Recent data on the cerebellum require new models and theories.

Cognition Credit assignment Long-term depression Long-term potentiation Marr-Albus model Purkinje neuron Recurrent connection Reward Supervised learning

Journal

Current opinion in neurobiology
ISSN: 1873-6882
Titre abrégé: Curr Opin Neurobiol
Pays: England
ID NLM: 9111376

Informations de publication

Date de publication:
10 2023
Historique:
received: 09 05 2023
revised: 22 07 2023
accepted: 23 07 2023
medline: 11 9 2023
pubmed: 18 8 2023
entrez: 17 8 2023
Statut: ppublish

Résumé

The cerebellum has been a popular topic for theoretical studies because its structure was thought to be simple. Since David Marr and James Albus related its function to motor skill learning and proposed the Marr-Albus cerebellar learning model, this theory has guided and inspired cerebellar research. In this review, we summarize the theoretical progress that has been made within this framework of error-based supervised learning. We discuss the experimental progress that demonstrates more complicated molecular and cellular mechanisms in the cerebellum as well as new cell types and recurrent connections. We also cover its involvement in diverse non-motor functions and evidence of other forms of learning. Finally, we highlight the need to explain these new experimental findings into an integrated cerebellar model that can unify its diverse computational functions.

Identifiants

pubmed: 37591124
pii: S0959-4388(23)00090-9
doi: 10.1016/j.conb.2023.102765
pii:
doi:

Types de publication

Journal Article Review Research Support, Non-U.S. Gov't Research Support, N.I.H., Extramural

Langues

eng

Sous-ensembles de citation

IM

Pagination

102765

Subventions

Organisme : NINDS NIH HHS
ID : R35 NS097343
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH046742
Pays : United States

Informations de copyright

Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Yunliang Zang (Y)

Academy of Medical Engineering and Translational Medicine, Medical Faculty, Tianjin University, Tianjin 300072, China; Volen Center and Biology Department, Brandeis University, Waltham, MA 02454, USA. Electronic address: yunlianzang@tju.edu.cn.

Erik De Schutter (E)

Computational Neuroscience Unit, Okinawa Institute of Science and Technology, Japan. Electronic address: https://twitter.com/DeschutterOIST.

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Classifications MeSH