A nonequilibrium-potential approach to competition in neural populations

Autores
Deza, Roberto Raul; Deza, Juan Ignacio; Martinez, Nataniel; Mejías, Jorge F.; Wio, Horacio S.
Año de publicación
2019
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Energy landscapes are a highly useful aid for the understanding of dynamical systems, and a particularly valuable tool for their analysis. For a broad class of rate neural-network models of relevance in neuroscience, we derive a global Lyapunov function which provides an energy landscape without any symmetry constraint. This newly obtained "nonequilibrium potential" (NEP)-the first one obtained for a model of neural circuits-predicts with high accuracy the outcomes of the dynamics in the globally stable cases studied here. Common features of the models in this class are bistability-with implications for working memory and slow neural oscillations-and population bursts, associated with signal detection in neuroscience. Instead, limit cycles are not found for the conditions in which the NEP is defined. Their nonexistence can be proven by resorting to the Bendixson-Dulac theorem, at least when the NEP remains positive and in the (also generic) singular limit of these models. This NEP constitutes a powerful tool to understand average neural network dynamics from a more formal standpoint, and will also be of help in the description of large heterogeneous neural networks.
Fil: Deza, Roberto Raul. Universidad Nacional de Mar del Plata; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Físicas de Mar del Plata. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Físicas de Mar del Plata; Argentina
Fil: Deza, Juan Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Físicas de Mar del Plata. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Físicas de Mar del Plata; Argentina. Universidad Atlantida Argentina; Argentina
Fil: Martinez, Nataniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Físicas de Mar del Plata. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Físicas de Mar del Plata; Argentina. Universidad Nacional de Mar del Plata; Argentina
Fil: Mejías, Jorge F.. Swammerdam Institute For Life Sciences; Países Bajos
Fil: Wio, Horacio S.. Universitat de Les Illes Balears; España
Materia
BISTABILITY
ENERGY LANDSCAPE
FIRING RATE DYNAMICS
NEURAL NETWORKS
NONEQUILIBRIUM POTENTIAL
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:11336/120231
Acceder
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network_name_str CONICET Digital (CONICET)
spelling A nonequilibrium-potential approach to competition in neural populationsDeza, Roberto RaulDeza, Juan IgnacioMartinez, NatanielMejías, Jorge F.Wio, Horacio S.BISTABILITYENERGY LANDSCAPEFIRING RATE DYNAMICSNEURAL NETWORKSNONEQUILIBRIUM POTENTIALhttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1Energy landscapes are a highly useful aid for the understanding of dynamical systems, and a particularly valuable tool for their analysis. For a broad class of rate neural-network models of relevance in neuroscience, we derive a global Lyapunov function which provides an energy landscape without any symmetry constraint. This newly obtained "nonequilibrium potential" (NEP)-the first one obtained for a model of neural circuits-predicts with high accuracy the outcomes of the dynamics in the globally stable cases studied here. Common features of the models in this class are bistability-with implications for working memory and slow neural oscillations-and population bursts, associated with signal detection in neuroscience. Instead, limit cycles are not found for the conditions in which the NEP is defined. Their nonexistence can be proven by resorting to the Bendixson-Dulac theorem, at least when the NEP remains positive and in the (also generic) singular limit of these models. This NEP constitutes a powerful tool to understand average neural network dynamics from a more formal standpoint, and will also be of help in the description of large heterogeneous neural networks.Fil: Deza, Roberto Raul. Universidad Nacional de Mar del Plata; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Físicas de Mar del Plata. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Físicas de Mar del Plata; ArgentinaFil: Deza, Juan Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Físicas de Mar del Plata. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Físicas de Mar del Plata; Argentina. Universidad Atlantida Argentina; ArgentinaFil: Martinez, Nataniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Físicas de Mar del Plata. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Físicas de Mar del Plata; Argentina. Universidad Nacional de Mar del Plata; ArgentinaFil: Mejías, Jorge F.. Swammerdam Institute For Life Sciences; Países BajosFil: Wio, Horacio S.. Universitat de Les Illes Balears; EspañaFrontiers Media S.A.2019-01info: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/11336/120231Deza, Roberto Raul; Deza, Juan Ignacio; Martinez, Nataniel; Mejías, Jorge F.; Wio, Horacio S.; A nonequilibrium-potential approach to competition in neural populations; Frontiers Media S.A.; Frontiers in Physics; 6; JAN; 1-20192296-424XCONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.frontiersin.org/articles/10.3389/fphy.2018.00154/fullinfo:eu-repo/semantics/altIdentifier/doi/10.3389/fphy.2018.00154info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-17T11:30:29Zoai:ri.conicet.gov.ar:11336/120231instacron: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:34982025-09-17 11:30:29.665CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A nonequilibrium-potential approach to competition in neural populations
title A nonequilibrium-potential approach to competition in neural populations
spellingShingle A nonequilibrium-potential approach to competition in neural populations
Deza, Roberto Raul
BISTABILITY
ENERGY LANDSCAPE
FIRING RATE DYNAMICS
NEURAL NETWORKS
NONEQUILIBRIUM POTENTIAL
title_short A nonequilibrium-potential approach to competition in neural populations
title_full A nonequilibrium-potential approach to competition in neural populations
title_fullStr A nonequilibrium-potential approach to competition in neural populations
title_full_unstemmed A nonequilibrium-potential approach to competition in neural populations
title_sort A nonequilibrium-potential approach to competition in neural populations
dc.creator.none.fl_str_mv Deza, Roberto Raul
Deza, Juan Ignacio
Martinez, Nataniel
Mejías, Jorge F.
