Bistable Firing Pattern in a Neural Network Model

Excessively high, neural synchronization has been associated with epileptic seizures, one of the most common brain diseases worldwide. A better understanding of neural synchronization mechanisms can thus help control or even treat epilepsy. In this paper, we study neural synchronization in a random...

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Main Authors: Paulo R. Protachevicz, Fernando S. Borges, Ewandson L. Lameu, Peng Ji, Kelly C. Iarosz, Alexandre H. Kihara, Ibere L. Caldas, Jose D. Szezech, Murilo S. Baptista, Elbert E. N. Macau, Chris G. Antonopoulos, Antonio M. Batista, Jürgen Kurths
Format: Article
Language:English
Published: Frontiers Media S.A. 2019-04-01
Series:Frontiers in Computational Neuroscience
Subjects:
Online Access:https://www.frontiersin.org/article/10.3389/fncom.2019.00019/full
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spelling doaj-7010d9f3661d4dfa89a2810497e400df2020-11-25T00:02:25ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882019-04-011310.3389/fncom.2019.00019453447Bistable Firing Pattern in a Neural Network ModelPaulo R. Protachevicz0Fernando S. Borges1Ewandson L. Lameu2Peng Ji3Peng Ji4Kelly C. Iarosz5Alexandre H. Kihara6Ibere L. Caldas7Jose D. Szezech8Jose D. Szezech9Murilo S. Baptista10Elbert E. N. Macau11Chris G. Antonopoulos12Antonio M. Batista13Antonio M. Batista14Jürgen Kurths15Jürgen Kurths16Graduate in Science Program—Physics, State University of Ponta Grossa, Ponta Grossa, BrazilCenter for Mathematics, Computation, and Cognition, Federal University of ABC, São Bernardo do Campo, BrazilNational Institute for Space Research, São José dos Campos, BrazilInstitute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, ChinaKey Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, ChinaInstitute of Physics, University of São Paulo, São Paulo, BrazilCenter for Mathematics, Computation, and Cognition, Federal University of ABC, São Bernardo do Campo, BrazilInstitute of Physics, University of São Paulo, São Paulo, BrazilGraduate in Science Program—Physics, State University of Ponta Grossa, Ponta Grossa, BrazilDepartment of Mathematics and Statistics, State University of Ponta Grossa, Ponta Grossa, BrazilInstitute for Complex Systems and Mathematical Biology, SUPA, University of Aberdeen, Aberdeen, United KingdomNational Institute for Space Research, São José dos Campos, BrazilDepartment of Mathematical Sciences, University of Essex, Colchester, United KingdomGraduate in Science Program—Physics, State University of Ponta Grossa, Ponta Grossa, BrazilDepartment of Mathematics and Statistics, State University of Ponta Grossa, Ponta Grossa, Brazil0Potsdam Institute for Climate Impact Research, Potsdam, Germany1Department of Physics, Humboldt University, Berlin, GermanyExcessively high, neural synchronization has been associated with epileptic seizures, one of the most common brain diseases worldwide. A better understanding of neural synchronization mechanisms can thus help control or even treat epilepsy. In this paper, we study neural synchronization in a random network where nodes are neurons with excitatory and inhibitory synapses, and neural activity for each node is provided by the adaptive exponential integrate-and-fire model. In this framework, we verify that the decrease in the influence of inhibition can generate synchronization originating from a pattern of desynchronized spikes. The transition from desynchronous spikes to synchronous bursts of activity, induced by varying the synaptic coupling, emerges in a hysteresis loop due to bistability where abnormal (excessively high synchronous) regimes exist. We verify that, for parameters in the bistability regime, a square current pulse can trigger excessively high (abnormal) synchronization, a process that can reproduce features of epileptic seizures. Then, we show that it is possible to suppress such abnormal synchronization by applying a small-amplitude external current on > 10% of the neurons in the network. Our results demonstrate that external electrical stimulation not only can trigger synchronous behavior, but more importantly, it can be used as a means to reduce abnormal synchronization and thus, control or treat effectively epileptic seizures.https://www.frontiersin.org/article/10.3389/fncom.2019.00019/fullbistable regimenetworkadaptive exponential integrate-and-fire neural modelneural dynamicssynchronizationepilepsy
