The contribution of the basal ganglia and cerebellum to motor learning: A neuro-computational approach.

Motor learning involves a widespread brain network including the basal ganglia, cerebellum, motor cortex, and brainstem. Despite its importance, little is known about how this network learns motor tasks and which role different parts of this network take. We designed a systems-level computational mo...

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Main Authors: Javier Baladron, Julien Vitay, Torsten Fietzek, Fred H Hamker
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2023-04-01
Series:PLoS Computational Biology
Online Access:https://doi.org/10.1371/journal.pcbi.1011024
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author Javier Baladron
Julien Vitay
Torsten Fietzek
Fred H Hamker
author_facet Javier Baladron
Julien Vitay
Torsten Fietzek
Fred H Hamker
author_sort Javier Baladron
collection DOAJ
description Motor learning involves a widespread brain network including the basal ganglia, cerebellum, motor cortex, and brainstem. Despite its importance, little is known about how this network learns motor tasks and which role different parts of this network take. We designed a systems-level computational model of motor learning, including a cortex-basal ganglia motor loop and the cerebellum that both determine the response of central pattern generators in the brainstem. First, we demonstrate its ability to learn arm movements toward different motor goals. Second, we test the model in a motor adaptation task with cognitive control, where the model replicates human data. We conclude that the cortex-basal ganglia loop learns via a novelty-based motor prediction error to determine concrete actions given a desired outcome, and that the cerebellum minimizes the remaining aiming error.
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spelling doaj.art-ed904378eee64d36afb595bb11c9fe382023-06-24T05:31:05ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582023-04-01194e101102410.1371/journal.pcbi.1011024The contribution of the basal ganglia and cerebellum to motor learning: A neuro-computational approach.Javier BaladronJulien VitayTorsten FietzekFred H HamkerMotor learning involves a widespread brain network including the basal ganglia, cerebellum, motor cortex, and brainstem. Despite its importance, little is known about how this network learns motor tasks and which role different parts of this network take. We designed a systems-level computational model of motor learning, including a cortex-basal ganglia motor loop and the cerebellum that both determine the response of central pattern generators in the brainstem. First, we demonstrate its ability to learn arm movements toward different motor goals. Second, we test the model in a motor adaptation task with cognitive control, where the model replicates human data. We conclude that the cortex-basal ganglia loop learns via a novelty-based motor prediction error to determine concrete actions given a desired outcome, and that the cerebellum minimizes the remaining aiming error.https://doi.org/10.1371/journal.pcbi.1011024
spellingShingle Javier Baladron
Julien Vitay
Torsten Fietzek
Fred H Hamker
The contribution of the basal ganglia and cerebellum to motor learning: A neuro-computational approach.
PLoS Computational Biology
title The contribution of the basal ganglia and cerebellum to motor learning: A neuro-computational approach.
title_full The contribution of the basal ganglia and cerebellum to motor learning: A neuro-computational approach.
title_fullStr The contribution of the basal ganglia and cerebellum to motor learning: A neuro-computational approach.
title_full_unstemmed The contribution of the basal ganglia and cerebellum to motor learning: A neuro-computational approach.
title_short The contribution of the basal ganglia and cerebellum to motor learning: A neuro-computational approach.
title_sort contribution of the basal ganglia and cerebellum to motor learning a neuro computational approach
url https://doi.org/10.1371/journal.pcbi.1011024
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