Wio, Horacio S.
author Deza, Roberto Raul
author_facet Deza, Roberto Raul
Deza, Juan Ignacio
Martinez, Nataniel
Mejías, Jorge F.
Wio, Horacio S.
author_role author
author2 Deza, Juan Ignacio
Martinez, Nataniel
Mejías, Jorge F.
Wio, Horacio S.
author2_role author
author
author
author
dc.subject.none.fl_str_mv BISTABILITY
ENERGY LANDSCAPE
FIRING RATE DYNAMICS
NEURAL NETWORKS
NONEQUILIBRIUM POTENTIAL
topic BISTABILITY
ENERGY LANDSCAPE
FIRING RATE DYNAMICS
NEURAL NETWORKS
NONEQUILIBRIUM POTENTIAL
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Energy landscapes are a highly useful aid for the understanding of dynamical systems, and a particularly valuable tool for their analysis. For a broad class of rate neural-network models of relevance in neuroscience, we derive a global Lyapunov function which provides an energy landscape without any symmetry constraint. This newly obtained "nonequilibrium potential" (NEP)-the first one obtained for a model of neural circuits-predicts with high accuracy the outcomes of the dynamics in the globally stable cases studied here. Common features of the models in this class are bistability-with implications for working memory and slow neural oscillations-and population bursts, associated with signal detection in neuroscience. Instead, limit cycles are not found for the conditions in which the NEP is defined. Their nonexistence can be proven by resorting to the Bendixson-Dulac theorem, at least when the NEP remains positive and in the (also generic) singular limit of these models. This NEP constitutes a powerful tool to understand average neural network dynamics from a more formal standpoint, and will also be of help in the description of large heterogeneous neural networks.
Fil: Deza, Roberto Raul. Universidad Nacional de Mar del Plata; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Físicas de Mar del Plata. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Físicas de Mar del Plata; Argentina
Fil: Deza, Juan Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Físicas de Mar del Plata. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Físicas de Mar del Plata; Argentina. Universidad Atlantida Argentina; Argentina
Fil: Martinez, Nataniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Físicas de Mar del Plata. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Físicas de Mar del Plata; Argentina. Universidad Nacional de Mar del Plata; Argentina
Fil: Mejías, Jorge F.. Swammerdam Institute For Life Sciences; Países Bajos
Fil: Wio, Horacio S.. Universitat de Les Illes Balears; España
description Energy landscapes are a highly useful aid for the understanding of dynamical systems, and a particularly valuable tool for their analysis. For a broad class of rate neural-network models of relevance in neuroscience, we derive a global Lyapunov function which provides an energy landscape without any symmetry constraint. This newly obtained "nonequilibrium potential" (NEP)-the first one obtained for a model of neural circuits-predicts with high accuracy the outcomes of the dynamics in the globally stable cases studied here. Common features of the models in this class are bistability-with implications for working memory and slow neural oscillations-and population bursts, associated with signal detection in neuroscience. Instead, limit cycles are not found for the conditions in which the NEP is defined. Their nonexistence can be proven by resorting to the Bendixson-Dulac theorem, at least when the NEP remains positive and in the (also generic) singular limit of these models. This NEP constitutes a powerful tool to understand average neural network dynamics from a more formal standpoint, and will also be of help in the description of large heterogeneous neural networks.
publishDate 2019
dc.date.none.fl_str_mv 2019-01
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/11336/120231
Deza, Roberto Raul; Deza, Juan Ignacio; Martinez, Nataniel; Mejías, Jorge F.; Wio, Horacio S.; A nonequilibrium-potential approach to competition in neural populations; Frontiers Media S.A.; Frontiers in Physics; 6; JAN; 1-2019
2296-424X
CONICET Digital
CONICET
url http://hdl.handle.net/11336/120231
identifier_str_mv Deza, Roberto Raul; Deza, Juan Ignacio; Martinez, Nataniel; Mejías, Jorge F.; Wio, Horacio S.; A nonequilibrium-potential approach to competition in neural populations; Frontiers Media S.A.; Frontiers in Physics; 6; JAN; 1-2019
2296-424X
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://www.frontiersin.org/articles/10.3389/fphy.2018.00154/full
info:eu-repo/semantics/altIdentifier/doi/10.3389/fphy.2018.00154
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 Frontiers Media S.A.
publisher.none.fl_str_mv Frontiers Media S.A.
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 13.001348

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