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A network of networks model to study phase synchronization using structural connection matrix of human brain

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  • Ferrari, F.A.S.
  • Viana, R.L.
  • Reis, A.S.
  • Iarosz, K.C.
  • Caldas, I.L.
  • Batista, A.M.

Abstract

The cerebral cortex plays a key role in complex cortical functions. It can be divided into areas according to their function (motor, sensory and association areas). In this paper, the cerebral cortex is described as a network of networks (cortex network), we consider that each cortical area is composed of a network with small-world property (cortical network). The neurons are assumed to have bursting properties with the dynamics described by the Rulkov model. We study the phase synchronization of the cortex network and the cortical networks. In our simulations, we verify that synchronization in cortex network is not homogeneous. Besides, we focus on the suppression of neural phase synchronization. Synchronization can be related to undesired and pathological abnormal rhythms in the brain. For this reason, we consider the delayed feedback control to suppress the synchronization. We show that delayed feedback control is efficient to suppress synchronous behavior in our network model when an appropriate signal intensity and time delay are defined.

Suggested Citation

  • Ferrari, F.A.S. & Viana, R.L. & Reis, A.S. & Iarosz, K.C. & Caldas, I.L. & Batista, A.M., 2018. "A network of networks model to study phase synchronization using structural connection matrix of human brain," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 162-170.
    • Handle: RePEc:eee:phsmap:v:496:y:2018:i:c:p:162-170
    DOI: 10.1016/j.physa.2017.12.129
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    References listed on IDEAS

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    1. Jesús Gómez-Gardeñes & Gorka Zamora-López & Yamir Moreno & Alex Arenas, 2010. "From Modular to Centralized Organization of Synchronization in Functional Areas of the Cat Cerebral Cortex," PLOS ONE, Public Library of Science, vol. 5(8), pages 1-11, August.
    2. Murilo S Baptista & Hai-Peng Ren & Johen C M Swarts & Rodrigo Carareto & Henk Nijmeijer & Celso Grebogi, 2012. "Collective Almost Synchronisation in Complex Networks," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-11, November.
    3. Chen, Hanshuang & Zhang, Jiqian & Liu, Jianqing, 2008. "Enhancement of neuronal coherence by diversity in coupled Rulkov-map models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(5), pages 1071-1076.
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    Citations

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    Cited by:

    1. Trobia, José & de Souza, Silvio L.T. & dos Santos, Margarete A. & Szezech, José D. & Batista, Antonio M. & Borges, Rafael R. & Pereira, Leandro da S. & Protachevicz, Paulo R. & Caldas, Iberê L. & Iaro, 2022. "On the dynamical behaviour of a glucose-insulin model," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).
    2. Ma, Mihua & Cai, Jianping & Zhang, Hua, 2019. "Quasi-synchronization of Lagrangian networks with parameter mismatches and communication delays via aperiodically intermittent pinning control," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 1146-1160.
    3. Reis, A.S. & Brugnago, E.L. & Viana, R.L. & Batista, A.M. & Iarosz, K.C. & Ferrari, F.A.S. & Caldas, I.L., 2023. "The role of the fitness model in the suppression of neuronal synchronous behavior with three-stage switching control in clustered networks," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).
    4. Reis, Adriane S. & Iarosz, Kelly C. & Ferrari, Fabiano A.S. & Caldas, Iberê L. & Batista, Antonio M. & Viana, Ricardo L., 2021. "Bursting synchronization in neuronal assemblies of scale-free networks," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).

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