AUTHOR=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 TITLE=Bistable Firing Pattern in a Neural Network Model JOURNAL=Frontiers in Computational Neuroscience VOLUME=13 YEAR=2019 URL=https://www.frontiersin.org/journals/computational-neuroscience/articles/10.3389/fncom.2019.00019 DOI=10.3389/fncom.2019.00019 ISSN=1662-5188 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.