IDEAS home Printed from https://ideas.repec.org/a/spr/eurphb/v97y2024i7d10.1140_epjb_s10051-024-00719-y.html
   My bibliography  Save this article

Dynamic behavior of memristor ML neurons and its application in secure communication

Author

Listed:
  • Kaijun Wu

    (Lanzhou Jiaotong University)

  • Zhaoxue Huang

    (Lanzhou Jiaotong University)

  • Mingjun Yan

    (Lanzhou Jiaotong University)

Abstract

Improving neurons in a real physiological environment and studying their electrical behavior is crucial for understanding human cognitive brain functions and neural dynamics. Neuronal cells reside in a complex physiological environment, where the electromagnetic fields generated by ion transmembrane movements affect their discharge activity. Therefore, to better simulate the real conditions of biological neurons, this paper incorporated the characteristics of the memristor and constructed a four-dimensional Morris-Lecar (ML) neuron model by adding a magneto-controlled memristor into the three-dimensional ML neuron model. Through the study of time series diagrams, phase plane diagrams, inter-spike interval (ISI) bifurcation diagrams, we explored the effects of the feedback gain coefficient and the relationship coefficient between membrane potential and magnetic flux on the firing behavior of neurons in the model. It was found that variations in these two parameters can lead to complex firing patterns in neurons. We also utilized the maximum Lyapunov exponent and dissipative theory to investigate the chaotic synchronization phenomenon in the memristor-based ML neuron model. Additionally, we explored the impact of noise on neuronal synchronization behavior within the system, finding that an appropriate noise intensity can effectively accelerate the neurons’ attainment of a synchronized state. Finally, applying the chaotic synchronization system to secure Communication, the simulation results and related analysis demonstrate that the system excels in encrypting and decrypting voice signals, offering high levels of security and confidentiality. Graphical abstract Simulation results of speech signal encryption and decryption

