Universal and nonuniversal neural dynamics on small world connectomes: A finite-size scaling analysis

Autores
Zarepour Nasir Abadi, Mahdi; Perotti, Juan Ignacio; Billoni, Orlando Vito; Chialvo, Dante Renato; Cannas, Sergio Alejandro
Año de publicación
2019
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Evidence of critical dynamics has been found recently in both experiments and models of large-scale brain dynamics. The understanding of the nature and features of such a critical regime is hampered by the relatively small size of the available connectome, which prevents, among other things, the determination of its associated universality class. To circumvent that, here we study a neural model defined on a class of small-world networks that share some topological features with the human connectome. We find that varying the topological parameters can give rise to a scale-invariant behavior either belonging to the mean-field percolation universality class or having nonuniversal critical exponents. In addition, we find certain regions of the topological parameter space where the system presents a discontinuous, i.e., noncritical, dynamical phase transition into a percolated state. Overall, these results shed light on the interplay of dynamical and topological roots of the complex brain dynamics.
Fil: Zarepour Nasir Abadi, Mahdi. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Física Enrique Gaviola. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; Argentina
Fil: Perotti, Juan Ignacio. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Física Enrique Gaviola. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; Argentina
Fil: Billoni, Orlando Vito. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Física Enrique Gaviola. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; Argentina
Fil: Chialvo, Dante Renato. Universidad Nacional de San Martín; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Cannas, Sergio Alejandro. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Física Enrique Gaviola. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; Argentina
Materia
NEURAL NETWORKS
CRITICALITY
FINITE SIZE SCALING
SMALL WORLD NETWORKS
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/125832
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/125832
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Universal and nonuniversal neural dynamics on small world connectomes: A finite-size scaling analysisZarepour Nasir Abadi, MahdiPerotti, Juan IgnacioBilloni, Orlando VitoChialvo, Dante RenatoCannas, Sergio AlejandroNEURAL NETWORKSCRITICALITYFINITE SIZE SCALINGSMALL WORLD NETWORKShttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1Evidence of critical dynamics has been found recently in both experiments and models of large-scale brain dynamics. The understanding of the nature and features of such a critical regime is hampered by the relatively small size of the available connectome, which prevents, among other things, the determination of its associated universality class. To circumvent that, here we study a neural model defined on a class of small-world networks that share some topological features with the human connectome. We find that varying the topological parameters can give rise to a scale-invariant behavior either belonging to the mean-field percolation universality class or having nonuniversal critical exponents. In addition, we find certain regions of the topological parameter space where the system presents a discontinuous, i.e., noncritical, dynamical phase transition into a percolated state. Overall, these results shed light on the interplay of dynamical and topological roots of the complex brain dynamics.Fil: Zarepour Nasir Abadi, Mahdi. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Física Enrique Gaviola. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; ArgentinaFil: Perotti, Juan Ignacio. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Física Enrique Gaviola. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; ArgentinaFil: Billoni, Orlando Vito. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Física Enrique Gaviola. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; ArgentinaFil: Chialvo, Dante Renato. Universidad Nacional de San Martín; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Cannas, Sergio Alejandro. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Física Enrique Gaviola. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; ArgentinaAmerican Physical Society2019-11-25info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/125832Zarepour Nasir Abadi, Mahdi; Perotti, Juan Ignacio; Billoni, Orlando Vito; Chialvo, Dante Renato; Cannas, Sergio Alejandro; Universal and nonuniversal neural dynamics on small world connectomes: A finite-size scaling analysis; American Physical Society; Physical Review E; 100; 5; 25-11-2019; 0521382470-00452470-0053CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://link.aps.org/doi/10.1103/PhysRevE.100.052138info:eu-repo/semantics/altIdentifier/doi/10.1103/PhysRevE.100.052138info:eu-repo/semantics/altIdentifier/arxiv/https://arxiv.org/abs/1905.05280info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T15:10:55Zoai:ri.conicet.gov.ar:11336/125832instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-15 15:10:55.413CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Universal and nonuniversal neural dynamics on small world connectomes: A finite-size scaling analysis
title Universal and nonuniversal neural dynamics on small world connectomes: A finite-size scaling analysis
