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Fractional-Order Memristive Wilson Neuron Model: Dynamical Analysis and Synchronization Patterns

Author

Listed:
  • Gayathri Vivekanandan

    (Centre for Artificial Intelligence, Chennai Institute of Technology, Chennai 600069, India)

  • Mahtab Mehrabbeik

    (Department of Biomedical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran 159163-4311, Iran)

  • Hayder Natiq

    (Information Technology Collage, Imam Ja’afar Al-Sadiq University, Baghdad 10001, Iraq)

  • Karthikeyan Rajagopal

    (Centre for Nonlinear Systems, Chennai Institute of Technology, Chennai 600069, India)

  • Esteban Tlelo-Cuautle

    (INAOE, Department of Electronics, Luis Enrique Erro No.1, Santa María Tonanzintla, San Andrés Cholula, Puebla 72840, Mexico)

Abstract

Fractional nonlinear systems have been considered in many fields due to their ability to bring memory-dependent properties into various systems. Therefore, using fractional derivatives to model real-world phenomena, such as neuronal dynamics, is of significant importance. This paper presents the fractional memristive Wilson neuron model and studies its dynamics as a single neuron. Furthermore, the collective behavior of neurons is researched when they are locally and diffusively coupled in a ring topology. It is found that the fractional-order neurons are bistable in some values of the fractional order. Additionally, complete synchronization, lag synchronization, phase synchronization, and sine-like synchronization patterns can be observed in the constructed network with different fractional orders.

Suggested Citation

  • Gayathri Vivekanandan & Mahtab Mehrabbeik & Hayder Natiq & Karthikeyan Rajagopal & Esteban Tlelo-Cuautle, 2022. "Fractional-Order Memristive Wilson Neuron Model: Dynamical Analysis and Synchronization Patterns," Mathematics, MDPI, vol. 10(16), pages 1-9, August.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:16:p:2827-:d:883883
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    References listed on IDEAS

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    Cited by:

    1. Fateev, I. & Polezhaev, A., 2024. "Chimera states in a lattice of superdiffusively coupled neurons," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).

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