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Complete synchronization analysis of neocortical network model

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Abstract

The brain is a complex network consisting of excitatory and inhibitory neurons. The connections between excitatory and inhibitory neurons lead to different dynamical behaviors. The synchronization is a significant behavior among these neurons. In this paper, the synchronization is analyzed by considering a simple neural network model for up-to-down-state oscillation of the cortical network. This neural network model includes a group of excitatory and inhibitory neurons coupled with each other. Synchronization of two neural models is analyzed, and it is revealed that it depends on the coupling of the excitatory neurons rather than the inhibitory ones. The network of neural models is also investigated by considering a one-dimensional and also two-layer structure. The results represent the formation of different dynamical behaviors such as imperfect synchronization, chimera state, and complete synchronization in the networks.

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Data availability statement

Data generated during the current study will be made available on reasonable request.

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Acknowledgements

This work is supported by the Natural Science Basic Research Program of Shaanxi (2021JM-533, 2021JQ-880, 2020JM-646), the Innovation Capability Support Program of Shaanxi (2018GHJD-21), the Support Plan for Sanqin Scholars Innovation Team in Shaanxi Province of China, the Youth Innovation Team of Shaanxi Universities, the Scientific Research Program Funded by Shaanxi Provincial Education Department (21JK0960), the Scientific Research Foundation of Xijing University (XJ21B01), and the Scientific Research Foundation of Xijing University (XJ200202). The work is also partially funded by the Center for Nonlinear Systems, Chennai Institute of Technology, India, vide funding number CIT/CNS/2022/RP-006.

Author information

Authors and Affiliations

  1. Center for Big Data Research in Health, Xijing University, Xi’an, 710123, China

    Jian Kang & Zhen Wang

  2. Xi’an Key Laboratory of Advanced Photo-Electronics Materials and Energy Conversion Device, School of Science, Xijing University, Xi’an, 710123, China

    Zhen Wang

  3. Centre for Artificial Intelligence, Chennai Institute of Technology, Chennai, India

    Janarthanan Ramadoss

  4. Department of Electronics Techniques, Babylon Technical Institute, Al-Furat Al-Awsat Technical University, Babylon, 51001, Iraq

    Ahmed M. Ali Ali

Authors
  1. Jian Kang
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  2. Janarthanan Ramadoss
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  3. Zhen Wang
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  4. Ahmed M. Ali Ali
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Correspondence to Zhen Wang.

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Appendix

Appendix

See Table A1.

Table A1 Biological description of variables and parameters of the model
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Kang, J., Ramadoss, J., Wang, Z. et al. Complete synchronization analysis of neocortical network model. Eur. Phys. J. Spec. Top. 231, 4037–4048 (2022). https://doi.org/10.1140/epjs/s11734-022-00630-6

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