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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Recurrence quantification analysis for the identification of burst phase synchronisation

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Author(s):
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Lameu, E. L. [1, 2] ; Yanchuk, S. [3] ; Macau, E. E. N. [2, 4] ; Borges, F. S. [5] ; Iarosz, K. C. [1, 6, 7, 8] ; Caldas, I. L. [8] ; Protachevicz, P. R. [9] ; Borges, R. R. [10] ; Viana, R. L. [11] ; Szezech, Jr., J. D. [9, 12] ; Batista, A. M. [6, 7, 8, 9, 12] ; Kurths, J. [1, 6]
Total Authors: 12
Affiliation:
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[1] Humboldt Univ, Dept Phys, D-12489 Berlin - Germany
[2] Natl Inst Space Res, BR-12227010 Sao Jose Dos Campos, SP - Brazil
[3] Tech Univ Berlin, Inst Math, D-10623 Berlin - Germany
[4] Univ Fed Sao Paulo, ICT Inst Sci & Technol, BR-12231280 Sao Jose Dos Campos, SP - Brazil
[5] Fed Univ ABC, Ctr Math Computat & Cognit, BR-09606045 Sao Bernardo Do Campo, SP - Brazil
[6] Potsdam Inst Climate Impact Res, D-14473 Potsdam - Germany
[7] Univ Aberdeen, SUPA, Inst Complex Syst & Math Biol, Aberdeen AB24 3UE - Scotland
[8] Univ Sao Paulo, Inst Phys, BR-05508900 Sao Paulo - Brazil
[9] Univ Estadual Ponta Grossa, Program Postgraduat Sci, BR-84030900 Ponta Grossa, Parana - Brazil
[10] Univ Tecnol Fed Parana, Dept Math, BR-84016210 Ponta Grossa, Parana - Brazil
[11] Univ Fed Parana, Dept Phys, BR-80060000 Curitiba, Parana - Brazil
[12] Univ Estadual Ponta Grossa, Dept Math & Stat, BR-84030900 Ponta Grossa, Parana - Brazil
Total Affiliations: 12
Document type: Journal article
Source: Chaos; v. 28, n. 8 AUG 2018.
Web of Science Citations: 3
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. Published by AIP Publishing. (AU)

FAPESP's process: 16/23398-8 - Synaptic plasticity in neuronal networks with time delay
Grantee:Ewandson Luiz Lameu
Support type: Scholarships in Brazil - Post-Doctorate
FAPESP's process: 15/50122-0 - Dynamic phenomena in complex networks: basics and applications
Grantee:Elbert Einstein Nehrer Macau
Support type: Research Projects - Thematic Grants
FAPESP's process: 17/18977-1 - Analysis of electrical synapses contribution in neuronal synchronization
Grantee:Fernando da Silva Borges
Support type: Scholarships in Brazil - Post-Doctorate
FAPESP's process: 15/07311-7 - Dynamic behaviour of neural networks
Grantee:Kelly Cristiane Iarosz
Support type: Scholarships in Brazil - Post-Doctorate
FAPESP's process: 11/19296-1 - Nonlinear dynamics
Grantee:Iberê Luiz Caldas
Support type: Research Projects - Thematic Grants
FAPESP's process: 17/13502-5 - Synchronisation in time-delayed networks with synaptic plasticity
Grantee:Ewandson Luiz Lameu
Support type: Scholarships abroad - Research Internship - Post-doctor
FAPESP's process: 17/20920-8 - Plasticity in neuronal networks
Grantee:Kelly Cristiane Iarosz
Support type: Scholarships abroad - Research Internship - Post-doctor