Attracting dynamics of frontal cortex ensembles during memory-guided decision-making.

A common theoretical view is that attractor-like properties of neuronal dynamics underlie cognitive processing. However, although often proposed theoretically, direct experimental support for the convergence of neural activity to stable population patterns as a signature of attracting states has bee...

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Autores principales:	Emili Balaguer-Ballester, Christopher C Lapish, Jeremy K Seamans, Daniel Durstewitz
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2011
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/fae6f4ad245b4673bf2831c93a585dae
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spelling	oai:doaj.org-article:fae6f4ad245b4673bf2831c93a585dae2021-11-18T05:50:31ZAttracting dynamics of frontal cortex ensembles during memory-guided decision-making.1553-734X1553-735810.1371/journal.pcbi.1002057https://doaj.org/article/fae6f4ad245b4673bf2831c93a585dae2011-05-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/21625577/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358A common theoretical view is that attractor-like properties of neuronal dynamics underlie cognitive processing. However, although often proposed theoretically, direct experimental support for the convergence of neural activity to stable population patterns as a signature of attracting states has been sparse so far, especially in higher cortical areas. Combining state space reconstruction theorems and statistical learning techniques, we were able to resolve details of anterior cingulate cortex (ACC) multiple single-unit activity (MSUA) ensemble dynamics during a higher cognitive task which were not accessible previously. The approach worked by constructing high-dimensional state spaces from delays of the original single-unit firing rate variables and the interactions among them, which were then statistically analyzed using kernel methods. We observed cognitive-epoch-specific neural ensemble states in ACC which were stable across many trials (in the sense of being predictive) and depended on behavioral performance. More interestingly, attracting properties of these cognitively defined ensemble states became apparent in high-dimensional expansions of the MSUA spaces due to a proper unfolding of the neural activity flow, with properties common across different animals. These results therefore suggest that ACC networks may process different subcomponents of higher cognitive tasks by transiting among different attracting states.Emili Balaguer-BallesterChristopher C LapishJeremy K SeamansDaniel DurstewitzPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 7, Iss 5, p e1002057 (2011)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Emili Balaguer-Ballester Christopher C Lapish Jeremy K Seamans Daniel Durstewitz Attracting dynamics of frontal cortex ensembles during memory-guided decision-making.
description	A common theoretical view is that attractor-like properties of neuronal dynamics underlie cognitive processing. However, although often proposed theoretically, direct experimental support for the convergence of neural activity to stable population patterns as a signature of attracting states has been sparse so far, especially in higher cortical areas. Combining state space reconstruction theorems and statistical learning techniques, we were able to resolve details of anterior cingulate cortex (ACC) multiple single-unit activity (MSUA) ensemble dynamics during a higher cognitive task which were not accessible previously. The approach worked by constructing high-dimensional state spaces from delays of the original single-unit firing rate variables and the interactions among them, which were then statistically analyzed using kernel methods. We observed cognitive-epoch-specific neural ensemble states in ACC which were stable across many trials (in the sense of being predictive) and depended on behavioral performance. More interestingly, attracting properties of these cognitively defined ensemble states became apparent in high-dimensional expansions of the MSUA spaces due to a proper unfolding of the neural activity flow, with properties common across different animals. These results therefore suggest that ACC networks may process different subcomponents of higher cognitive tasks by transiting among different attracting states.
format	article
author	Emili Balaguer-Ballester Christopher C Lapish Jeremy K Seamans Daniel Durstewitz
author_facet	Emili Balaguer-Ballester Christopher C Lapish Jeremy K Seamans Daniel Durstewitz
author_sort	Emili Balaguer-Ballester
title	Attracting dynamics of frontal cortex ensembles during memory-guided decision-making.
title_short	Attracting dynamics of frontal cortex ensembles during memory-guided decision-making.
title_full	Attracting dynamics of frontal cortex ensembles during memory-guided decision-making.
title_fullStr	Attracting dynamics of frontal cortex ensembles during memory-guided decision-making.
title_full_unstemmed	Attracting dynamics of frontal cortex ensembles during memory-guided decision-making.
title_sort	attracting dynamics of frontal cortex ensembles during memory-guided decision-making.
publisher	Public Library of Science (PLoS)
publishDate	2011
url	https://doaj.org/article/fae6f4ad245b4673bf2831c93a585dae
work_keys_str_mv	AT emilibalaguerballester attractingdynamicsoffrontalcortexensemblesduringmemoryguideddecisionmaking AT christopherclapish attractingdynamicsoffrontalcortexensemblesduringmemoryguideddecisionmaking AT jeremykseamans attractingdynamicsoffrontalcortexensemblesduringmemoryguideddecisionmaking AT danieldurstewitz attractingdynamicsoffrontalcortexensemblesduringmemoryguideddecisionmaking
_version_	1718424828982067200

Attracting dynamics of frontal cortex ensembles during memory-guided decision-making.

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