spellingShingle Universal and nonuniversal neural dynamics on small world connectomes: A finite-size scaling analysis
Zarepour Nasir Abadi, Mahdi
NEURAL NETWORKS
CRITICALITY
FINITE SIZE SCALING
SMALL WORLD NETWORKS
title_short Universal and nonuniversal neural dynamics on small world connectomes: A finite-size scaling analysis
title_full Universal and nonuniversal neural dynamics on small world connectomes: A finite-size scaling analysis
title_fullStr Universal and nonuniversal neural dynamics on small world connectomes: A finite-size scaling analysis
title_full_unstemmed Universal and nonuniversal neural dynamics on small world connectomes: A finite-size scaling analysis
title_sort Universal and nonuniversal neural dynamics on small world connectomes: A finite-size scaling analysis
dc.creator.none.fl_str_mv Zarepour Nasir Abadi, Mahdi
Perotti, Juan Ignacio
Billoni, Orlando Vito
Chialvo, Dante Renato
Cannas, Sergio Alejandro
author Zarepour Nasir Abadi, Mahdi
author_facet Zarepour Nasir Abadi, Mahdi
Perotti, Juan Ignacio
Billoni, Orlando Vito
Chialvo, Dante Renato
Cannas, Sergio Alejandro
author_role author
author2 Perotti, Juan Ignacio
Billoni, Orlando Vito
Chialvo, Dante Renato
Cannas, Sergio Alejandro
author2_role author
author
author
author
dc.subject.none.fl_str_mv NEURAL NETWORKS
CRITICALITY
FINITE SIZE SCALING
SMALL WORLD NETWORKS
topic NEURAL NETWORKS
CRITICALITY
FINITE SIZE SCALING
SMALL WORLD NETWORKS
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Evidence of critical dynamics has been found recently in both experiments and models of large-scale brain dynamics. The understanding of the nature and features of such a critical regime is hampered by the relatively small size of the available connectome, which prevents, among other things, the determination of its associated universality class. To circumvent that, here we study a neural model defined on a class of small-world networks that share some topological features with the human connectome. We find that varying the topological parameters can give rise to a scale-invariant behavior either belonging to the mean-field percolation universality class or having nonuniversal critical exponents. In addition, we find certain regions of the topological parameter space where the system presents a discontinuous, i.e., noncritical, dynamical phase transition into a percolated state. Overall, these results shed light on the interplay of dynamical and topological roots of the complex brain dynamics.
Fil: Zarepour Nasir Abadi, Mahdi. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Física Enrique Gaviola. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; Argentina
Fil: Perotti, Juan Ignacio. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Física Enrique Gaviola. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; Argentina
Fil: Billoni, Orlando Vito. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Física Enrique Gaviola. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; Argentina
Fil: Chialvo, Dante Renato. Universidad Nacional de San Martín; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Cannas, Sergio Alejandro. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Física Enrique Gaviola. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; Argentina
description Evidence of critical dynamics has been found recently in both experiments and models of large-scale brain dynamics. The understanding of the nature and features of such a critical regime is hampered by the relatively small size of the available connectome, which prevents, among other things, the determination of its associated universality class. To circumvent that, here we study a neural model defined on a class of small-world networks that share some topological features with the human connectome. We find that varying the topological parameters can give rise to a scale-invariant behavior either belonging to the mean-field percolation universality class or having nonuniversal critical exponents. In addition, we find certain regions of the topological parameter space where the system presents a discontinuous, i.e., noncritical, dynamical phase transition into a percolated state. Overall, these results shed light on the interplay of dynamical and topological roots of the complex brain dynamics.
publishDate 2019
dc.date.none.fl_str_mv 2019-11-25
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/125832
Zarepour Nasir Abadi, Mahdi; Perotti, Juan Ignacio; Billoni, Orlando Vito; Chialvo, Dante Renato; Cannas, Sergio Alejandro; Universal and nonuniversal neural dynamics on small world connectomes: A finite-size scaling analysis; American Physical Society; Physical Review E; 100; 5; 25-11-2019; 052138
2470-0045
2470-0053
CONICET Digital
CONICET
url http://hdl.handle.net/11336/125832
identifier_str_mv Zarepour Nasir Abadi, Mahdi; Perotti, Juan Ignacio; Billoni, Orlando Vito; Chialvo, Dante Renato; Cannas, Sergio Alejandro; Universal and nonuniversal neural dynamics on small world connectomes: A finite-size scaling analysis; American Physical Society; Physical Review E; 100; 5; 25-11-2019; 052138
2470-0045
2470-0053
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://link.aps.org/doi/10.1103/PhysRevE.100.052138
info:eu-repo/semantics/altIdentifier/doi/10.1103/PhysRevE.100.052138
info:eu-repo/semantics/altIdentifier/arxiv/https://arxiv.org/abs/1905.05280
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv American Physical Society
publisher.none.fl_str_mv American Physical Society
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1846083257704120320
score 13.22299

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