Causal information quantification of prominent dynamical features of biological neurons

Autores
Montani, Fernando Fabián; Baravalle, Román; Montangie, Lisandro; Rosso, Osvaldo Aníbal
Año de publicación
2015
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Neurons tend to fire a spike when they are near a bifurcation from the resting state to spiking activity. It is a delicate balance between noise, dynamic currents and initial condition that determines the phase diagram of neural activity. Many possible ionic mechanisms can be accounted for as the source of spike generation. Moreover, the biophysics and the dynamics behind it can usually be described through a phase diagram that involves membrane voltage versus the activation variable of the ionic channel. In this paper, we present a novel methodology to characterize the dynamics of this system, which takes into account the fine temporal ‘structures’ of the complex neuronal signals. This allows us to accurately distinguish the most fundamental properties of neurophysiological neurons that were previously described by Izhikevich considering the phase-space trajectory, using a time causal space: statistical complexity versus Fisher information versus Shannon entropy.
Fil: Montani, Fernando Fabián. 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: Baravalle, Román. 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: Montangie, Lisandro. 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: Rosso, Osvaldo Aníbal. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Tecnológico de Buenos Aires; Argentina. Universidade Federal de Alagoas; Brasil
Materia
Neurons
Entropy
Statistical Physics
Fisher Information Measure
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:11336/47920
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Causal information quantification of prominent dynamical features of biological neuronsMontani, Fernando FabiánBaravalle, RománMontangie, LisandroRosso, Osvaldo AníbalNeuronsEntropyStatistical PhysicsFisher Information Measurehttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1Neurons tend to fire a spike when they are near a bifurcation from the resting state to spiking activity. It is a delicate balance between noise, dynamic currents and initial condition that determines the phase diagram of neural activity. Many possible ionic mechanisms can be accounted for as the source of spike generation. Moreover, the biophysics and the dynamics behind it can usually be described through a phase diagram that involves membrane voltage versus the activation variable of the ionic channel. In this paper, we present a novel methodology to characterize the dynamics of this system, which takes into account the fine temporal ‘structures’ of the complex neuronal signals. This allows us to accurately distinguish the most fundamental properties of neurophysiological neurons that were previously described by Izhikevich considering the phase-space trajectory, using a time causal space: statistical complexity versus Fisher information versus Shannon entropy.Fil: Montani, Fernando Fabián. 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: Baravalle, Román. 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: Montangie, Lisandro. 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: Rosso, Osvaldo Aníbal. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Tecnológico de Buenos Aires; Argentina. Universidade Federal de Alagoas; BrasilThe Royal Society2015-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/47920Montani, Fernando Fabián; Baravalle, Román; Montangie, Lisandro; Rosso, Osvaldo Aníbal; Causal information quantification of prominent dynamical features of biological neurons; The Royal Society; Philosophical Transactions of the Royal Society A - Mathematical Physical and Engineering Sciences; 373; 2056; 12-2015; 1-141364-503XCONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1098/rsta.2015.0109info:eu-repo/semantics/altIdentifier/url/http://rsta.royalsocietypublishing.org/content/373/2056/20150109.longinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T11:01:41Zoai:ri.conicet.gov.ar:11336/47920instacron: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:34982025-10-22 11:01:42.031CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Causal information quantification of prominent dynamical features of biological neurons
title Causal information quantification of prominent dynamical features of biological neurons
spellingShingle Causal information quantification of prominent dynamical features of biological neurons
Montani, Fernando Fabián
Neurons
Entropy
Statistical Physics
Fisher Information Measure
title_short Causal information quantification of prominent dynamical features of biological neurons
title_full Causal information quantification of prominent dynamical features of biological neurons
title_fullStr Causal information quantification of prominent dynamical features of biological neurons
title_full_unstemmed Causal information quantification of prominent dynamical features of biological neurons
title_sort Causal information quantification of prominent dynamical features of biological neurons
dc.creator.none.fl_str_mv Montani, Fernando Fabián
Baravalle, Román
Montangie, Lisandro
Rosso, Osvaldo Aníbal
author Montani, Fernando Fabián
author_facet Montani, Fernando Fabián
Baravalle, Román
Montangie, Lisandro
Rosso, Osvaldo Aníbal
author_role author
author2 Baravalle, Román
Montangie, Lisandro
Rosso, Osvaldo Aníbal
author2_role author
author
author
dc.subject.none.fl_str_mv Neurons
Entropy
Statistical Physics
Fisher Information Measure
topic Neurons
Entropy
Statistical Physics
Fisher Information Measure
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Neurons tend to fire a spike when they are near a bifurcation from the resting state to spiking activity. It is a delicate balance between noise, dynamic currents and initial condition that determines the phase diagram of neural activity. Many possible ionic mechanisms can be accounted for as the source of spike generation. Moreover, the biophysics and the dynamics behind it can usually be described through a phase diagram that involves membrane voltage versus the activation variable of the ionic channel. In this paper, we present a novel methodology to characterize the dynamics of this system, which takes into account the fine temporal ‘structures’ of the complex neuronal signals. This allows us to accurately distinguish the most fundamental properties of neurophysiological neurons that were previously described by Izhikevich considering the phase-space trajectory, using a time causal space: statistical complexity versus Fisher information versus Shannon entropy.
Fil: Montani, Fernando Fabián. 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: Baravalle, Román. 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: Montangie, Lisandro. 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: Rosso, Osvaldo Aníbal. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Tecnológico de Buenos Aires; Argentina. Universidade Federal de Alagoas; Brasil
description Neurons tend to fire a spike when they are near a bifurcation from the resting state to spiking activity. It is a delicate balance between noise, dynamic currents and initial condition that determines the phase diagram of neural activity. Many possible ionic mechanisms can be accounted for as the source of spike generation. Moreover, the biophysics and the dynamics behind it can usually be described through a phase diagram that involves membrane voltage versus the activation variable of the ionic channel. In this paper, we present a novel methodology to characterize the dynamics of this system, which takes into account the fine temporal ‘structures’ of the complex neuronal signals. This allows us to accurately distinguish the most fundamental properties of neurophysiological neurons that were previously described by Izhikevich considering the phase-space trajectory, using a time causal space: statistical complexity versus Fisher information versus Shannon entropy.
publishDate 2015
dc.date.none.fl_str_mv 2015-12
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/11336/47920
Montani, Fernando Fabián; Baravalle, Román; Montangie, Lisandro; Rosso, Osvaldo Aníbal; Causal information quantification of prominent dynamical features of biological neurons; The Royal Society; Philosophical Transactions of the Royal Society A - Mathematical Physical and Engineering Sciences; 373; 2056; 12-2015; 1-14
1364-503X
CONICET Digital
CONICET
url http://hdl.handle.net/11336/47920
identifier_str_mv Montani, Fernando Fabián; Baravalle, Román; Montangie, Lisandro; Rosso, Osvaldo Aníbal; Causal information quantification of prominent dynamical features of biological neurons; The Royal Society; Philosophical Transactions of the Royal Society A - Mathematical Physical and Engineering Sciences; 373; 2056; 12-2015; 1-14
1364-503X
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1098/rsta.2015.0109
info:eu-repo/semantics/altIdentifier/url/http://rsta.royalsocietypublishing.org/content/373/2056/20150109.long
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 The Royal Society
publisher.none.fl_str_mv The Royal 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 12.982451

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