Causal information quantification of prominent dynamical features of biological neurons

Autores
Montani, Fernando Fabián; Baravalle, Román; Montangie, Lisandro; Rosso, Osvaldo A.
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.
Instituto de Física de Líquidos y Sistemas Biológicos
Materia
Física
Entropy
Fisher information measure
Neurons
Statistical complexity
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/86925

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spelling Causal information quantification of prominent dynamical features of biological neuronsMontani, Fernando FabiánBaravalle, RománMontangie, LisandroRosso, Osvaldo A.FísicaEntropyFisher information measureNeuronsStatistical complexityNeurons 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.Instituto de Física de Líquidos y Sistemas Biológicos2015info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/86925enginfo:eu-repo/semantics/altIdentifier/issn/1364-503Xinfo:eu-repo/semantics/altIdentifier/doi/10.1098/rsta.2015.0109info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:16:50Zoai:sedici.unlp.edu.ar:10915/86925Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:16:50.672SEDICI (UNLP) - Universidad Nacional de La Platafalse
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
Física
Entropy
Fisher information measure
Neurons
Statistical complexity
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 A.
author Montani, Fernando Fabián
author_facet Montani, Fernando Fabián
Baravalle, Román
Montangie, Lisandro
Rosso, Osvaldo A.
author_role author
author2 Baravalle, Román
Montangie, Lisandro
Rosso, Osvaldo A.
author2_role author
author
author
dc.subject.none.fl_str_mv Física
Entropy
Fisher information measure
Neurons
Statistical complexity
topic Física
Entropy
Fisher information measure
Neurons
Statistical complexity
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.
Instituto de Física de Líquidos y Sistemas Biológicos
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
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Articulo
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format article
status_str publishedVersion
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url http://sedici.unlp.edu.ar/handle/10915/86925
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/issn/1364-503X
info:eu-repo/semantics/altIdentifier/doi/10.1098/rsta.2015.0109
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
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instname:Universidad Nacional de La Plata
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reponame_str SEDICI (UNLP)
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instname_str Universidad Nacional de La Plata
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institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
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