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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/125832
Ver los metadatos del registro completo
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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 |
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reponame:CONICET Digital (CONICET) instname:Consejo Nacional de Investigaciones Científicas y Técnicas |
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Consejo Nacional de Investigaciones Científicas y Técnicas |
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CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas |
repository.mail.fl_str_mv |
dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar |
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13.22299 |