Suggested Citation

  • Kaijun Wu & Zhaoxue Huang & Mingjun Yan, 2024. "Dynamic behavior of memristor ML neurons and its application in secure communication," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 97(7), pages 1-21, July.
  • Handle: RePEc:spr:eurphb:v:97:y:2024:i:7:d:10.1140_epjb_s10051-024-00719-y
    DOI: 10.1140/epjb/s10051-024-00719-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1140/epjb/s10051-024-00719-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1140/epjb/s10051-024-00719-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Xu, Ying & Jia, Ya & Ma, Jun & Alsaedi, Ahmed & Ahmad, Bashir, 2017. "Synchronization between neurons coupled by memristor," Chaos, Solitons & Fractals, Elsevier, vol. 104(C), pages 435-442.
    2. Wang, Xueqin & Yu, Dong & Li, Tianyu & Jia, Ya, 2023. "Logistic stochastic resonance in the Hodgkin–Huxley neuronal system under electromagnetic induction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    3. Baysal, Veli & Solmaz, Ramazan & Ma, Jun, 2023. "Investigation of chaotic resonance in Type-I and Type-II Morris-Lecar neurons," Applied Mathematics and Computation, Elsevier, vol. 448(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jahanshahi, Hadi & Yousefpour, Amin & Munoz-Pacheco, Jesus M. & Kacar, Sezgin & Pham, Viet-Thanh & Alsaadi, Fawaz E., 2020. "A new fractional-order hyperchaotic memristor oscillator: Dynamic analysis, robust adaptive synchronization, and its application to voice encryption," Applied Mathematics and Computation, Elsevier, vol. 383(C).
    2. Wu, Huagan & Gu, Jinxiang & Guo, Yixuan & Chen, Mo & Xu, Quan, 2024. "Biphasic action potentials in an individual cellular neural network cell," Chaos, Solitons & Fractals, Elsevier, vol. 182(C).
    3. Guo, Yeye & Wang, Chunni & Yao, Zhao & Xu, Ying, 2022. "Desynchronization of thermosensitive neurons by using energy pumping," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 602(C).
    4. Bodo, B. & Armand Eyebe Fouda, J.S. & Mvogo, A. & Tagne, S., 2018. "Experimental hysteresis in memristor based Duffing oscillator," Chaos, Solitons & Fractals, Elsevier, vol. 115(C), pages 190-195.
    5. Kaijun Wu & Jiawei Li, 2023. "Effects of high–low-frequency electromagnetic radiation on vibrational resonance in FitzHugh–Nagumo neuronal systems," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 96(9), pages 1-19, September.
    6. Branislav Rehák & Volodymyr Lynnyk, 2021. "Synchronization of a Network Composed of Stochastic Hindmarsh–Rose Neurons," Mathematics, MDPI, vol. 9(20), pages 1-16, October.
    7. Dutta, Maitreyee & Roy, Binoy Krishna, 2021. "A new memductance-based fractional-order chaotic system and its fixed-time synchronisation," Chaos, Solitons & Fractals, Elsevier, vol. 145(C).
    8. Sun, Guoping & Yang, Feifei & Ren, Guodong & Wang, Chunni, 2023. "Energy encoding in a biophysical neuron and adaptive energy balance under field coupling," Chaos, Solitons & Fractals, Elsevier, vol. 169(C).
    9. Liu, Zhilong & Zhou, Ping & Ma, Jun & Hobiny, Aatef & Alzahrani, Faris, 2020. "Autonomic learning via saturation gain method, and synchronization between neurons," Chaos, Solitons & Fractals, Elsevier, vol. 131(C).
    10. Shadizadeh, S. Mohadeseh & Nazarimehr, Fahimeh & Jafari, Sajad & Rajagopal, Karthikeyan, 2022. "Investigating different synaptic connections of the Chay neuron model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    11. Wang, Chunni & Liu, Zhilong & Hobiny, Aatef & Xu, Wenkang & Ma, Jun, 2020. "Capturing and shunting energy in chaotic Chua circuit," Chaos, Solitons & Fractals, Elsevier, vol. 134(C).
    12. Fateev, I. & Polezhaev, A., 2024. "Chimera states in a lattice of superdiffusively coupled neurons," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).
    13. Wang, Zhen & Parastesh, Fatemeh & Rajagopal, Karthikeyan & Hamarash, Ibrahim Ismael & Hussain, Iqtadar, 2020. "Delay-induced synchronization in two coupled chaotic memristive Hopfield neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 134(C).
    14. Dang, Shihong & Bayani, Atiyeh & Tian, Huaigu & Wang, Zhen & Parastesh, Fatemeh & Nazarimehr, Fahimeh, 2024. "Investigating the route to synchronization in real-world neuronal networks of autaptic photosensitive neurons," Chaos, Solitons & Fractals, Elsevier, vol. 186(C).
    15. Li, Xing & Zou, Jianxun & Feng, Zhe & Wu, Zuheng & Xu, Zuyu & Yang, Fei & Zhu, Yunlai & Dai, Yuehua, 2023. "Thermal design engineering for improving the variation of memristor threshold," Chaos, Solitons & Fractals, Elsevier, vol. 171(C).
    16. Liu, Dan & Zhao, Song & Luo, Xiaoyuan & Yuan, Yi, 2021. "Synchronization for fractional-order extended Hindmarsh-Rose neuronal models with magneto-acoustical stimulation input," Chaos, Solitons & Fractals, Elsevier, vol. 144(C).
    17. Ge, Mengyan & Lu, Lulu & Xu, Ying & Mamatimin, Rozihajim & Pei, Qiming & Jia, Ya, 2020. "Vibrational mono-/bi-resonance and wave propagation in FitzHugh–Nagumo neural systems under electromagnetic induction," Chaos, Solitons & Fractals, Elsevier, vol. 133(C).
    18. Li, Zhijun & Chen, Kaijie, 2023. "Neuromorphic behaviors in a neuron circuit based on current-controlled Chua Corsage Memristor," Chaos, Solitons & Fractals, Elsevier, vol. 175(P1).
    19. Zhu, Wanting & Sun, Kehui & Wang, Huihai & Fu, Longxiang & Minati, Ludovico, 2024. "Dynamics, synchronization and analog circuit implementation of a discrete neuron-like map with pulsating spiral dynamics," Chaos, Solitons & Fractals, Elsevier, vol. 186(C).
    20. Erkan, Erdem, 2023. "Signal encoding performance of astrocyte-dressed Morris Lecar neurons," Chaos, Solitons & Fractals, Elsevier, vol. 177(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:eurphb:v:97:y:2024:i:7:d:10.1140_epjb_s10051-024-00719-y. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.