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From Modular to Centralized Organization of Synchronization in Functional Areas of the Cat Cerebral Cortex

Author

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  • Jesús Gómez-Gardeñes
  • Gorka Zamora-López
  • Yamir Moreno
  • Alex Arenas

Abstract

Recent studies have pointed out the importance of transient synchronization between widely distributed neural assemblies to understand conscious perception. These neural assemblies form intricate networks of neurons and synapses whose detailed map for mammals is still unknown and far from our experimental capabilities. Only in a few cases, for example the C. elegans, we know the complete mapping of the neuronal tissue or its mesoscopic level of description provided by cortical areas. Here we study the process of transient and global synchronization using a simple model of phase-coupled oscillators assigned to cortical areas in the cerebral cat cortex. Our results highlight the impact of the topological connectivity in the developing of synchronization, revealing a transition in the synchronization organization that goes from a modular decentralized coherence to a centralized synchronized regime controlled by a few cortical areas forming a Rich-Club connectivity pattern.

Suggested Citation

  • 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.
    • Handle: RePEc:plo:pone00:0012313
    DOI: 10.1371/journal.pone.0012313
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    Cited by:

    1. Wang, Qingyun & Zheng, Yanhong & Ma, Jun, 2013. "Cooperative dynamics in neuronal networks," Chaos, Solitons & Fractals, Elsevier, vol. 56(C), pages 19-27.
    2. 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.
    3. Ahmadi, Negar & Pei, Yulong & Pechenizkiy, Mykola, 2019. "Effect of linear mixing in EEG on synchronization and complex network measures studied using the Kuramoto model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 520(C), pages 289-308.
    4. Yang Tang & Huijun Gao & Wei Zou & Jürgen Kurths, 2012. "Identifying Controlling Nodes in Neuronal Networks in Different Scales," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-13, July.
    5. Capitán, José A. & Aguirre, Jacobo & Manrubia, Susanna, 2015. "Dynamical community structure of populations evolving on genotype networks," Chaos, Solitons & Fractals, Elsevier, vol. 72(C), pages 99-106.
    6. Jing Han & Lin Wang, 2013. "Nondestructive Intervention to Multi-Agent Systems through an Intelligent Agent," PLOS ONE, Public Library of Science, vol. 8(5), pages 1-11, May.
    7. David Samu & Anil K Seth & Thomas Nowotny, 2014. "Influence of Wiring Cost on the Large-Scale Architecture of Human Cortical Connectivity," PLOS Computational Biology, Public Library of Science, vol. 10(4), pages 1-24, April.
    8. Santos, M.S. & Szezech, J.D. & Borges, F.S. & Iarosz, K.C. & Caldas, I.L. & Batista, A.M. & Viana, R.L. & Kurths, J., 2017. "Chimera-like states in a neuronal network model of the cat brain," Chaos, Solitons & Fractals, Elsevier, vol. 101(C), pages 86-91.

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