Causal information quantification of prominent dynamical features of biological neurons

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 so...

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Detalles Bibliográficos
Autores principales: Montani, Fernando Fabián, Baravalle, Román, Montangie, Lisandro, Rosso, Osvaldo A.
Formato: Articulo
Lenguaje:Inglés
Publicado: 2015
Materias:
Física
Entropy
Fisher information measure
Neurons
Statistical complexity
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/86925
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-86925
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Física
Entropy
Fisher information measure
Neurons
Statistical complexity
spellingShingle Física
Entropy
Fisher information measure
Neurons
Statistical complexity
Montani, Fernando Fabián
Baravalle, Román
Montangie, Lisandro
Rosso, Osvaldo A.
Causal information quantification of prominent dynamical features of biological neurons
topic_facet Física
Entropy
Fisher information measure
Neurons
Statistical complexity
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.
format Articulo
Articulo
author Montani, Fernando Fabián
Baravalle, Román
Montangie, Lisandro
Rosso, Osvaldo A.
author_facet Montani, Fernando Fabián
Baravalle, Román
Montangie, Lisandro
Rosso, Osvaldo A.
author_sort Montani, Fernando Fabián
title Causal information quantification of prominent dynamical features of biological neurons
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
publishDate 2015
url http://sedici.unlp.edu.ar/handle/10915/86925
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AT rossoosvaldoa causalinformationquantificationofprominentdynamicalfeaturesofbiologicalneurons
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