Bistable firing pattern in a neural network model

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*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)
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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 languageEnglish
Article number19
Number of pages8
JournalFrontiers in Computational Neuroscience
Publication statusPublished - 5 Apr 2019


  • Adaptive exponential integrate-and-fire neural model
  • Bistable regime
  • Epilepsy
  • Network
  • Neural dynamics
  • Synchronization


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