Motor cortex latent dynamics encode spatial and temporal arm movement parameters independently.


Journal

The Journal of neuroscience : the official journal of the Society for Neuroscience
ISSN: 1529-2401
Titre abrégé: J Neurosci
Pays: United States
ID NLM: 8102140

Informations de publication

Date de publication:
26 Jul 2024
Historique:
received: 19 09 2023
revised: 12 07 2024
accepted: 17 07 2024
medline: 27 7 2024
pubmed: 27 7 2024
entrez: 26 7 2024
Statut: aheadofprint

Résumé

The fluid movement of an arm requires multiple spatiotemporal parameters to be set independently. Recent studies have argued that arm movements are generated by the collective dynamics of neurons in motor cortex. An untested prediction of this hypothesis is that independent parameters of movement must map to independent components of the neural dynamics. Using a task where three male monkeys made a sequence of reaching movements to randomly placed targets, we show that the spatial and temporal parameters of arm movements are independently encoded in the low-dimensional trajectories of population activity in motor cortex: Each movement's direction corresponds to a fixed neural trajectory through neural state space and its speed to how quickly that trajectory is traversed. Recurrent neural network models show this coding allows independent control over the spatial and temporal parameters of movement by separate network parameters. Our results support a key prediction of the dynamical systems view of motor cortex, but also argue that not all parameters of movement are defined by different trajectories of population activity

Identifiants

pubmed: 39060178
pii: JNEUROSCI.1777-23.2024
doi: 10.1523/JNEUROSCI.1777-23.2024
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

Copyright © 2024 Colins Rodriguez et al.

Auteurs

Andrea Colins Rodriguez (A)

School of Psychology, University of Nottingham, Nottingham, United Kingdom.

Matthew G Perich (MG)

Département de neurosciences, Faculté de médecine, Université de Montréal. Montréal, Canada.
Québec Artificial Intelligence Institute (Mila), Québec, Canada.

Lee Miller (L)

Northwestern University, Department of Biomedical Engineering, Chicago, USA.

Mark D Humphries (MD)

School of Psychology, University of Nottingham, Nottingham, United Kingdom mark.humphries@nottingham.ac.uk.

Classifications MeSH