Modelling the neural code in large populations of correlated neurons

Neurons respond selectively to stimuli, and thereby define a code that associates stimuli with population response patterns. Certain correlations within population responses (noise correlations) significantly impact the information content of the code, especially in large populations. Understanding...

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Autores principales:	Sacha Sokoloski, Amir Aschner, Ruben Coen-Cagli
Formato:	article
Lenguaje:	EN
Publicado:	eLife Sciences Publications Ltd 2021
Materias:	primary visual cortex neural coding bayesian modelling Medicine R Science Q Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/d425cd9fe6fa4b8c8a45b497f73128d1
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id	oai:doaj.org-article:d425cd9fe6fa4b8c8a45b497f73128d1
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spelling	oai:doaj.org-article:d425cd9fe6fa4b8c8a45b497f73128d12021-11-12T15:20:12ZModelling the neural code in large populations of correlated neurons10.7554/eLife.646152050-084Xe64615https://doaj.org/article/d425cd9fe6fa4b8c8a45b497f73128d12021-10-01T00:00:00Zhttps://elifesciences.org/articles/64615https://doaj.org/toc/2050-084XNeurons respond selectively to stimuli, and thereby define a code that associates stimuli with population response patterns. Certain correlations within population responses (noise correlations) significantly impact the information content of the code, especially in large populations. Understanding the neural code thus necessitates response models that quantify the coding properties of modelled populations, while fitting large-scale neural recordings and capturing noise correlations. In this paper, we propose a class of response model based on mixture models and exponential families. We show how to fit our models with expectation-maximization, and that they capture diverse variability and covariability in recordings of macaque primary visual cortex. We also show how they facilitate accurate Bayesian decoding, provide a closed-form expression for the Fisher information, and are compatible with theories of probabilistic population coding. Our framework could allow researchers to quantitatively validate the predictions of neural coding theories against both large-scale neural recordings and cognitive performance.Sacha SokoloskiAmir AschnerRuben Coen-CaglieLife Sciences Publications Ltdarticleprimary visual cortexneural codingbayesian modellingMedicineRScienceQBiology (General)QH301-705.5ENeLife, Vol 10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	primary visual cortex neural coding bayesian modelling Medicine R Science Q Biology (General) QH301-705.5
spellingShingle	primary visual cortex neural coding bayesian modelling Medicine R Science Q Biology (General) QH301-705.5 Sacha Sokoloski Amir Aschner Ruben Coen-Cagli Modelling the neural code in large populations of correlated neurons
description	Neurons respond selectively to stimuli, and thereby define a code that associates stimuli with population response patterns. Certain correlations within population responses (noise correlations) significantly impact the information content of the code, especially in large populations. Understanding the neural code thus necessitates response models that quantify the coding properties of modelled populations, while fitting large-scale neural recordings and capturing noise correlations. In this paper, we propose a class of response model based on mixture models and exponential families. We show how to fit our models with expectation-maximization, and that they capture diverse variability and covariability in recordings of macaque primary visual cortex. We also show how they facilitate accurate Bayesian decoding, provide a closed-form expression for the Fisher information, and are compatible with theories of probabilistic population coding. Our framework could allow researchers to quantitatively validate the predictions of neural coding theories against both large-scale neural recordings and cognitive performance.
format	article
author	Sacha Sokoloski Amir Aschner Ruben Coen-Cagli
author_facet	Sacha Sokoloski Amir Aschner Ruben Coen-Cagli
author_sort	Sacha Sokoloski
title	Modelling the neural code in large populations of correlated neurons
title_short	Modelling the neural code in large populations of correlated neurons
title_full	Modelling the neural code in large populations of correlated neurons
title_fullStr	Modelling the neural code in large populations of correlated neurons
title_full_unstemmed	Modelling the neural code in large populations of correlated neurons
title_sort	modelling the neural code in large populations of correlated neurons
publisher	eLife Sciences Publications Ltd
publishDate	2021
url	https://doaj.org/article/d425cd9fe6fa4b8c8a45b497f73128d1
work_keys_str_mv	AT sachasokoloski modellingtheneuralcodeinlargepopulationsofcorrelatedneurons AT amiraschner modellingtheneuralcodeinlargepopulationsofcorrelatedneurons AT rubencoencagli modellingtheneuralcodeinlargepopulationsofcorrelatedneurons
_version_	1718430400863272960

Modelling the neural code in large populations of correlated neurons

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