Continual task learning in natural and artificial agents.
Hebbian gating
machine learning
neural networks
neuroimaging
representational geometry
Journal
Trends in neurosciences
ISSN: 1878-108X
Titre abrégé: Trends Neurosci
Pays: England
ID NLM: 7808616
Informations de publication
Date de publication:
03 2023
03 2023
Historique:
received:
10
10
2022
revised:
07
12
2022
accepted:
15
12
2022
pubmed:
23
1
2023
medline:
25
2
2023
entrez:
22
1
2023
Statut:
ppublish
Résumé
How do humans and other animals learn new tasks? A wave of brain recording studies has investigated how neural representations change during task learning, with a focus on how tasks can be acquired and coded in ways that minimise mutual interference. We review recent work that has explored the geometry and dimensionality of neural task representations in neocortex, and computational models that have exploited these findings to understand how the brain may partition knowledge between tasks. We discuss how ideas from machine learning, including those that combine supervised and unsupervised learning, are helping neuroscientists understand how natural tasks are learned and coded in biological brains.
Identifiants
pubmed: 36682991
pii: S0166-2236(22)00260-0
doi: 10.1016/j.tins.2022.12.006
pii:
doi:
Types de publication
Journal Article
Review
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
199-210Subventions
Organisme : Wellcome Trust
ID : 216386/Z/19/Z
Pays : United Kingdom
Informations de copyright
Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.