Finite-size correlation behavior near a critical point: a simple metric for monitoring the state of a neural network

Autores
Aguilar Trejo, Eyisto José; Mártin, Daniel Alejandro; Grigera, Tomas Sebastian; Cannas, Sergio Alejandro; Chialvo, Dante Renato
Año de publicación
2022
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In this note, a correlation metric κc is proposed which is based on the universal behavior of the linear/logarithmic growth of the correlation length near/far the critical point of a continuous phase transition. The problem is studied on a previously described neuronal network model for which is known the scaling of the correlation length with the size of the observation region. It is verified that the κc metric is maximized for the conditions at which a power law distribution of neuronal avalanches sizes is observed, thus characterizing well the critical state of the network. Potential applications and limitations for its use with currently available optical imaging techniques are discussed.
Fil: Aguilar Trejo, Eyisto José. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de San Martin. Escuela de Ciencia y Tecnologia. Centro de Estudios Multidisciplinarios En Sistemas Complejos y Ciencias del Cerebro.; Argentina
Fil: Mártin, Daniel Alejandro. Universidad Nacional de San Martin. Escuela de Ciencia y Tecnologia. Centro de Estudios Multidisciplinarios En Sistemas Complejos y Ciencias del Cerebro.; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Grigera, Tomas Sebastian. Facultad de Ciencias Exactas, Universidad Nacional de la Plata; Argentina. Consiglio Nazionale delle Ricerche; Italia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física de Líquidos y Sistemas Biológicos. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física de Líquidos y Sistemas Biológicos; Argentina
Fil: Cannas, Sergio Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas; 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. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de San Martin. Escuela de Ciencia y Tecnologia. Centro de Estudios Multidisciplinarios En Sistemas Complejos y Ciencias del Cerebro.; Argentina
Materia
DISORDERED SYSTEMS
NEURAL NETWORKS
COMPLEX SYSTEMS
FINITE-SIZE CORRELATION
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:11336/188025
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/188025
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Finite-size correlation behavior near a critical point: a simple metric for monitoring the state of a neural networkAguilar Trejo, Eyisto JoséMártin, Daniel AlejandroGrigera, Tomas SebastianCannas, Sergio AlejandroChialvo, Dante RenatoDISORDERED SYSTEMSNEURAL NETWORKSCOMPLEX SYSTEMSFINITE-SIZE CORRELATIONhttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1In this note, a correlation metric κc is proposed which is based on the universal behavior of the linear/logarithmic growth of the correlation length near/far the critical point of a continuous phase transition. The problem is studied on a previously described neuronal network model for which is known the scaling of the correlation length with the size of the observation region. It is verified that the κc metric is maximized for the conditions at which a power law distribution of neuronal avalanches sizes is observed, thus characterizing well the critical state of the network. Potential applications and limitations for its use with currently available optical imaging techniques are discussed.Fil: Aguilar Trejo, Eyisto José. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de San Martin. Escuela de Ciencia y Tecnologia. Centro de Estudios Multidisciplinarios En Sistemas Complejos y Ciencias del Cerebro.; ArgentinaFil: Mártin, Daniel Alejandro. Universidad Nacional de San Martin. Escuela de Ciencia y Tecnologia. Centro de Estudios Multidisciplinarios En Sistemas Complejos y Ciencias del Cerebro.; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Grigera, Tomas Sebastian. Facultad de Ciencias Exactas, Universidad Nacional de la Plata; Argentina. Consiglio Nazionale delle Ricerche; Italia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física de Líquidos y Sistemas Biológicos. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física de Líquidos y Sistemas Biológicos; ArgentinaFil: Cannas, Sergio Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas; 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. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de San Martin. Escuela de Ciencia y Tecnologia. Centro de Estudios Multidisciplinarios En Sistemas Complejos y Ciencias del Cerebro.; ArgentinaAmerican Physical Society2022-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/188025Aguilar Trejo, Eyisto José; Mártin, Daniel Alejandro; Grigera, Tomas Sebastian; Cannas, Sergio Alejandro; Chialvo, Dante Renato; Finite-size correlation behavior near a critical point: a simple metric for monitoring the state of a neural network; American Physical Society; Physical Review E: Statistical, Nonlinear and Soft Matter Physics; 106; 054313; 5-2022; 1-52331-8422CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://doi.org/10.48550/arxiv.2205.11341info:eu-repo/semantics/altIdentifier/doi/10.48550/ARXIV.2205.11341info:eu-repo/semantics/altIdentifier/url/https://journals.aps.org/pre/abstract/10.1103/PhysRevE.106.054313info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T15:37:26Zoai:ri.conicet.gov.ar:11336/188025instacron: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 15:37:27.096CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Finite-size correlation behavior near a critical point: a simple metric for monitoring the state of a neural network
