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A Computational Model of How Cholinergic Interneurons Protect Striatal-dependent Learning

A Computational Model of How Cholinergic Interneurons Protect Striatal-dependent Learning An essential component of skill acquisition is learning the environmental conditions in which that skill is relevant. This article proposes and tests a neurobiologically detailed theory of how such learning is mediated. The theory assumes that a key component of this learning is provided by the cholinergic interneurons in the striatum known as tonically active neurons (TANs). The TANs are assumed to exert a tonic inhibitory influence over cortical inputs to the striatum that prevents the execution of any striatal-dependent actions. The TANs learn to pause in rewarding environments, and this pause releases the striatal output neurons from this inhibitory effect, thereby facilitating the learning and expression of striatal-dependent behaviors. When rewards are no longer available, the TANs cease to pause, which protects striatal learning from decay. A computational version of this theory accounts for a variety of single-cell recording data and some classic behavioral phenomena, including fast reacquisition after extinction. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Cognitive Neuroscience MIT Press

A Computational Model of How Cholinergic Interneurons Protect Striatal-dependent Learning

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References (87)

Publisher
MIT Press
Copyright
© 2011 Massachusetts Institute of Technology
Subject
Articles
ISSN
0898-929X
eISSN
1530-8898
DOI
10.1162/jocn.2010.21523
pmid
20521851
Publisher site
See Article on Publisher Site

Abstract

An essential component of skill acquisition is learning the environmental conditions in which that skill is relevant. This article proposes and tests a neurobiologically detailed theory of how such learning is mediated. The theory assumes that a key component of this learning is provided by the cholinergic interneurons in the striatum known as tonically active neurons (TANs). The TANs are assumed to exert a tonic inhibitory influence over cortical inputs to the striatum that prevents the execution of any striatal-dependent actions. The TANs learn to pause in rewarding environments, and this pause releases the striatal output neurons from this inhibitory effect, thereby facilitating the learning and expression of striatal-dependent behaviors. When rewards are no longer available, the TANs cease to pause, which protects striatal learning from decay. A computational version of this theory accounts for a variety of single-cell recording data and some classic behavioral phenomena, including fast reacquisition after extinction.

Journal

Journal of Cognitive NeuroscienceMIT Press

Published: Jun 1, 2011

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