@Article{ProtacheviczBICBVLMB:2018:HoSyCa,
               author = "Protachevicz, P. R. and Borges, F. S. and Iarosz, K. C. and 
                         Caldas, I. L. and Baptista, M. S. and Viana, R. L. and Lameu, 
                         Ewandson Luiz and Macau, Elbert Einstein Nehrer and Batista, A. 
                         M.",
          affiliation = "{Universidade Estadual de Ponta Grossa (UEPG)} and {Universidade 
                         de S{\~a}o Paulo (USP)} and {Universidade de S{\~a}o Paulo 
                         (USP)} and {Universidade de S{\~a}o Paulo (USP)} and {Institute 
                         for Complex Systems and Mathematical Biology} and {Universidade 
                         Federal do Paran{\'a} (UFPR)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Universidade Estadual de Ponta Grossa 
                         (UEPG)}",
                title = "How synapses can enhance sensibility of a neural network",
              journal = "Physica A: Statistical Mechanics and its Applications",
                 year = "2018",
               volume = "492",
                pages = "1045--1052",
                month = "Feb.",
             keywords = "Plasticity, Cellular automaton, Dynamic range.",
             abstract = "In this work, we study the dynamic range in a neural network 
                         modelled by cellular automaton. We consider deterministic and 
                         non-deterministic rules to simulate electrical and chemical 
                         synapses. Chemical synapses have an intrinsic time-delay and are 
                         susceptible to parameter variations guided by learning Hebbian 
                         rules of behaviour. The learning rules are related to 
                         neuroplasticity that describes change to the neural connections in 
                         the brain. Our results show that chemical synapses can abruptly 
                         enhance sensibility of the neural network, a manifestation that 
                         can become even more predominant if learning rules of evolution 
                         are applied to the chemical synapses.",
                  doi = "10.1016/j.physa.2017.11.034",
                  url = "http://dx.doi.org/10.1016/j.physa.2017.11.034",
                 issn = "0378-4371",
             language = "en",
           targetfile = "protachevicz_how.pdf",
        urlaccessdate = "27 abr. 2024"
}