Eur. Phys. J. Special Topics 228, 2023-2033 (2019)
https://doi.org/10.1140/epjst/e2019-800240-5

Regular Article

Chimera in a network of memristor-based Hopfield neural network

¹ Department of Biomedical Engineering, Amirkabir University of Technology, 424 Hafez Ave., Tehran 15875-4413, Iran
² Neuroscience Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran
³ School of Engineering, Monash University, Selangor, Malaysia
⁴ Division of Dynamics, Lodz University of Technology, Stefanowskiego 1/15, 90-924 Lodz, Poland

^a e-mail: sajadjafari@aut.ac.ir

Received: 28 December 2018
Received in final form: 25 March 2019
Published online: 14 October 2019

Abstract

Memristors have shown great potential to yield novel features in various domains. Therefore, memristor-based systems are being studied in widespread applications. In this paper, a newly proposed hyperbolic-type memristor-based Hopfield neural network is studied, as a single unit of a coupled network. Particularly, the effects of the coupling between each state variable of the system on the network behavior is investigated. It is observed that changing the coupling variable leads to different patterns at each coupling strength, including partial chimera state, chimera state, synchronization, imperfect synchronization and oscillation death. When the memristor-based elements are coupled with each other, increasing the coupling strength causes a regular transition from asynchronization to chimera state and then toward synchronization.