author = "Lameu, Ewandson Luiz and Yanchuk, S. and Macau, Elbert Einstein 
                         Nehrer and Borges, F. S. and Iarosz, K. C. and Caldas, I. L. and 
                         Protachevicz, P. R. and Borges, R. R. and Viana, R. L. and Szezech 
                         Junior, J. D. and Batista, A. M. and Kurths, J.",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Technical 
                         University of Berlin} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Universidade Federal do ABC (UFABC)} and 
                         {Humboldt University} and {Universidade de S{\~a}o Paulo (USP)} 
                         and {Universidade Estadual de Ponta Grossa} and {Universidade 
                         Federal Tecnol{\'o}gica do Paran{\'a} (UFTPR)} and {Universidade 
                         Federal do Paran{\'a} (UFPR)} and {Universidade Federal de Ponta 
                         Grossa} and {Universidade de S{\~a}o Paulo (USP)} and {Humboldt 
                title = "Recurrence quantification analysis for the identification of burst 
                         phase synchronisation",
              journal = "Chaos",
                 year = "2018",
               volume = "28",
               number = "8",
                pages = "e085701",
                month = "Aug.",
             abstract = "In this work, we apply the spatial recurrence quantification 
                         analysis (RQA) to identify chaotic burst phase synchronisation in 
                         networks. We consider one neural network with small-world topology 
                         and another one composed of small-world subnetworks. The neuron 
                         dynamics is described by the Rulkov map, which is a 
                         two-dimensional map that has been used to model chaotic bursting 
                         neurons. We show that with the use of spatial RQA, it is possible 
                         to identify groups of synchronised neurons and determine their 
                         size. For the single network, we obtain an analytical expression 
                         for the spatial recurrence rate using a Gaussian approximation. In 
                         clustered networks, the spatial RQA allows the identification of 
                         phase synchronisation among neurons within and between the 
                         subnetworks. Our results imply that RQA can serve as a useful tool 
                         for studying phase synchronisation even in networks of networks.",
                  doi = "10.1063/1.5024324",
                  url = "http://dx.doi.org/10.1063/1.5024324",
                 issn = "1054-1500",
             language = "pt",
           targetfile = "lameu-recurrence.pdf",
        urlaccessdate = "24 nov. 2020"