The European Physical Journal Special Topics

, Volume 228, Issue 10, pp 1943–1950 | Cite as

Detecting bifurcation points in a memristive neuron model

  • Yongjian Liu
  • Fahimeh NazarimehrEmail author
  • Abdul Jalil M. Khalaf
  • Ahmed Alsaedi
  • Tasawar Hayat
Regular Article
Part of the following topical collections:
  1. Memristor-based Systems: Nonlinearity, Dynamics and Applications


In this paper, bifurcations of a memristive neuron model are analyzed. The system shows different limit cycles and chaotic attractors by varying external current. The focus of this paper is finding bifurcation points of the system and predicting them using critical slowing down indicators. The system has different tipping points such as transition from a period-2 limit cycle to period-3 limit cycle, period-3 limit cycle to period-6 limit cycle and limit cycle to chaos. Two critical slowing down indicators have been used to predict tipping points of the system. The first critical slowing down indicator is autocorrelation at lag-1 which cannot indicate bifurcation points of the system. The second one is Lyapunov exponent which shows acceptable results in prediction of bifurcation points of the memristive neuron model.


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Copyright information

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

Authors and Affiliations

  • Yongjian Liu
    • 1
  • Fahimeh Nazarimehr
    • 2
    Email author
  • Abdul Jalil M. Khalaf
    • 3
  • Ahmed Alsaedi
    • 4
  • Tasawar Hayat
    • 4
    • 5
  1. 1.Guangxi Colleges and Universities Key Laboratory of Complex System Optimization and Big Data Processing, Yulin Normal UniversityYulin, GuangxiP.R. China
  2. 2.Biomedical Engineering Department, Amirkabir University of TechnologyTehranIran
  3. 3.Ministry of Higher Education and Scientific ResearchBaghdadIraq
  4. 4.NAAM Research Group, King Abdulaziz, University of JeddahJeddahSaudi Arabia
  5. 5.Department of MathematicsQuaid-I-Azam University 45320IslamabadPakistan

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