Journal

bioRxiv : the preprint server for biology
Titre abrégé: bioRxiv
Pays: United States
ID NLM: 101680187

Informations de publication

Date de publication:
24 May 2023
Historique:
pubmed: 9 6 2023
medline: 9 6 2023
entrez: 9 6 2023
Statut: epublish

Résumé

Animals can quickly adapt learned movements in response to external perturbations. Motor adaptation is likely influenced by an animal's existing movement repertoire, but the nature of this influence is unclear. Long-term learning causes lasting changes in neural connectivity which determine the activity patterns that can be produced. Here, we sought to understand how a neural population's activity repertoire, acquired through long-term learning, affects short-term adaptation by modeling motor cortical neural population dynamics during

Identifiants

pubmed: 37293081
doi: 10.1101/2023.05.23.541925
pmc: PMC10245862
pii:
doi:

Types de publication

Preprint

Langues

eng

Subventions

Organisme : Wellcome Trust
Pays : United Kingdom
Organisme : NINDS NIH HHS
ID : R01 NS053603
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS074044
Pays : United States

Déclaration de conflit d'intérêts

Competing Interests J.A.G. receives funding from Meta Platform Technologies, LLC.

Auteurs

Joanna C Chang (JC)

Department of Bioengineering, Imperial College London, London, UK.

Matthew G Perich (MG)

Département de neurosciences, Université de Montréal, Montréal, Canada.

Lee E Miller (LE)

Department of Neuroscience, Northwestern University, USA.
Department of Biomedical Engineering, Northwestern University, Evanston, IL, USA.
Department of Physical Medicine and Rehabilitation, Northwestern University, and Shirley Ryan Ability Lab, Chicago, IL, USA.

Juan A Gallego (JA)

Department of Bioengineering, Imperial College London, London, UK.

Claudia Clopath (C)

Department of Bioengineering, Imperial College London, London, UK.

Classifications MeSH