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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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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