Impact of network structure and cellular response on spike time correlations.

Novel experimental techniques reveal the simultaneous activity of larger and larger numbers of neurons. As a result there is increasing interest in the structure of cooperative--or correlated--activity in neural populations, and in the possible impact of such correlations on the neural code. A funda...

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Autores principales:	James Trousdale, Yu Hu, Eric Shea-Brown, Krešimir Josić
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/71bc0e07abe142929a19268eb4b85846
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spelling	oai:doaj.org-article:71bc0e07abe142929a19268eb4b858462021-11-18T05:51:29ZImpact of network structure and cellular response on spike time correlations.1553-734X1553-735810.1371/journal.pcbi.1002408https://doaj.org/article/71bc0e07abe142929a19268eb4b858462012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22457608/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Novel experimental techniques reveal the simultaneous activity of larger and larger numbers of neurons. As a result there is increasing interest in the structure of cooperative--or correlated--activity in neural populations, and in the possible impact of such correlations on the neural code. A fundamental theoretical challenge is to understand how the architecture of network connectivity along with the dynamical properties of single cells shape the magnitude and timescale of correlations. We provide a general approach to this problem by extending prior techniques based on linear response theory. We consider networks of general integrate-and-fire cells with arbitrary architecture, and provide explicit expressions for the approximate cross-correlation between constituent cells. These correlations depend strongly on the operating point (input mean and variance) of the neurons, even when connectivity is fixed. Moreover, the approximations admit an expansion in powers of the matrices that describe the network architecture. This expansion can be readily interpreted in terms of paths between different cells. We apply our results to large excitatory-inhibitory networks, and demonstrate first how precise balance--or lack thereof--between the strengths and timescales of excitatory and inhibitory synapses is reflected in the overall correlation structure of the network. We then derive explicit expressions for the average correlation structure in randomly connected networks. These expressions help to identify the important factors that shape coordinated neural activity in such networks.James TrousdaleYu HuEric Shea-BrownKrešimir JosićPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 8, Iss 3, p e1002408 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 James Trousdale Yu Hu Eric Shea-Brown Krešimir Josić Impact of network structure and cellular response on spike time correlations.
description	Novel experimental techniques reveal the simultaneous activity of larger and larger numbers of neurons. As a result there is increasing interest in the structure of cooperative--or correlated--activity in neural populations, and in the possible impact of such correlations on the neural code. A fundamental theoretical challenge is to understand how the architecture of network connectivity along with the dynamical properties of single cells shape the magnitude and timescale of correlations. We provide a general approach to this problem by extending prior techniques based on linear response theory. We consider networks of general integrate-and-fire cells with arbitrary architecture, and provide explicit expressions for the approximate cross-correlation between constituent cells. These correlations depend strongly on the operating point (input mean and variance) of the neurons, even when connectivity is fixed. Moreover, the approximations admit an expansion in powers of the matrices that describe the network architecture. This expansion can be readily interpreted in terms of paths between different cells. We apply our results to large excitatory-inhibitory networks, and demonstrate first how precise balance--or lack thereof--between the strengths and timescales of excitatory and inhibitory synapses is reflected in the overall correlation structure of the network. We then derive explicit expressions for the average correlation structure in randomly connected networks. These expressions help to identify the important factors that shape coordinated neural activity in such networks.
format	article
author	James Trousdale Yu Hu Eric Shea-Brown Krešimir Josić
author_facet	James Trousdale Yu Hu Eric Shea-Brown Krešimir Josić
author_sort	James Trousdale
title	Impact of network structure and cellular response on spike time correlations.
title_short	Impact of network structure and cellular response on spike time correlations.
title_full	Impact of network structure and cellular response on spike time correlations.
title_fullStr	Impact of network structure and cellular response on spike time correlations.
title_full_unstemmed	Impact of network structure and cellular response on spike time correlations.
title_sort	impact of network structure and cellular response on spike time correlations.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/71bc0e07abe142929a19268eb4b85846
work_keys_str_mv	AT jamestrousdale impactofnetworkstructureandcellularresponseonspiketimecorrelations AT yuhu impactofnetworkstructureandcellularresponseonspiketimecorrelations AT ericsheabrown impactofnetworkstructureandcellularresponseonspiketimecorrelations AT kresimirjosic impactofnetworkstructureandcellularresponseonspiketimecorrelations
_version_	1718424734677336064

Impact of network structure and cellular response on spike time correlations.

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