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@Article{BorgesLIPCVMBB:2018:InToNa,
               author = "Borges, F. S. and Lameu, Ewandson Luiz and Iarosz, K. C. and 
                         Protachevicz, P. R. and Caldas, I. L. and Viana, R. L. and Macau, 
                         Elbert Einstein Nehrer and Batista, A. M. and Baptista, M. S.",
          affiliation = "{Universidade de S{\~a}o Paulo (USP)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Universidade de S{\~a}o Paulo 
                         (USP)} and {Universidade Estadual de Ponta Grossa (UEPG)} and 
                         {Universidade de S{\~a}o Paulo (USP)} and {Universidade Federal 
                         do Paran{\'a} (UFPR0} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Universidade de S{\~a}o Paulo (USP)} and 
                         {University of Aberdeen}",
                title = "Inference of topology and the nature of synapses, and the flow of 
                         information in neuronal networks",
              journal = "Physical Review E",
                 year = "2018",
               volume = "97",
               number = "2",
                pages = "e022303",
             abstract = "The characterization of neuronal connectivity is one of the most 
                         important matters in neuroscience. In this work, we show that a 
                         recently proposed informational quantity, the causal mutual 
                         information, employed with an appropriate methodology, can be used 
                         not only to correctly infer the direction of the underlying 
                         physical synapses, but also to identify their excitatory or 
                         inhibitory nature, considering easy to handle and measure 
                         bivariate time series. The success of our approach relies on a 
                         surprising property found in neuronal networks by which 
                         nonadjacent neurons do understand each other (positive mutual 
                         information), however, this exchange of information is not capable 
                         of causing effect (zero transfer entropy). Remarkably, inhibitory 
                         connections, responsible for enhancing synchronization, transfer 
                         more information than excitatory connections, known to enhance 
                         entropy in the network. We also demonstrate that our methodology 
                         can be used to correctly infer directionality of synapses even in 
                         the presence of dynamic and observational Gaussian noise, and is 
                         also successful in providing the effective directionality of 
                         intermodular connectivity, when only mean fields can be 
                         measured.",
                  doi = "10.1103/PhysRevE.97.022303",
                  url = "http://dx.doi.org/10.1103/PhysRevE.97.022303",
                 issn = "1539-3755",
                label = "self-archiving-INPE-MCTIC-GOV-BR",
             language = "en",
           targetfile = "borges_inference.pdf",
        urlaccessdate = "29 nov. 2020"
}


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