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Quasiuniversal scaling in mouse-brain neuronal activity stems from edge-of-instability critical dynamics
The brain is in a state of perpetual reverberant neural activity, even in the absence of specific tasks or stimuli. Shedding light on the origin and functional significance of such a dynamical state is essential to understanding how the brain transmits, processes, and stores information. An inspirin...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
National Academy of Sciences
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9992863/ https://www.ncbi.nlm.nih.gov/pubmed/36827262 http://dx.doi.org/10.1073/pnas.2208998120 |
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author | Morales, Guillermo B. di Santo, Serena Muñoz, Miguel A. |
author_facet | Morales, Guillermo B. di Santo, Serena Muñoz, Miguel A. |
author_sort | Morales, Guillermo B. |
collection | PubMed |
description | The brain is in a state of perpetual reverberant neural activity, even in the absence of specific tasks or stimuli. Shedding light on the origin and functional significance of such a dynamical state is essential to understanding how the brain transmits, processes, and stores information. An inspiring, albeit controversial, conjecture proposes that some statistical characteristics of empirically observed neuronal activity can be understood by assuming that brain networks operate in a dynamical regime with features, including the emergence of scale invariance, resembling those seen typically near phase transitions. Here, we present a data-driven analysis based on simultaneous high-throughput recordings of the activity of thousands of individual neurons in various regions of the mouse brain. To analyze these data, we construct a unified theoretical framework that synergistically combines a phenomenological renormalization group approach and techniques that infer the general dynamical state of a neural population, while designing complementary tools. This strategy allows us to uncover strong signatures of scale invariance that are “quasiuniversal” across brain regions and experiments, revealing that all the analyzed areas operate, to a greater or lesser extent, near the edge of instability. |
format | Online Article Text |
id | pubmed-9992863 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | National Academy of Sciences |
record_format | MEDLINE/PubMed |
spelling | pubmed-99928632023-08-24 Quasiuniversal scaling in mouse-brain neuronal activity stems from edge-of-instability critical dynamics Morales, Guillermo B. di Santo, Serena Muñoz, Miguel A. Proc Natl Acad Sci U S A Physical Sciences The brain is in a state of perpetual reverberant neural activity, even in the absence of specific tasks or stimuli. Shedding light on the origin and functional significance of such a dynamical state is essential to understanding how the brain transmits, processes, and stores information. An inspiring, albeit controversial, conjecture proposes that some statistical characteristics of empirically observed neuronal activity can be understood by assuming that brain networks operate in a dynamical regime with features, including the emergence of scale invariance, resembling those seen typically near phase transitions. Here, we present a data-driven analysis based on simultaneous high-throughput recordings of the activity of thousands of individual neurons in various regions of the mouse brain. To analyze these data, we construct a unified theoretical framework that synergistically combines a phenomenological renormalization group approach and techniques that infer the general dynamical state of a neural population, while designing complementary tools. This strategy allows us to uncover strong signatures of scale invariance that are “quasiuniversal” across brain regions and experiments, revealing that all the analyzed areas operate, to a greater or lesser extent, near the edge of instability. National Academy of Sciences 2023-02-24 2023-02-28 /pmc/articles/PMC9992863/ /pubmed/36827262 http://dx.doi.org/10.1073/pnas.2208998120 Text en Copyright © 2023 the Author(s). Published by PNAS. https://creativecommons.org/licenses/by-nc-nd/4.0/This article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) . |
spellingShingle | Physical Sciences Morales, Guillermo B. di Santo, Serena Muñoz, Miguel A. Quasiuniversal scaling in mouse-brain neuronal activity stems from edge-of-instability critical dynamics |
title | Quasiuniversal scaling in mouse-brain neuronal activity stems from edge-of-instability critical dynamics |
title_full | Quasiuniversal scaling in mouse-brain neuronal activity stems from edge-of-instability critical dynamics |
title_fullStr | Quasiuniversal scaling in mouse-brain neuronal activity stems from edge-of-instability critical dynamics |
title_full_unstemmed | Quasiuniversal scaling in mouse-brain neuronal activity stems from edge-of-instability critical dynamics |
title_short | Quasiuniversal scaling in mouse-brain neuronal activity stems from edge-of-instability critical dynamics |
title_sort | quasiuniversal scaling in mouse-brain neuronal activity stems from edge-of-instability critical dynamics |
topic | Physical Sciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9992863/ https://www.ncbi.nlm.nih.gov/pubmed/36827262 http://dx.doi.org/10.1073/pnas.2208998120 |
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