Fast Hebbian plasticity and working memory.


Journal

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

Informations de publication

Date de publication:
Dec 2023
Historique:
received: 14 02 2023
revised: 10 10 2023
accepted: 19 10 2023
medline: 5 12 2023
pubmed: 20 11 2023
entrez: 19 11 2023
Statut: ppublish

Résumé

Theories and models of working memory (WM) were at least since the mid-1990s dominated by the persistent activity hypothesis. The past decade has seen rising concerns about the shortcomings of sustained activity as the mechanism for short-term maintenance of WM information in the light of accumulating experimental evidence for so-called activity-silent WM and the fundamental difficulty in explaining robust multi-item WM. In consequence, alternative theories are now explored mostly in the direction of fast synaptic plasticity as the underlying mechanism. The question of non-Hebbian vs Hebbian synaptic plasticity emerges naturally in this context. In this review, we focus on fast Hebbian plasticity and trace the origins of WM theories and models building on this form of associative learning.

Identifiants

pubmed: 37980802
pii: S0959-4388(23)00134-4
doi: 10.1016/j.conb.2023.102809
pii:
doi:

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

102809

Informations de copyright

Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of competing interest Nothing to declare.

Auteurs

Anders Lansner (A)

Stockholm University, Department of Mathematics, SE-106 91 Stockholm, Sweden; KTH Royal Institute of Technology, Dept of Computational Science and Technology, 100 44 Stockholm, Sweden; SeRC (Swedish e-Science Research Center), Sweden. Electronic address: ala@kth.se.

Florian Fiebig (F)

KTH Royal Institute of Technology, Dept of Computational Science and Technology, 100 44 Stockholm, Sweden. Electronic address: fiebig@kth.se.

Pawel Herman (P)

KTH Royal Institute of Technology, Dept of Computational Science and Technology, 100 44 Stockholm, Sweden; Digital Futures, KTH Royal Institute of Technology, Stockholm, Sweden; SeRC (Swedish e-Science Research Center), Sweden. Electronic address: https://twitter.com/PHermanKTHbrain.

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Classifications MeSH