Attracting dynamics of frontal cortex ensembles during memory-guided decision-making
Authors: Balaguer-Ballester, E., Lapish, C.C., Seamans, J.K. and Durstewitz, D.
Journal: PLoS Computational Biology
Volume: 7
Issue: 5
eISSN: 1553-7358
ISSN: 1553-734X
DOI: 10.1371/journal.pcbi.1002057
Abstract: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.
https://eprints.bournemouth.ac.uk/23462/
Source: Scopus
Attracting dynamics of frontal cortex ensembles during memory-guided decision-making.
Authors: Balaguer-Ballester, E., Lapish, C.C., Seamans, J.K. and Durstewitz, D.
Journal: PLoS Comput Biol
Volume: 7
Issue: 5
Pages: e1002057
eISSN: 1553-7358
DOI: 10.1371/journal.pcbi.1002057
Abstract: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.
https://eprints.bournemouth.ac.uk/23462/
Source: PubMed
Preferred by: Emili Balaguer-Ballester
Attracting Dynamics of Frontal Cortex Ensembles during Memory-Guided Decision-Making
Authors: Balaguer-Ballester, E., Lapish, C.C., Seamans, J.K. and Durstewitz, D.
Journal: PLOS COMPUTATIONAL BIOLOGY
Volume: 7
Issue: 5
eISSN: 1553-7358
ISSN: 1553-734X
DOI: 10.1371/journal.pcbi.1002057
https://eprints.bournemouth.ac.uk/23462/
Source: Web of Science (Lite)
Attracting dynamics of frontal cortex ensembles during memory-guided decision-making
Authors: Balaguer-Ballester, E., Lapish, C.C., Seamans, J.K. and Durstewitz, D.
Journal: PLoS Computational Biology
Volume: 7
Pages: e1002057
Publisher: Public Library of Science
https://eprints.bournemouth.ac.uk/23462/
Source: Manual
Attracting Dynamics of Frontal Cortex Ensembles during Memory-Guided Decision-Making.
Authors: Balaguer-Ballester, E., Lapish, C.C., Seamans, J.K. and Durstewitz, D.
Journal: PLoS Comput. Biol.
Volume: 7
DOI: 10.1371/journal.pcbi.1002057
https://eprints.bournemouth.ac.uk/23462/
Source: DBLP
Attracting dynamics of frontal cortex ensembles during memory-guided decision-making.
Authors: Balaguer-Ballester, E., Lapish, C.C., Seamans, J.K. and Durstewitz, D.
Journal: PLoS computational biology
Volume: 7
Issue: 5
Pages: e1002057
eISSN: 1553-7358
ISSN: 1553-734X
DOI: 10.1371/journal.pcbi.1002057
Abstract: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.
https://eprints.bournemouth.ac.uk/23462/
Source: Europe PubMed Central
Attracting dynamics of frontal cortex ensembles during memory-guided decision-making.
Authors: Balaguer-Ballester, E., Lapish, C.C., Seamans, J.K. and Durstewitz, D.
Journal: PLoS Computational Biology
Volume: 7
Issue: 5
Pages: e1002057
ISSN: 1553-7358
Abstract: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.
https://eprints.bournemouth.ac.uk/23462/
Source: BURO EPrints