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A two-layered brain network model and its chimera state.

Ling Kang1, Changhai Tian1,2, Siyu Huo1

  • 1Department of Physics, East China Normal University, Shanghai, 200062, P.R. China.

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|October 9, 2019
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This summary is machine-generated.

Researchers developed a two-layered brain network model to study neural synchronization. The model reveals that specific structural and dynamic parameters are crucial for the emergence of complex patterns, including chimera states, in the cerebral cortex.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Network Science

Background:

  • The cerebral cortex exhibits complex network dynamics involving interhemispheric communication via the corpus callosum.
  • Understanding neural synchronization patterns is key to deciphering brain function.

Purpose of the Study:

  • To develop and analyze a two-layered neural network model of the cerebral cortex.
  • To investigate the emergence of synchronization patterns, particularly chimera states, in interhemispheric neural networks.
  • To identify key parameters influencing these synchronization phenomena.

Main Methods:

  • Construction of a two-layered network model representing the left and right cerebral hemispheres.
  • Simulation of coupled neuron dynamics with varying parameters (coupling strengths, phase, inter-couplings, clustering).
  • Extension to a generalized two-layered network with clustered subnetworks for each hemisphere.

Main Results:

  • The model reproduces diverse synchronization patterns, including chimera states, influenced by system parameters.
  • Chimera state emergence is sensitive to the number of inter-couplings and network structure (clustering).
  • Synchronization borderline is explained through theoretical analysis.

Conclusions:

  • Chimera states arise from a precise interplay between structural (inter-couplings, clustering) and dynamic (coupling strengths, phase) parameters.
  • The findings offer insights into the mechanisms underlying brain function and interhemispheric coordination.
  • This modeling approach provides a framework for studying complex neural network behaviors.