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The European Physical Journal Special Topics

, Volume 228, Issue 10, pp 2023–2033 | Cite as

Chimera in a network of memristor-based Hopfield neural network

  • Fatemeh Parastesh
  • Sajad JafariEmail author
  • Hamed Azarnoush
  • Boshra Hatef
  • Hamidreza Namazi
  • Dawid Dudkowski
Regular Article
  • 27 Downloads
Part of the following topical collections:
  1. Memristor-based Systems: Nonlinearity, Dynamics and Applications

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.

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Copyright information

© EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Fatemeh Parastesh
    • 1
  • Sajad Jafari
    • 1
    Email author
  • Hamed Azarnoush
    • 1
  • Boshra Hatef
    • 2
  • Hamidreza Namazi
    • 3
  • Dawid Dudkowski
    • 4
  1. 1.Department of Biomedical EngineeringAmirkabir University of TechnologyTehranIran
  2. 2.Neuroscience Research Center, Baqiyatallah University of Medical SciencesTehranIran
  3. 3.School of Engineering, Monash UniversitySelangorMalaysia
  4. 4.Division of Dynamics, Lodz University of TechnologyLodzPoland

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