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How Many Gammas? Redefining Hippocampal Theta-Gamma Dynamic During Spatial Learning

The hippocampal formation is one of the brain systems in which the functional roles of coordinated oscillations in information representation and communication are better studied. Within this circuit, neuronal oscillations are conceived as a mechanism to precisely coordinate upstream and downstream...

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Autores principales: Aguilera, Matthieu, Douchamps, Vincent, Battaglia, Demian, Goutagny, Romain
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8843838/
https://www.ncbi.nlm.nih.gov/pubmed/35177972
http://dx.doi.org/10.3389/fnbeh.2022.811278
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author Aguilera, Matthieu
Douchamps, Vincent
Battaglia, Demian
Goutagny, Romain
author_facet Aguilera, Matthieu
Douchamps, Vincent
Battaglia, Demian
Goutagny, Romain
author_sort Aguilera, Matthieu
collection PubMed
description The hippocampal formation is one of the brain systems in which the functional roles of coordinated oscillations in information representation and communication are better studied. Within this circuit, neuronal oscillations are conceived as a mechanism to precisely coordinate upstream and downstream neuronal ensembles, underlying dynamic exchange of information. Within a global reference framework provided by theta (θ) oscillations, different gamma-frequency (γ) carriers would temporally segregate information originating from different sources, thereby allowing networks to disambiguate convergent inputs. Two γ sub-bands were thus defined according to their frequency (slow γ, 30–80 Hz; medium γ, 60–120 Hz) and differential power distribution across CA1 dendritic layers. According to this prevalent model, layer-specific γ oscillations in CA1 would reliably identify the temporal dynamics of afferent inputs and may therefore aid in identifying specific memory processes (encoding for medium γ vs. retrieval for slow γ). However, this influential view, derived from time-averages of either specific γ sub-bands or different projection methods, might not capture the complexity of CA1 θ-γ interactions. Recent studies investigating γ oscillations at the θ cycle timescale have revealed a more dynamic and diverse landscape of θ-γ motifs, with many θ cycles containing multiple γ bouts of various frequencies. To properly capture the hippocampal oscillatory complexity, we have argued in this review that we should consider the entirety of the data and its multidimensional complexity. This will call for a revision of the actual model and will require the use of new tools allowing the description of individual γ bouts in their full complexity.
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spelling pubmed-88438382022-02-16 How Many Gammas? Redefining Hippocampal Theta-Gamma Dynamic During Spatial Learning Aguilera, Matthieu Douchamps, Vincent Battaglia, Demian Goutagny, Romain Front Behav Neurosci Behavioral Neuroscience The hippocampal formation is one of the brain systems in which the functional roles of coordinated oscillations in information representation and communication are better studied. Within this circuit, neuronal oscillations are conceived as a mechanism to precisely coordinate upstream and downstream neuronal ensembles, underlying dynamic exchange of information. Within a global reference framework provided by theta (θ) oscillations, different gamma-frequency (γ) carriers would temporally segregate information originating from different sources, thereby allowing networks to disambiguate convergent inputs. Two γ sub-bands were thus defined according to their frequency (slow γ, 30–80 Hz; medium γ, 60–120 Hz) and differential power distribution across CA1 dendritic layers. According to this prevalent model, layer-specific γ oscillations in CA1 would reliably identify the temporal dynamics of afferent inputs and may therefore aid in identifying specific memory processes (encoding for medium γ vs. retrieval for slow γ). However, this influential view, derived from time-averages of either specific γ sub-bands or different projection methods, might not capture the complexity of CA1 θ-γ interactions. Recent studies investigating γ oscillations at the θ cycle timescale have revealed a more dynamic and diverse landscape of θ-γ motifs, with many θ cycles containing multiple γ bouts of various frequencies. To properly capture the hippocampal oscillatory complexity, we have argued in this review that we should consider the entirety of the data and its multidimensional complexity. This will call for a revision of the actual model and will require the use of new tools allowing the description of individual γ bouts in their full complexity. Frontiers Media S.A. 2022-02-01 /pmc/articles/PMC8843838/ /pubmed/35177972 http://dx.doi.org/10.3389/fnbeh.2022.811278 Text en Copyright © 2022 Aguilera, Douchamps, Battaglia and Goutagny. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Behavioral Neuroscience
Aguilera, Matthieu
Douchamps, Vincent
Battaglia, Demian
Goutagny, Romain
How Many Gammas? Redefining Hippocampal Theta-Gamma Dynamic During Spatial Learning
title How Many Gammas? Redefining Hippocampal Theta-Gamma Dynamic During Spatial Learning
title_full How Many Gammas? Redefining Hippocampal Theta-Gamma Dynamic During Spatial Learning
title_fullStr How Many Gammas? Redefining Hippocampal Theta-Gamma Dynamic During Spatial Learning
title_full_unstemmed How Many Gammas? Redefining Hippocampal Theta-Gamma Dynamic During Spatial Learning
title_short How Many Gammas? Redefining Hippocampal Theta-Gamma Dynamic During Spatial Learning
title_sort how many gammas? redefining hippocampal theta-gamma dynamic during spatial learning
topic Behavioral Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8843838/
https://www.ncbi.nlm.nih.gov/pubmed/35177972
http://dx.doi.org/10.3389/fnbeh.2022.811278
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