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The Dynamics of Balanced Spiking Neuronal Networks Under Poisson Drive Is Not Chaotic

Some previous studies have shown that chaotic dynamics in the balanced state, i.e., one with balanced excitatory and inhibitory inputs into cortical neurons, is the underlying mechanism for the irregularity of neural activity. In this work, we focus on networks of current-based integrate-and-fire ne...

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Autores principales: Gu, Qing-long L., Tian, Zhong-qi K., Kovačič, Gregor, Zhou, Douglas, Cai, David
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6036256/
https://www.ncbi.nlm.nih.gov/pubmed/30013471
http://dx.doi.org/10.3389/fncom.2018.00047
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author Gu, Qing-long L.
Tian, Zhong-qi K.
Kovačič, Gregor
Zhou, Douglas
Cai, David
author_facet Gu, Qing-long L.
Tian, Zhong-qi K.
Kovačič, Gregor
Zhou, Douglas
Cai, David
author_sort Gu, Qing-long L.
collection PubMed
description Some previous studies have shown that chaotic dynamics in the balanced state, i.e., one with balanced excitatory and inhibitory inputs into cortical neurons, is the underlying mechanism for the irregularity of neural activity. In this work, we focus on networks of current-based integrate-and-fire neurons with delta-pulse coupling. While we show that the balanced state robustly persists in this system within a broad range of parameters, we mathematically prove that the largest Lyapunov exponent of this type of neuronal networks is negative. Therefore, the irregular firing activity can exist in the system without the chaotic dynamics. That is the irregularity of balanced neuronal networks need not arise from chaos.
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spelling pubmed-60362562018-07-16 The Dynamics of Balanced Spiking Neuronal Networks Under Poisson Drive Is Not Chaotic Gu, Qing-long L. Tian, Zhong-qi K. Kovačič, Gregor Zhou, Douglas Cai, David Front Comput Neurosci Neuroscience Some previous studies have shown that chaotic dynamics in the balanced state, i.e., one with balanced excitatory and inhibitory inputs into cortical neurons, is the underlying mechanism for the irregularity of neural activity. In this work, we focus on networks of current-based integrate-and-fire neurons with delta-pulse coupling. While we show that the balanced state robustly persists in this system within a broad range of parameters, we mathematically prove that the largest Lyapunov exponent of this type of neuronal networks is negative. Therefore, the irregular firing activity can exist in the system without the chaotic dynamics. That is the irregularity of balanced neuronal networks need not arise from chaos. Frontiers Media S.A. 2018-06-28 /pmc/articles/PMC6036256/ /pubmed/30013471 http://dx.doi.org/10.3389/fncom.2018.00047 Text en Copyright © 2018 Gu, Tian, Kovačič, Zhou and Cai. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Gu, Qing-long L.
Tian, Zhong-qi K.
Kovačič, Gregor
Zhou, Douglas
Cai, David
The Dynamics of Balanced Spiking Neuronal Networks Under Poisson Drive Is Not Chaotic
title The Dynamics of Balanced Spiking Neuronal Networks Under Poisson Drive Is Not Chaotic
title_full The Dynamics of Balanced Spiking Neuronal Networks Under Poisson Drive Is Not Chaotic
title_fullStr The Dynamics of Balanced Spiking Neuronal Networks Under Poisson Drive Is Not Chaotic
title_full_unstemmed The Dynamics of Balanced Spiking Neuronal Networks Under Poisson Drive Is Not Chaotic
title_short The Dynamics of Balanced Spiking Neuronal Networks Under Poisson Drive Is Not Chaotic
title_sort dynamics of balanced spiking neuronal networks under poisson drive is not chaotic
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6036256/
https://www.ncbi.nlm.nih.gov/pubmed/30013471
http://dx.doi.org/10.3389/fncom.2018.00047
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