@Article{ProtacheviczBLJIKCSBMABK:2019:BiFiPa,
               author = "Protachevicz, Paulo R. and Borges, Fernando S. and Lameu, Ewandson 
                         Luiz and Ji, Peng and Iarosz, Kelly c. and Kihara, Alexandre H. 
                         and Caldas, Ibere L. and Szezech Junior, Jos{\'e} D. and 
                         Baptista, Murilo S. and Macau, Elbert Einstein Nehrer and 
                         Antonopoulos, Chris G. and Batista, Antonio M. and Kuths, Jurgen",
          affiliation = "{Universidade Estadual de Ponta Grossa} and {Universidade Federal 
                         do ABC (UFABC)} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)} and {Fudan University} and {Universidade de S{\~a}o Paulo 
                         (USP)} and {Universidade Federal do ABC (UFABC)} and {Universidade 
                         de S{\~a}o Paulo (USP)} and {Universidade Estadual de Ponta 
                         Grossa} and {University of Aberdeen} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {University of Essex} and 
                         {Universidade Estadual de Ponta Grossa} and {Potsdam Institute for 
                         Climate Impact Research}",
                title = "Bistable firing pattern in a neural network model",
              journal = "Frontiers in Computational Neuroscience",
                 year = "2019",
               volume = "13",
                month = "Feb.",
             keywords = "bistable regime, network, adaptive exponential integrate-and-fire 
                         neural model, neural dynamics, synchronization, epilepsy.",
             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.",
                  doi = "10.3389/fncom.2019.00019",
                  url = "http://dx.doi.org/10.3389/fncom.2019.00019",
                 issn = "1662-5188",
             language = "en",
           targetfile = "fncom-13-00019.pdf",
        urlaccessdate = "27 abr. 2024"
}