Harnessing behavioral diversity to understand neural computations for cognition.
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 2019
10 2019
Historique:
received:
29
01
2019
revised:
28
08
2019
accepted:
11
09
2019
pubmed:
2
11
2019
medline:
12
2
2020
entrez:
1
11
2019
Statut:
ppublish
Résumé
With the increasing acquisition of large-scale neural recordings comes the challenge of inferring the computations they perform and understanding how these give rise to behavior. Here, we review emerging conceptual and technological advances that begin to address this challenge, garnering insights from both biological and artificial neural networks. We argue that neural data should be recorded during rich behavioral tasks, to model cognitive processes and estimate latent behavioral variables. Careful quantification of animal movements can also provide a more complete picture of how movements shape neural dynamics and reflect changes in brain state, such as arousal or stress. Artificial neural networks (ANNs) could serve as artificial model organisms to connect neural dynamics and rich behavioral data. ANNs have already begun to reveal how a wide range of different behaviors can be implemented, generating hypotheses about how observed neural activity might drive behavior and explaining diversity in behavioral strategies.
Identifiants
pubmed: 31670073
pii: S0959-4388(19)30028-5
doi: 10.1016/j.conb.2019.09.011
pmc: PMC6931281
mid: NIHMS1542909
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
229-238Subventions
Organisme : NEI NIH HHS
ID : R01 EY022979
Pays : United States
Informations de copyright
Copyright © 2019 Elsevier Ltd. All rights reserved.
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