Abstract
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.
| Original language | English |
|---|---|
| Article number | 19 |
| Number of pages | 8 |
| Journal | Frontiers in Computational Neuroscience |
| Volume | 13 |
| DOIs | |
| Publication status | Published - 5 Apr 2019 |
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Keywords
- Adaptive exponential integrate-and-fire neural model
- Bistable regime
- Epilepsy
- Network
- Neural dynamics
- Synchronization
ASJC Scopus subject areas
- Neuroscience (miscellaneous)
- Cellular and Molecular Neuroscience
Cite this
Bistable firing pattern in a neural network model. / Protachevicz, Paulo R.; Borges, Fernando S.; Lameu, Ewandson L.; Ji, Peng; Iarosz, Kelly C.; Kihara, Alexandre H.; Caldas, Ibere L.; Szezech, Jose D.; Baptista, Murilo S.; Macau, Elbert E.N.; Antonopoulos, Chris G.; Batista, Antonio M.; Kurths, Jürgen.
In: Frontiers in Computational Neuroscience, Vol. 13, 19, 05.04.2019.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Bistable firing pattern in a neural network model
AU - Protachevicz, Paulo R.
AU - Borges, Fernando S.
AU - Lameu, Ewandson L.
AU - Ji, Peng
AU - Iarosz, Kelly C.
AU - Kihara, Alexandre H.
AU - Caldas, Ibere L.
AU - Szezech, Jose D.
AU - Baptista, Murilo S.
AU - Macau, Elbert E.N.
AU - Antonopoulos, Chris G.
AU - Batista, Antonio M.
AU - Kurths, Jürgen
N1 - FUNDING This study was possible by partial financial support from the following Brazilian government agencies: Fundação Araucária, CNPq (433782/2016-1, 310124/2017-4, and 428388/2018-3), CAPES, and FAPESP (2015/50122-0, 2015/07311-7, 2016/16148-5, 2016/23398-8, 2017/13502-5, 2017/18977-1, 2018/03211-6). ACKNOWLEDGMENTS We also wish to thank the Newton Fund, COFAP, and International Visiting Fellowships Scheme of the University of Essex. We also thank IRTG 1740 for support.
PY - 2019/4/5
Y1 - 2019/4/5
N2 - 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.
AB - 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.
KW - Adaptive exponential integrate-and-fire neural model
KW - Bistable regime
KW - Epilepsy
KW - Network
KW - Neural dynamics
KW - Synchronization
UR - http://www.scopus.com/inward/record.url?scp=85064220263&partnerID=8YFLogxK
UR - https://www.frontiersin.org/article/10.3389/fncom.2019.00019/full
UR - http://www.mendeley.com/research/bistable-firing-pattern-neural-network-model
U2 - 10.3389/fncom.2019.00019
DO - 10.3389/fncom.2019.00019
M3 - Article
AN - SCOPUS:85064220263
VL - 13
JO - Frontiers in Computational Neuroscience
JF - Frontiers in Computational Neuroscience
SN - 1662-5188
M1 - 19
ER -