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

Authors: Balaguer-Ballester, E., Lapish, C.C., Seamans, J.K. and Durstewitz, D.

http://eprints.bournemouth.ac.uk/23462/

Journal: PLoS Computational Biology

Volume: 7

Pages: e1002057

Publisher: Public Library of Science

This source preferred by Emili Balaguer-Ballester

This data was imported from PubMed:

Authors: Balaguer-Ballester, E., Lapish, C.C., Seamans, J.K. and Durstewitz, D.

http://eprints.bournemouth.ac.uk/23462/

Journal: PLoS Comput Biol

Volume: 7

Issue: 5

Pages: e1002057

eISSN: 1553-7358

DOI: 10.1371/journal.pcbi.1002057

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.

This data was imported from DBLP:

Authors: Balaguer-Ballester, E., Lapish, C.C., Seamans, J.K. and Durstewitz, D.

http://eprints.bournemouth.ac.uk/23462/

Journal: PLoS Computational Biology

Volume: 7

This data was imported from Scopus:

Authors: Balaguer-Ballester, E., Lapish, C.C., Seamans, J.K. and Durstewitz, D.

http://eprints.bournemouth.ac.uk/23462/

Journal: PLoS Computational Biology

Volume: 7

Issue: 5

eISSN: 1553-7358

ISSN: 1553-734X

DOI: 10.1371/journal.pcbi.1002057

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. © 2011 Balaguer-Ballester et al.

This data was imported from Web of Science (Lite):

Authors: Balaguer-Ballester, E., Lapish, C.C., Seamans, J.K. and Durstewitz, D.

http://eprints.bournemouth.ac.uk/23462/

Journal: PLOS COMPUTATIONAL BIOLOGY

Volume: 7

Issue: 5

eISSN: 1553-7358

ISSN: 1553-734X

DOI: 10.1371/journal.pcbi.1002057

This data was imported from Europe PubMed Central:

Authors: Balaguer-Ballester, E., Lapish, C.C., Seamans, J.K. and Durstewitz, D.

http://eprints.bournemouth.ac.uk/23462/

Journal: PLoS computational biology

Volume: 7

Issue: 5

Pages: e1002057

eISSN: 1553-7358

ISSN: 1553-734X

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.

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