title Finite-size correlation behavior near a critical point: a simple metric for monitoring the state of a neural network
spellingShingle Finite-size correlation behavior near a critical point: a simple metric for monitoring the state of a neural network
Aguilar Trejo, Eyisto José
DISORDERED SYSTEMS
NEURAL NETWORKS
COMPLEX SYSTEMS
FINITE-SIZE CORRELATION
title_short Finite-size correlation behavior near a critical point: a simple metric for monitoring the state of a neural network
title_full Finite-size correlation behavior near a critical point: a simple metric for monitoring the state of a neural network
title_fullStr Finite-size correlation behavior near a critical point: a simple metric for monitoring the state of a neural network
title_full_unstemmed Finite-size correlation behavior near a critical point: a simple metric for monitoring the state of a neural network
title_sort Finite-size correlation behavior near a critical point: a simple metric for monitoring the state of a neural network
dc.creator.none.fl_str_mv Aguilar Trejo, Eyisto José
Mártin, Daniel Alejandro
Grigera, Tomas Sebastian
Cannas, Sergio Alejandro
Chialvo, Dante Renato
author Aguilar Trejo, Eyisto José
author_facet Aguilar Trejo, Eyisto José
Mártin, Daniel Alejandro
Grigera, Tomas Sebastian
Cannas, Sergio Alejandro
Chialvo, Dante Renato
author_role author
author2 Mártin, Daniel Alejandro
Grigera, Tomas Sebastian
Cannas, Sergio Alejandro
Chialvo, Dante Renato
author2_role author
author
author
author
dc.subject.none.fl_str_mv DISORDERED SYSTEMS
NEURAL NETWORKS
COMPLEX SYSTEMS
FINITE-SIZE CORRELATION
topic DISORDERED SYSTEMS
NEURAL NETWORKS
COMPLEX SYSTEMS
FINITE-SIZE CORRELATION
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv In this note, a correlation metric κc is proposed which is based on the universal behavior of the linear/logarithmic growth of the correlation length near/far the critical point of a continuous phase transition. The problem is studied on a previously described neuronal network model for which is known the scaling of the correlation length with the size of the observation region. It is verified that the κc metric is maximized for the conditions at which a power law distribution of neuronal avalanches sizes is observed, thus characterizing well the critical state of the network. Potential applications and limitations for its use with currently available optical imaging techniques are discussed.
Fil: Aguilar Trejo, Eyisto José. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de San Martin. Escuela de Ciencia y Tecnologia. Centro de Estudios Multidisciplinarios En Sistemas Complejos y Ciencias del Cerebro.; Argentina
Fil: Mártin, Daniel Alejandro. Universidad Nacional de San Martin. Escuela de Ciencia y Tecnologia. Centro de Estudios Multidisciplinarios En Sistemas Complejos y Ciencias del Cerebro.; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Grigera, Tomas Sebastian. Facultad de Ciencias Exactas, Universidad Nacional de la Plata; Argentina. Consiglio Nazionale delle Ricerche; Italia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física de Líquidos y Sistemas Biológicos. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física de Líquidos y Sistemas Biológicos; Argentina
Fil: Cannas, Sergio Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas; 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. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de San Martin. Escuela de Ciencia y Tecnologia. Centro de Estudios Multidisciplinarios En Sistemas Complejos y Ciencias del Cerebro.; Argentina
description In this note, a correlation metric κc is proposed which is based on the universal behavior of the linear/logarithmic growth of the correlation length near/far the critical point of a continuous phase transition. The problem is studied on a previously described neuronal network model for which is known the scaling of the correlation length with the size of the observation region. It is verified that the κc metric is maximized for the conditions at which a power law distribution of neuronal avalanches sizes is observed, thus characterizing well the critical state of the network. Potential applications and limitations for its use with currently available optical imaging techniques are discussed.
publishDate 2022
dc.date.none.fl_str_mv 2022-05
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/11336/188025
Aguilar Trejo, Eyisto José; Mártin, Daniel Alejandro; Grigera, Tomas Sebastian; Cannas, Sergio Alejandro; Chialvo, Dante Renato; Finite-size correlation behavior near a critical point: a simple metric for monitoring the state of a neural network; American Physical Society; Physical Review E: Statistical, Nonlinear and Soft Matter Physics; 106; 054313; 5-2022; 1-5
2331-8422
CONICET Digital
CONICET
url http://hdl.handle.net/11336/188025
identifier_str_mv Aguilar Trejo, Eyisto José; Mártin, Daniel Alejandro; Grigera, Tomas Sebastian; Cannas, Sergio Alejandro; Chialvo, Dante Renato; Finite-size correlation behavior near a critical point: a simple metric for monitoring the state of a neural network; American Physical Society; Physical Review E: Statistical, Nonlinear and Soft Matter Physics; 106; 054313; 5-2022; 1-5
2331-8422
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://doi.org/10.48550/arxiv.2205.11341
info:eu-repo/semantics/altIdentifier/doi/10.48550/ARXIV.2205.11341
info:eu-repo/semantics/altIdentifier/url/https://journals.aps.org/pre/abstract/10.1103/PhysRevE.106.054313
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
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_ 1846083495561003008
score 13.22299

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