Optimal responsiveness and information flow in networks of heterogeneous neurons

Abstract Cerebral cortex is characterized by a strong neuron-to-neuron heterogeneity, but it is unclear what consequences this may have for cortical computations, while most computational models consider networks of identical units. Here, we study network models of spiking neurons endowed with heter...

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Autores principales:	Matteo Di Volo, Alain Destexhe
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/62c3e6d577b74a6cb4a9e9e04d4fa9f6
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spelling	oai:doaj.org-article:62c3e6d577b74a6cb4a9e9e04d4fa9f62021-12-02T17:51:26ZOptimal responsiveness and information flow in networks of heterogeneous neurons10.1038/s41598-021-96745-22045-2322https://doaj.org/article/62c3e6d577b74a6cb4a9e9e04d4fa9f62021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-96745-2https://doaj.org/toc/2045-2322Abstract Cerebral cortex is characterized by a strong neuron-to-neuron heterogeneity, but it is unclear what consequences this may have for cortical computations, while most computational models consider networks of identical units. Here, we study network models of spiking neurons endowed with heterogeneity, that we treat independently for excitatory and inhibitory neurons. We find that heterogeneous networks are generally more responsive, with an optimal responsiveness occurring for levels of heterogeneity found experimentally in different published datasets, for both excitatory and inhibitory neurons. To investigate the underlying mechanisms, we introduce a mean-field model of heterogeneous networks. This mean-field model captures optimal responsiveness and suggests that it is related to the stability of the spontaneous asynchronous state. The mean-field model also predicts that new dynamical states can emerge from heterogeneity, a prediction which is confirmed by network simulations. Finally we show that heterogeneous networks maximise the information flow in large-scale networks, through recurrent connections. We conclude that neuronal heterogeneity confers different responsiveness to neural networks, which should be taken into account to investigate their information processing capabilities.Matteo Di VoloAlain DestexheNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Matteo Di Volo Alain Destexhe Optimal responsiveness and information flow in networks of heterogeneous neurons
description	Abstract Cerebral cortex is characterized by a strong neuron-to-neuron heterogeneity, but it is unclear what consequences this may have for cortical computations, while most computational models consider networks of identical units. Here, we study network models of spiking neurons endowed with heterogeneity, that we treat independently for excitatory and inhibitory neurons. We find that heterogeneous networks are generally more responsive, with an optimal responsiveness occurring for levels of heterogeneity found experimentally in different published datasets, for both excitatory and inhibitory neurons. To investigate the underlying mechanisms, we introduce a mean-field model of heterogeneous networks. This mean-field model captures optimal responsiveness and suggests that it is related to the stability of the spontaneous asynchronous state. The mean-field model also predicts that new dynamical states can emerge from heterogeneity, a prediction which is confirmed by network simulations. Finally we show that heterogeneous networks maximise the information flow in large-scale networks, through recurrent connections. We conclude that neuronal heterogeneity confers different responsiveness to neural networks, which should be taken into account to investigate their information processing capabilities.
format	article
author	Matteo Di Volo Alain Destexhe
author_facet	Matteo Di Volo Alain Destexhe
author_sort	Matteo Di Volo
title	Optimal responsiveness and information flow in networks of heterogeneous neurons
title_short	Optimal responsiveness and information flow in networks of heterogeneous neurons
title_full	Optimal responsiveness and information flow in networks of heterogeneous neurons
title_fullStr	Optimal responsiveness and information flow in networks of heterogeneous neurons
title_full_unstemmed	Optimal responsiveness and information flow in networks of heterogeneous neurons
title_sort	optimal responsiveness and information flow in networks of heterogeneous neurons
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/62c3e6d577b74a6cb4a9e9e04d4fa9f6
work_keys_str_mv	AT matteodivolo optimalresponsivenessandinformationflowinnetworksofheterogeneousneurons AT alaindestexhe optimalresponsivenessandinformationflowinnetworksofheterogeneousneurons
_version_	1718379229262905344

Optimal responsiveness and information flow in networks of heterogeneous neurons

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