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

Gayathri Vivekanandan, Mahtab Mehrabbeik, Hayder Natiq, Karthikeyan Rajagopal and Esteban Tlelo-Cuautle ()
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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

Mathematics, 2022, vol. 10, issue 16, 1-9

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.

Keywords: fractional-order derivative; memristive Wilson model; synchronization; multistability (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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