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Biological neuron modeling based on bifunctional memristor and its application in secure communication

Author

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  • Han, Zhitang
  • Sun, Bo
  • Banerjee, Santo
  • Mou, Jun

Abstract

In recent years, memristors have shown great potential in building artificial neural networks. Many researchers have worked on improving and innovating the design of memristors. The bifunctional memristor proposed in this paper provides new ideas for establishing artificial neural networks. And a novel second-order memristor Hindmarsh–Rose neuron (SOM-HR) is constructed on this basis. The SOM-HR model can well simulate the dynamical behavior of the memristive autapse HR neuron under electromagnetic radiation (EMR), and many types of firing patterns, coexisting firing patterns and state transition are found. In addition, the correctness of the numerical simulation is verified by circuit simulation and digital signal processor (DSP) implementation. Finally, the chaotic sequences generated by this model are used for image encryption, which guarantees the security of the encryption scheme. The results of the study provide a reference for construction and application of complex neural network.

Suggested Citation

  • Han, Zhitang & Sun, Bo & Banerjee, Santo & Mou, Jun, 2024. "Biological neuron modeling based on bifunctional memristor and its application in secure communication," Chaos, Solitons & Fractals, Elsevier, vol. 184(C).
    • Handle: RePEc:eee:chsofr:v:184:y:2024:i:c:s0960077924005721
    DOI: 10.1016/j.chaos.2024.115020
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    References listed on IDEAS

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    1. Yanjie Ma & Jun Mou & Jinshi Lu & Santo Banerjee & Yinghong Cao, 2023. "A Discrete Memristor Coupled Two-Dimensional Generalized Square Hyperchaotic Maps," FRACTALS (fractals), World Scientific Publishing Co. Pte. Ltd., vol. 31(06), pages 1-10.
    2. Cao, Hongli & Wang, Yu & Banerjee, Santo & Cao, Yinghong & Mou, Jun, 2024. "A discrete Chialvo–Rulkov neuron network coupled with a novel memristor model: Design, Dynamical analysis, DSP implementation and its application," Chaos, Solitons & Fractals, Elsevier, vol. 179(C).
    3. Ding, Shoukui & Wang, Ning & Bao, Han & Chen, Bei & Wu, Huagan & Xu, Quan, 2023. "Memristor synapse-coupled piecewise-linear simplified Hopfield neural network: Dynamics analysis and circuit implementation," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
    4. Zhang, Xu & Min, Fuhong & Dou, Yiping & Xu, Yeyin, 2023. "Bifurcation analysis of a modified FitzHugh-Nagumo neuron with electric field," Chaos, Solitons & Fractals, Elsevier, vol. 170(C).
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    Citations

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    Cited by:

    1. Guo, Qixu & Liu, Jian & Nie, Huaqing & Wang, Huiyan & Wang, Bo, 2026. "Distance-controllable hidden multi-scroll coexisting attractors in a complex-variable disturbed laser," Chaos, Solitons & Fractals, Elsevier, vol. 202(P1).
    2. Yao, Zhao & Wang, Lidan & Duan, Shukai, 2025. "Response mechanism of the auditory FitzHugh–Nagumo neuron," Chaos, Solitons & Fractals, Elsevier, vol. 201(P2).
    3. Ma, Tao & Mou, Jun & Chen, Wanzhong, 2025. "Dynamics and implementation of a functional neuron model with hyperchaotic behavior under electromagnetic radiation," Chaos, Solitons & Fractals, Elsevier, vol. 190(C).
    4. Mao, Yidan & Dong, Yujiao & Lu, Zhenzhou & Xiang, Chenyang & Wang, Jinqi & Liang, Yan, 2025. "Second-order locally active memristor based neuronal circuit," Chaos, Solitons & Fractals, Elsevier, vol. 195(C).
    5. Shi, Fan & Cao, Yinghong & Banerjee, Santo & Ahmad, Adil M. & Mou, Jun, 2024. "A novel neural networks with memristor coupled memcapacitor-synapse neuron," Chaos, Solitons & Fractals, Elsevier, vol. 189(P2).
    6. Anzo-Hernández, Andrés, 2026. "Dynamics of a Hindmarsh–Rose neuron with dual memristive inputs: Electromagnetic flux and autapse feedback," Chaos, Solitons & Fractals, Elsevier, vol. 203(C).

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