Transient oscillations as computations for cognition: Analysis, modeling and function.


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: 31 01 2023
revised: 31 08 2023
accepted: 06 09 2023
medline: 5 12 2023
pubmed: 8 10 2023
entrez: 7 10 2023
Statut: ppublish

Résumé

Our view of neural oscillations is currently changing. The dominant picture of sustained oscillations is now often replaced by transient oscillations occurring in bursts. This phenomenon seems to be quite comprehensive, as it has been reported for different oscillation frequencies, including the theta, beta, and gamma bands, as well as cortical and subcortical regions in a variety of cognitive tasks and species. Here we review recent developments in their analysis, computational modeling, and functional roles. For the analysis of transient oscillations methods using lagged coherence and Hidden Markov Models have been developed and applied in recent studies to ascertain their transient nature and study their contribution to cognitive functions. Furthermore, computational models have been developed that account for their stochastic nature, which poses interesting functional constraints. Finally, as transient oscillations have been observed across many species, they are likely of functional significance and we consider challenges in characterizing their function.

Identifiants

pubmed: 37804772
pii: S0959-4388(23)00121-6
doi: 10.1016/j.conb.2023.102796
pii:
doi:

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

102796

Informations de copyright

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

Déclaration de conflit d'intérêts

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Robert Schmidt (R)

Institute for Neural Computation, Faculty of Computer Science, Ruhr-University Bochum, Germany. Electronic address: robert.schmidt@rub.de.

Jonas Rose (J)

Neural Basis of Learning, Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Germany.

Vignesh Muralidharan (V)

Center for Brain Science and Application, School of AI and Data Science, Indian Institute of Technology Jodhpur, India. Electronic address: https://twitter.com/vigmdhrn.

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Classifications MeSH