of population 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 average surprise and information transmission in a neural population.
Publishing Year
Date Published
2013-03-01
Journal Title
PLoS Computational Biology
Volume
9
Issue
3
Article Number
e1002922
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Cite this
Granot Atedgi E, Tkačik G, Segev R, Schneidman E. Stimulus-dependent maximum entropy models of neural population codes. PLoS Computational Biology. 2013;9(3). doi:10.1371/journal.pcbi.1002922
Granot Atedgi, E., Tkačik, G., Segev, R., & Schneidman, E. (2013). Stimulus-dependent maximum entropy models of neural population codes. PLoS Computational Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1002922
Granot Atedgi, Einat, Gašper Tkačik, Ronen Segev, and Elad Schneidman. “Stimulus-Dependent Maximum Entropy Models of Neural Population Codes.” PLoS Computational Biology. Public Library of Science, 2013. https://doi.org/10.1371/journal.pcbi.1002922.
E. Granot Atedgi, G. Tkačik, R. Segev, and E. Schneidman, “Stimulus-dependent maximum entropy models of neural population codes,” PLoS Computational Biology, vol. 9, no. 3. Public Library of Science, 2013.
Granot Atedgi E, Tkačik G, Segev R, Schneidman E. 2013. Stimulus-dependent maximum entropy models of neural population codes. PLoS Computational Biology. 9(3), e1002922.
Granot Atedgi, Einat, et al. “Stimulus-Dependent Maximum Entropy Models of Neural Population Codes.” PLoS Computational Biology, vol. 9, no. 3, e1002922, Public Library of Science, 2013, doi:10.1371/journal.pcbi.1002922.
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