Self-tuned criticality: Controlling a neuron near its bifurcation point via temporal correlations

Autores
Moraes, Juliane T.; Aguilar Trejo, Eyisto José; Camargo, Sabrina; Ferreira, Silvio C.; Chialvo, Dante Renato
Año de publicación
2023
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Previous work showed that the collective activity of large neuronal networks can be tamed to remain near its critical point by a feedback control that maximizes the temporal correlations of the mean-field fluctuations. Since such correlations behave similarly near instabilities across nonlinear dynamical systems, it is expected that the principle should control also low-dimensional dynamical systems exhibiting continuous or discontinuous bifurcations from fixed points to limit cycles. Here we present numerical evidence that the dynamics of a single neuron can be controlled in the vicinity of its bifurcation point. The approach is tested in two models: a two-dimensional generic excitable map and the paradigmatic FitzHugh-Nagumo neuron model. The results show that in both cases, the system can be self-tuned to its bifurcation point by modifying the control parameter according to the first coefficient of the autocorrelation function.
Fil: Moraes, Juliane T.. Universidade Federal de Viçosa.; Brasil
Fil: Aguilar Trejo, Eyisto José. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Ciencias Físicas. - Universidad Nacional de San Martín. Instituto de Ciencias Físicas; Argentina. Universidad Nacional de San Martin. Escuela de Ciencia y Tecnologia. Centro de Estudios Multidisciplinarios En Sistemas Complejos y Ciencias del Cerebro.; Argentina
Fil: Camargo, Sabrina. 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. Instituto de Ciencias Físicas. - Universidad Nacional de San Martín. Instituto de Ciencias Físicas; Argentina
Fil: Ferreira, Silvio C.. Universidade Federal de Viçosa.; Brasil
Fil: Chialvo, Dante Renato. Jagiellonian University; . Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Ciencias Físicas. - Universidad Nacional de San Martín. Instituto de Ciencias Físicas; Argentina
Materia
Complex systems
Complex networks
Self-tuned criticality
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:11336/220782
Acceder
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spelling Self-tuned criticality: Controlling a neuron near its bifurcation point via temporal correlationsMoraes, Juliane T.Aguilar Trejo, Eyisto JoséCamargo, SabrinaFerreira, Silvio C.Chialvo, Dante RenatoComplex systemsComplex networksSelf-tuned criticalityhttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1Previous work showed that the collective activity of large neuronal networks can be tamed to remain near its critical point by a feedback control that maximizes the temporal correlations of the mean-field fluctuations. Since such correlations behave similarly near instabilities across nonlinear dynamical systems, it is expected that the principle should control also low-dimensional dynamical systems exhibiting continuous or discontinuous bifurcations from fixed points to limit cycles. Here we present numerical evidence that the dynamics of a single neuron can be controlled in the vicinity of its bifurcation point. The approach is tested in two models: a two-dimensional generic excitable map and the paradigmatic FitzHugh-Nagumo neuron model. The results show that in both cases, the system can be self-tuned to its bifurcation point by modifying the control parameter according to the first coefficient of the autocorrelation function.Fil: Moraes, Juliane T.. Universidade Federal de Viçosa.; BrasilFil: Aguilar Trejo, Eyisto José. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Ciencias Físicas. - Universidad Nacional de San Martín. Instituto de Ciencias Físicas; Argentina. Universidad Nacional de San Martin. Escuela de Ciencia y Tecnologia. Centro de Estudios Multidisciplinarios En Sistemas Complejos y Ciencias del Cerebro.; ArgentinaFil: Camargo, Sabrina. 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. Instituto de Ciencias Físicas. - Universidad Nacional de San Martín. Instituto de Ciencias Físicas; ArgentinaFil: Ferreira, Silvio C.. Universidade Federal de Viçosa.; BrasilFil: Chialvo, Dante Renato. Jagiellonian University; . Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Ciencias Físicas. - Universidad Nacional de San Martín. Instituto de Ciencias Físicas; ArgentinaAmerican Physical Society2023-03info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/220782Moraes, Juliane T.; Aguilar Trejo, Eyisto José; Camargo, Sabrina; Ferreira, Silvio C.; Chialvo, Dante Renato; Self-tuned criticality: Controlling a neuron near its bifurcation point via temporal correlations; American Physical Society; Physical Review E: Statistical, Nonlinear and Soft Matter Physics; 107; 3; 3-2023; 1-61539-37552470-0053CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://journals.aps.org/pre/abstract/10.1103/PhysRevE.107.034204info:eu-repo/semantics/altIdentifier/doi/10.1103/PhysRevE.107.034204info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T14:55:45Zoai:ri.conicet.gov.ar:11336/220782instacron: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 14:55:46.196CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Self-tuned criticality: Controlling a neuron near its bifurcation point via temporal correlations
title Self-tuned criticality: Controlling a neuron near its bifurcation point via temporal correlations
spellingShingle Self-tuned criticality: Controlling a neuron near its bifurcation point via temporal correlations
Moraes, Juliane T.
