Stimulus-dependent maximum entropy models of neural population codes.

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. F...

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Autores principales:	Einat Granot-Atedgi, Gašper Tkačik, Ronen Segev, Elad Schneidman
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/049461a7e66448c7928f8cc59215ad42
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spelling	oai:doaj.org-article:049461a7e66448c7928f8cc59215ad422021-11-18T05:52:22ZStimulus-dependent maximum entropy models of neural population codes.1553-734X1553-735810.1371/journal.pcbi.1002922https://doaj.org/article/049461a7e66448c7928f8cc59215ad422013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23516339/pdf/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Neural 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.Einat Granot-AtedgiGašper TkačikRonen SegevElad SchneidmanPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 9, Iss 3, p e1002922 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Einat Granot-Atedgi Gašper Tkačik Ronen Segev Elad Schneidman Stimulus-dependent maximum entropy models of neural population codes.
description	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.
format	article
author	Einat Granot-Atedgi Gašper Tkačik Ronen Segev Elad Schneidman
author_facet	Einat Granot-Atedgi Gašper Tkačik Ronen Segev Elad Schneidman
author_sort	Einat Granot-Atedgi
title	Stimulus-dependent maximum entropy models of neural population codes.
title_short	Stimulus-dependent maximum entropy models of neural population codes.
title_full	Stimulus-dependent maximum entropy models of neural population codes.
title_fullStr	Stimulus-dependent maximum entropy models of neural population codes.
title_full_unstemmed	Stimulus-dependent maximum entropy models of neural population codes.
title_sort	stimulus-dependent maximum entropy models of neural population codes.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/049461a7e66448c7928f8cc59215ad42
work_keys_str_mv	AT einatgranotatedgi stimulusdependentmaximumentropymodelsofneuralpopulationcodes AT gaspertkacik stimulusdependentmaximumentropymodelsofneuralpopulationcodes AT ronensegev stimulusdependentmaximumentropymodelsofneuralpopulationcodes AT eladschneidman stimulusdependentmaximumentropymodelsofneuralpopulationcodes
_version_	1718424708078108672

Stimulus-dependent maximum entropy models of neural population codes.

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