Symmetries and synchronization from whole-neural activity in

Bryant Avila, Pedro Augusto, David Phillips, Tommaso Gili, Manuel Zimmer, Hernán A Makse

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Article in ArXiv, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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4 · The record

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Symmetries and synchronization from whole-neural activity in the2025

5 · Who and what money

Authors and funding

6 authors.

Bryant AvilaLevich Institute and Physics Department, City College of New York, New York, NY 10031, USA.

Pedro AugustoDepartment of Neuroscience and Developmental Biology, University of Vienna, Vienna Biocenter (VBC), Vienna, Austria.

David PhillipsMechanical Engineering Department, University of New Mexico, Albuquerque, NM 87131, USA.

Tommaso GiliNetworks Unit, IMT Scuola Alti Studi Lucca, Piazza San Francesco 15, 55100, Lucca, Italy.

Manuel ZimmerDepartment of Neuroscience and Developmental Biology, University of Vienna, Vienna Biocenter (VBC), Vienna, Austria.

Hernán A MakseLevich Institute and Physics Department, City College of New York, New York, NY 10031, USA.

Funding

Application of the principle of symmetry to neural circuitry: From building blocks to neural synchronization in the connectomeR01EB028157 · NIBIB · CITY COLLEGE OF NEW YORK · PI MAKSE, HERNAN, ZIMMER, MANUEL · 2020 to 2020

$1.1M

NIBIB NIH HHS R01 EB028157Wellcome Trust

6 · The paper itself

Abstract

Understanding the dynamical behavior of complex systems from their underlying network architectures is a long-standing question in complexity theory. Therefore, many metrics have been devised to extract network features like motifs, centrality, and modularity measures. It has previously been proposed that network symmetries are of particular importance since they are expected to underly the synchronization of a system's units, which is ubiquitously observed in nervous system activity patterns. However, perfectly symmetrical structures are difficult to assess in noisy measurements of biological systems, like neuronal connectomes. Here, we devise a principled method to infer network symmetries from combined connectome and neuronal activity data. Using nervous system-wide population activity recordings of the

Identifiers

PMID39279832

PMCPMC11398546

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.