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Monthly
160 pp. per issue
8 1/2 x 11, illustrated
Founded: 1989
ISSN 0898-929X
E-ISSN 1530-8898
2008 ISI Impact Factor: 4.867
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February 2006, Vol. 18, No. 2, Pages 242-257
Posted Online March 28, 2006.
(doi:10.1162/jocn.2006.18.2.242)
© 2006 Massachusetts Institute of Technology
A Large-scale Neurocomputational Model of Task-oriented Behavior Selection and Working Memory in Prefrontal Cortex George L. Chadderdon and Olaf SpornsIndiana University Reprint requests should be sent to Olaf Sporns, Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, or via e-mail: osporns@indiana.edu.
The prefrontal cortex (PFC) is crucially involved in the executive component of working memory, representation of task state, and behavior selection. This article presents a large-scale computational model of the PFC and associated brain regions designed to investigate the mechanisms by which working memory and task state interact to select adaptive behaviors from a behavioral repertoire. The model consists of multiple brain regions containing neuronal populations with realistic physiological and anatomical properties, including extrastriate visual cortical regions, the inferotemporal cortex, the PFC, the striatum, and midbrain dopamine (DA) neurons. The onset of a delayed match-to-sample or delayed nonmatch-to-sample task triggers tonic DA release in the PFC causing a switch into a persistent, stimulus-insensitive dynamic state that promotes the maintenance of stimulus representations within prefrontal networks. Other modeled prefrontal and striatal units select cognitive acceptance or rejection behaviors according to which task is active and whether prefrontal working memory representations match the current stimulus. Working memory task performance and memory fields of prefrontal delay units are degraded by extreme elevation or depletion of tonic DA levels. Analyses of cellular and synaptic activity suggest that hyponormal DA levels result in increased prefrontal activation, whereas hypernormal DA levels lead to decreased activation. Our simulation results suggest a range of predictions for behavioral, single-cell, and neuroimaging response data under the proposed task set and under manipulations of DA concentration.
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