author = "Borges, Fernando da Silva and Protachevicz, P. R. and Pena, R . F. 
                         O. and Lameu, Ewandson Luiz and Higa, G. S. V. and Kihara, A. H. 
                         and Matias, F. S. and Antonopoulos, C. G. and Pasquale, R. de and 
                         Roque, A. C. and Iarosz, K. C. and Ji, P. and Batista, A. M.",
          affiliation = "{Universidade Federal do ABC (UFABC)} and {Universidade Federal de 
                         Ponta Grossa (UFPG)} and {Universidade de S{\~a}o Paulo (USP)} 
                         and {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Universidade Federal do ABC (UFABC)} and {Universidade Federal do 
                         ABC (UFABC)} and {Universidade Federal de Alagoas (UFAL)} and 
                         {University of Essex} 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 {Fudan University} and {Universidade 
                         Federal de Ponta Grossa (UFPG)}",
                title = "Self-sustained activity of low firing rate in balanced networks",
              journal = "Physica A",
                 year = "2020",
               volume = "537",
                pages = "e122671",
                month = "Jan.",
             keywords = "Spontaneous activity, Neural networks, Whole-cell recordings, 
                         Asynchronous irregular activity.",
             abstract = "Self-sustained activity in the brain is observed in the absence of 
                         external stimuli and contributes to signal propagation, neural 
                         coding, and dynamic stability. It also plays an important role in 
                         cognitive processes. In this work, by means of studying 
                         intracellular recordings from CA1 neurons in rats and results from 
                         numerical simulations, we demonstrate that self-sustained activity 
                         presents high variability of patterns, such as low neural firing 
                         rates and activity in the form of small-bursts in distinct 
                         neurons. In our numerical simulations, we consider random networks 
                         composed of coupled, adaptive exponential integrate-and-fire 
                         neurons. The neural dynamics in the random networks simulates 
                         regular spiking (excitatory) and fast spiking (inhibitory) 
                         neurons. We show that both the connection probability and network 
                         size are fundamental properties that give rise to self-sustained 
                         activity in qualitative agreement with our experimental results. 
                         Finally, we provide a more detailed description of self-sustained 
                         activity in terms of lifetime distributions, synaptic 
                         conductances, and synaptic currents.",
                  doi = "10.1016/j.physa.2019.122671",
                  url = "http://dx.doi.org/10.1016/j.physa.2019.122671",
                 issn = "0378-4371",
             language = "en",
           targetfile = "borges_self.pdf",
        urlaccessdate = "04 jul. 2020"