Complex systems
Complex networks
Self-tuned criticality
title_short Self-tuned criticality: Controlling a neuron near its bifurcation point via temporal correlations
title_full Self-tuned criticality: Controlling a neuron near its bifurcation point via temporal correlations
title_fullStr Self-tuned criticality: Controlling a neuron near its bifurcation point via temporal correlations
title_full_unstemmed Self-tuned criticality: Controlling a neuron near its bifurcation point via temporal correlations
title_sort Self-tuned criticality: Controlling a neuron near its bifurcation point via temporal correlations
dc.creator.none.fl_str_mv Moraes, Juliane T.
Aguilar Trejo, Eyisto José
Camargo, Sabrina
Ferreira, Silvio C.
Chialvo, Dante Renato
author Moraes, Juliane T.
author_facet Moraes, Juliane T.
Aguilar Trejo, Eyisto José
Camargo, Sabrina
Ferreira, Silvio C.
Chialvo, Dante Renato
author_role author
author2 Aguilar Trejo, Eyisto José
Camargo, Sabrina
Ferreira, Silvio C.
Chialvo, Dante Renato
author2_role author
author
author
author
dc.subject.none.fl_str_mv Complex systems
Complex networks
Self-tuned criticality
topic Complex systems
Complex networks
Self-tuned criticality
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Previous work showed that the collective activity of large neuronal networks can be tamed to remain near its critical point by a feedback control that maximizes the temporal correlations of the mean-field fluctuations. Since such correlations behave similarly near instabilities across nonlinear dynamical systems, it is expected that the principle should control also low-dimensional dynamical systems exhibiting continuous or discontinuous bifurcations from fixed points to limit cycles. Here we present numerical evidence that the dynamics of a single neuron can be controlled in the vicinity of its bifurcation point. The approach is tested in two models: a two-dimensional generic excitable map and the paradigmatic FitzHugh-Nagumo neuron model. The results show that in both cases, the system can be self-tuned to its bifurcation point by modifying the control parameter according to the first coefficient of the autocorrelation function.
Fil: Moraes, Juliane T.. Universidade Federal de Viçosa.; Brasil
Fil: Aguilar Trejo, Eyisto José. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Ciencias Físicas. - Universidad Nacional de San Martín. Instituto de Ciencias Físicas; Argentina. Universidad Nacional de San Martin. Escuela de Ciencia y Tecnologia. Centro de Estudios Multidisciplinarios En Sistemas Complejos y Ciencias del Cerebro.; Argentina
Fil: Camargo, Sabrina. 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. Instituto de Ciencias Físicas. - Universidad Nacional de San Martín. Instituto de Ciencias Físicas; Argentina
Fil: Ferreira, Silvio C.. Universidade Federal de Viçosa.; Brasil
Fil: Chialvo, Dante Renato. Jagiellonian University; . Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Ciencias Físicas. - Universidad Nacional de San Martín. Instituto de Ciencias Físicas; Argentina
description Previous work showed that the collective activity of large neuronal networks can be tamed to remain near its critical point by a feedback control that maximizes the temporal correlations of the mean-field fluctuations. Since such correlations behave similarly near instabilities across nonlinear dynamical systems, it is expected that the principle should control also low-dimensional dynamical systems exhibiting continuous or discontinuous bifurcations from fixed points to limit cycles. Here we present numerical evidence that the dynamics of a single neuron can be controlled in the vicinity of its bifurcation point. The approach is tested in two models: a two-dimensional generic excitable map and the paradigmatic FitzHugh-Nagumo neuron model. The results show that in both cases, the system can be self-tuned to its bifurcation point by modifying the control parameter according to the first coefficient of the autocorrelation function.
publishDate 2023
dc.date.none.fl_str_mv 2023-03
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/11336/220782
Moraes, Juliane T.; Aguilar Trejo, Eyisto José; Camargo, Sabrina; Ferreira, Silvio C.; Chialvo, Dante Renato; Self-tuned criticality: Controlling a neuron near its bifurcation point via temporal correlations; American Physical Society; Physical Review E: Statistical, Nonlinear and Soft Matter Physics; 107; 3; 3-2023; 1-6
1539-3755
2470-0053
CONICET Digital
CONICET
url http://hdl.handle.net/11336/220782
identifier_str_mv Moraes, Juliane T.; Aguilar Trejo, Eyisto José; Camargo, Sabrina; Ferreira, Silvio C.; Chialvo, Dante Renato; Self-tuned criticality: Controlling a neuron near its bifurcation point via temporal correlations; American Physical Society; Physical Review E: Statistical, Nonlinear and Soft Matter Physics; 107; 3; 3-2023; 1-6
1539-3755
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://journals.aps.org/pre/abstract/10.1103/PhysRevE.107.034204
info:eu-repo/semantics/altIdentifier/doi/10.1103/PhysRevE.107.034204
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
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
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score 13.22299

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