collection DOAJ
language English
format Article
sources DOAJ
author Paulo R. Protachevicz
Fernando S. Borges
Ewandson L. Lameu
Peng Ji
Peng Ji
Kelly C. Iarosz
Alexandre H. Kihara
Ibere L. Caldas
Jose D. Szezech
Jose D. Szezech
Murilo S. Baptista
Elbert E. N. Macau
Chris G. Antonopoulos
Antonio M. Batista
Antonio M. Batista
Jürgen Kurths
Jürgen Kurths
spellingShingle Paulo R. Protachevicz
Fernando S. Borges
Ewandson L. Lameu
Peng Ji
Peng Ji
Kelly C. Iarosz
Alexandre H. Kihara
Ibere L. Caldas
Jose D. Szezech
Jose D. Szezech
Murilo S. Baptista
Elbert E. N. Macau
Chris G. Antonopoulos
Antonio M. Batista
Antonio M. Batista
Jürgen Kurths
Jürgen Kurths
Bistable Firing Pattern in a Neural Network Model
Frontiers in Computational Neuroscience
bistable regime
network
adaptive exponential integrate-and-fire neural model
neural dynamics
synchronization
epilepsy
author_facet Paulo R. Protachevicz
Fernando S. Borges
Ewandson L. Lameu
Peng Ji
Peng Ji
Kelly C. Iarosz
Alexandre H. Kihara
Ibere L. Caldas
Jose D. Szezech
Jose D. Szezech
Murilo S. Baptista
Elbert E. N. Macau
Chris G. Antonopoulos
Antonio M. Batista
Antonio M. Batista
Jürgen Kurths
Jürgen Kurths
author_sort Paulo R. Protachevicz
title Bistable Firing Pattern in a Neural Network Model
title_short Bistable Firing Pattern in a Neural Network Model
title_full Bistable Firing Pattern in a Neural Network Model
title_fullStr Bistable Firing Pattern in a Neural Network Model
title_full_unstemmed Bistable Firing Pattern in a Neural Network Model
title_sort bistable firing pattern in a neural network model
publisher Frontiers Media S.A.
series Frontiers in Computational Neuroscience
issn 1662-5188
publishDate 2019-04-01
description Excessively high, neural synchronization has been associated with epileptic seizures, one of the most common brain diseases worldwide. A better understanding of neural synchronization mechanisms can thus help control or even treat epilepsy. In this paper, we study neural synchronization in a random network where nodes are neurons with excitatory and inhibitory synapses, and neural activity for each node is provided by the adaptive exponential integrate-and-fire model. In this framework, we verify that the decrease in the influence of inhibition can generate synchronization originating from a pattern of desynchronized spikes. The transition from desynchronous spikes to synchronous bursts of activity, induced by varying the synaptic coupling, emerges in a hysteresis loop due to bistability where abnormal (excessively high synchronous) regimes exist. We verify that, for parameters in the bistability regime, a square current pulse can trigger excessively high (abnormal) synchronization, a process that can reproduce features of epileptic seizures. Then, we show that it is possible to suppress such abnormal synchronization by applying a small-amplitude external current on > 10% of the neurons in the network. Our results demonstrate that external electrical stimulation not only can trigger synchronous behavior, but more importantly, it can be used as a means to reduce abnormal synchronization and thus, control or treat effectively epileptic seizures.
topic bistable regime
network
adaptive exponential integrate-and-fire neural model
neural dynamics
synchronization
epilepsy
url https://www.frontiersin.org/article/10.3389/fncom.2019.00019/full
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