Awake hippocampal synchronous events are incorporated into offline neuronal reactivation.

CP: Neuroscience awake synchronization learning neuronal reactivation place cells sharp-wave ripple

Journal

Cell reports
ISSN: 2211-1247
Titre abrégé: Cell Rep
Pays: United States
ID NLM: 101573691

Informations de publication

Date de publication:
29 08 2023
Historique:
received: 21 04 2023
revised: 30 05 2023
accepted: 11 07 2023
medline: 4 9 2023
pubmed: 26 7 2023
entrez: 26 7 2023
Statut: ppublish

Résumé

Learning novel experiences reorganizes hippocampal neuronal circuits, represented as coordinated reactivation patterns in post-experience offline states for memory consolidation. This study examines how awake synchronous events during a novel run are related to post-run reactivation patterns. The disruption of awake sharp-wave ripples inhibited experience-induced increases in the contributions of neurons to post-experience synchronous events. Hippocampal place cells that participate more in awake synchronous events are more strongly reactivated during post-experience synchronous events. Awake synchronous neuronal patterns, in cooperation with place-selective firing patterns, determine cell ensembles that undergo pronounced increases and decreases in their correlated spikes. Taken together, awake synchronous events are fundamental for identifying hippocampal neuronal ensembles to be incorporated into synchronous reactivation during subsequent offline states, thereby facilitating memory consolidation.

Identifiants

pubmed: 37494183
pii: S2211-1247(23)00882-3
doi: 10.1016/j.celrep.2023.112871
pii:
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

112871

Informations de copyright

Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of interests The authors declare no competing interests.

Auteurs

Saichiro Yagi (S)

Laboratory of Chemical Pharmacology, Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo 113-0033, Japan.

Hideyoshi Igata (H)

Laboratory of Chemical Pharmacology, Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo 113-0033, Japan.

Yuji Ikegaya (Y)

Laboratory of Chemical Pharmacology, Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo 113-0033, Japan; Institute for AI and Beyond, The University of Tokyo, Tokyo 113-0033, Japan; Center for Information and Neural Networks, National Institute of Information and Communications Technology, Suita City, Osaka 565-0871, Japan.

Takuya Sasaki (T)

Laboratory of Chemical Pharmacology, Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo 113-0033, Japan; Department of Pharmacology, Graduate School of Pharmaceutical Sciences, Tohoku University, 6-3 Aramaki-Aoba, Aoba-Ku, Sendai 980-8578, Japan. Electronic address: takuya.sasaki.b4@tohoku.ac.jp.

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Classifications MeSH