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Synchronization and patterns in a memristive network in noisy electric field

Author

Listed:
  • Feifei Yang

    (Lanzhou University of Technology)

  • Xikui Hu

    (Chongqing University of Posts and Telecommunications)

  • Guodong Ren

    (Lanzhou University of Technology)

  • Jun Ma

    (Lanzhou University of Technology
    Chongqing University of Posts and Telecommunications
    Lanzhou University of Technology)

Abstract

A simple neural circuit coupled by magnetic flux-controlled memristor (MFCM) can be controlled to describe the electromagnetic effect and radiation on biological neurons. In this paper, the effect of external electric field on biophysical neurons is identified by adding a charge-controlled memristor into a nonlinear circuit. This memristive circuit can present a variety of firing patterns by tuning the angular frequency of an external voltage source. As a result, the physical field energy in this neural circuit and its equivalent Hamilton energy for memristive neuron are dependent on the firing modes of neural activities. For clustered neurons, field energy is exchanged and propagated to obtain fast energy balance by regulating the charge flow in the chain network. Indeed, the growth of coupling intensity is controlled by the energy difference between adjacent neurons, and perfect energy balance keeps a saturation value for coupling intensity. The collective behaviors of memristive neurons in the chain network are adjusted by regulating the coupling intensity for the exchange of charges. In addition, noisy disturbance from external electric field is applied to study the synchronization stability and wave propagation in the network, and energy flow is estimated. Graphical abstract A simple neural circuit coupled by magnetic flux-controlled memristor (MFCM) can be controlled to describe the electromagnetic effect and radiation on biological neurons. In this paper, the effect of external electric field on biophysical neurons is identified by adding a charge-controlled memristor into a nonlinear circuit. This memristive circuit can present a variety of firing patterns by tuning the angular frequency of an external voltage source. As a result, the physical field energy in this neural circuit and its equivalent Hamilton energy for memristive neuron are dependent on the firing modes of neural activities. For clustered neurons, field energy is exchanged and propagated to obtain fast energy balance by regulating the charge flow in the chain network. Indeed, the growth of coupling intensity is controlled by the energy difference between adjacent neurons, and perfect energy balance keeps a saturation value for coupling intensity. The collective behaviors of memristive neurons in the chain network are adjusted by regulating the coupling intensity for the exchange of charges. In addition, noisy disturbance from external electric field is applied to study the synchronization stability and wave propagation in the network, and energy flow is estimated.

Suggested Citation

  • Feifei Yang & Xikui Hu & Guodong Ren & Jun Ma, 2023. "Synchronization and patterns in a memristive network in noisy electric field," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 96(6), pages 1-14, June.
    • Handle: RePEc:spr:eurphb:v:96:y:2023:i:6:d:10.1140_epjb_s10051-023-00549-4
    DOI: 10.1140/epjb/s10051-023-00549-4
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    References listed on IDEAS

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    1. Yu, Dong & Wang, Guowei & Ding, Qianming & Li, Tianyu & Jia, Ya, 2022. "Effects of bounded noise and time delay on signal transmission in excitable neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 157(C).
    2. Masoliver, Maria & Masoller, Cristina & Zakharova, Anna, 2021. "Control of coherence resonance in multiplex neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 145(C).
    3. Chen, Yonghui & Zhang, Xian & Xue, Yu, 2022. "Global exponential synchronization of high-order quaternion Hopfield neural networks with unbounded distributed delays and time-varying discrete delays," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 193(C), pages 173-189.
    4. Xu, Ying & Guo, Yeye & Ren, Guodong & Ma, Jun, 2020. "Dynamics and stochastic resonance in a thermosensitive neuron," Applied Mathematics and Computation, Elsevier, vol. 385(C).
    5. Ajay Deep Kachhvah, 2017. "The effect of distributed time-delays on the synchronization of neuronal networks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 90(1), pages 1-5, January.
    6. Lin, Hairong & Wang, Chunhua, 2020. "Influences of electromagnetic radiation distribution on chaotic dynamics of a neural network," Applied Mathematics and Computation, Elsevier, vol. 369(C).
    7. Zhou, Ping & Yao, Zhao & Ma, Jun & Zhu, Zhigang, 2021. "A piezoelectric sensing neuron and resonance synchronization between auditory neurons under stimulus," Chaos, Solitons & Fractals, Elsevier, vol. 145(C).
    8. Wu, Fuqiang & Hu, Xikui & Ma, Jun, 2022. "Estimation of the effect of magnetic field on a memristive neuron," Applied Mathematics and Computation, Elsevier, vol. 432(C).
    9. Shafiya, M. & Nagamani, G. & Dafik, D., 2022. "Global synchronization of uncertain fractional-order BAM neural networks with time delay via improved fractional-order integral inequality," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 191(C), pages 168-186.
    10. Takembo, C. Ntahkie & Nyifeh, P. & Fouda, H.P. Ekobena & Kofane, T.C., 2022. "Modulated wave pattern stability in chain neural networks under high–low frequency magnetic radiation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).
    11. Lukas N. Groschner & Jonatan G. Malis & Birte Zuidinga & Alexander Borst, 2022. "A biophysical account of multiplication by a single neuron," Nature, Nature, vol. 603(7899), pages 119-123, March.
    12. Plotnikov, Sergei A. & Fradkov, Alexander L., 2019. "On synchronization in heterogeneous FitzHugh–Nagumo networks," Chaos, Solitons & Fractals, Elsevier, vol. 121(C), pages 85-91.
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