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A Proposed Circuit Computation in Basal Ganglia: History‐Dependent Gain

In this Scientific Perspectives we first review the recent advances in our understanding of the functional architecture of basal ganglia circuits. Then we argue that these data can best be explained by a model in which basal ganglia act to control the gain of movement kinematics to shape performance...

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Autores principales: Yttri, Eric Allen, Dudman, Joshua Tate
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6001446/
https://www.ncbi.nlm.nih.gov/pubmed/29575303
http://dx.doi.org/10.1002/mds.27321
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author Yttri, Eric Allen
Dudman, Joshua Tate
author_facet Yttri, Eric Allen
Dudman, Joshua Tate
author_sort Yttri, Eric Allen
collection PubMed
description In this Scientific Perspectives we first review the recent advances in our understanding of the functional architecture of basal ganglia circuits. Then we argue that these data can best be explained by a model in which basal ganglia act to control the gain of movement kinematics to shape performance based on prior experience, which we refer to as a history‐dependent gain computation. Finally, we discuss how insights from the history‐dependent gain model might translate from the bench to the bedside, primarily the implications for the design of adaptive deep brain stimulation. Thus, we explicate the key empirical and conceptual support for a normative, computational model with substantial explanatory power for the broad role of basal ganglia circuits in health and disease. © 2018 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.
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spelling pubmed-60014462018-06-21 A Proposed Circuit Computation in Basal Ganglia: History‐Dependent Gain Yttri, Eric Allen Dudman, Joshua Tate Mov Disord Scientific Perspectives In this Scientific Perspectives we first review the recent advances in our understanding of the functional architecture of basal ganglia circuits. Then we argue that these data can best be explained by a model in which basal ganglia act to control the gain of movement kinematics to shape performance based on prior experience, which we refer to as a history‐dependent gain computation. Finally, we discuss how insights from the history‐dependent gain model might translate from the bench to the bedside, primarily the implications for the design of adaptive deep brain stimulation. Thus, we explicate the key empirical and conceptual support for a normative, computational model with substantial explanatory power for the broad role of basal ganglia circuits in health and disease. © 2018 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society. John Wiley and Sons Inc. 2018-03-24 2018-05 /pmc/articles/PMC6001446/ /pubmed/29575303 http://dx.doi.org/10.1002/mds.27321 Text en © 2018 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
spellingShingle Scientific Perspectives
Yttri, Eric Allen
Dudman, Joshua Tate
A Proposed Circuit Computation in Basal Ganglia: History‐Dependent Gain
title A Proposed Circuit Computation in Basal Ganglia: History‐Dependent Gain
title_full A Proposed Circuit Computation in Basal Ganglia: History‐Dependent Gain
title_fullStr A Proposed Circuit Computation in Basal Ganglia: History‐Dependent Gain
title_full_unstemmed A Proposed Circuit Computation in Basal Ganglia: History‐Dependent Gain
title_short A Proposed Circuit Computation in Basal Ganglia: History‐Dependent Gain
title_sort proposed circuit computation in basal ganglia: history‐dependent gain
topic Scientific Perspectives
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6001446/
https://www.ncbi.nlm.nih.gov/pubmed/29575303
http://dx.doi.org/10.1002/mds.27321
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