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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Bibliographic Details
Published in:PLoS computational biology 2011-05, Vol.7 (5), p.e1002057-e1002057
Main Authors: Balaguer-Ballester, Emili, Lapish, Christopher C, Seamans, Jeremy K, Durstewitz, Daniel
Format: Article
Language:English
Subjects:
Animal cognition
Animals
Anterior cingulate cortex
Behavior
Biology
Decision making
Decision Making - physiology
Frontal Lobe - physiology
Gyrus Cinguli - physiology
Maze Learning - physiology
Memory
Memory - physiology
Nerve Net - physiology
Neurons
Neurons - physiology
Physiological aspects
Population
Rats
Studies
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Summary: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.
ISSN:1553-7358
1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1002057