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    Bistable firing pattern in a neural network model
    (Lausanne : Frontiers Media, 2019) Protachevicz, Paulo R.; Borges, Fernando S.; Lameu, Ewandson L.; Ji, Peng; Iarosz, Kelly C.; Kihara, Alexandre H.; Caldas, Ibere L.; Szezech Jr., Jose D.; Baptista, Murilo S.; Macau, Elbert E.N.; Antonopoulos, Chris G.; Batista, Antonio M.; Kurths, Jürgen
    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. © 2019 Protachevicz, Borges, Lameu, Ji, Iarosz, Kihara, Caldas, Szezech, Baptista, Macau, Antonopoulos, Batista and Kurths.