IDEAS home Printed from https://ideas.repec.org/a/eee/apmaco/v432y2022ics0096300322004404.html
   My bibliography  Save this article

Estimation of the effect of magnetic field on a memristive neuron

Author

Listed:
  • Wu, Fuqiang
  • Hu, Xikui
  • Ma, Jun

Abstract

A cell's action potential can be induced by rapid pumping and changes in intracellular and extracellular ions, and a magnetic field is generated to regulate neural activity effectively. Stochastic propagation and diffusion of ions across the cell membrane can break the balance of ion concentration. An appropriate external stimulus can speed up the exchange of ions and then different firing modes in the neuron can be induced for energy release. An improved neuron model is proposed and suggested to explore the effect of the magnetic field from the physical aspect, which a memristive channel with locally active feature is added to the known Hodgkin-Huxley (HH) neuron model by connecting a memristor in parallel with the HH neural circuit. That is, the memristive channel current is equivalent to the induction current, and an additive magnetic flux variable is introduced into the HH model, and its evolution is guided by the law of electromagnetic induction and energy conversion. The induction current can modulate the excitability and the threshold for inducing distinct action potentials. The transition from different-type firing patterns is induced by modulating the inductive current. In addition, an external magnetic field from two sources is applied to estimate the mode transition and firing patterns by regulating the magnetic flux, which induces further changes in the neural activity of the neuron. The neuron keeps quiescent when two identical magnetic field sources are emitted at high frequency. While the neuron can be activated to generate continuous firing patterns when the two applied magnetic field sources show certain diversity in the frequency. The potential mechanism is that the frequency synthesized by the two stimuli is close to the intrinsic frequency of the neuron, and then bursting can be induced effectively.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:apmaco:v:432:y:2022:i:c:s0096300322004404
    DOI: 10.1016/j.amc.2022.127366
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0096300322004404
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.amc.2022.127366?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. Wu, Fuqiang & Wang, Chunni & Jin, Wuyin & Ma, Jun, 2017. "Dynamical responses in a new neuron model subjected to electromagnetic induction and phase noise," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 81-88.
    2. Dmitri B. Strukov & Gregory S. Snider & Duncan R. Stewart & R. Stanley Williams, 2008. "The missing memristor found," Nature, Nature, vol. 453(7191), pages 80-83, May.
    3. Wu, Fuqiang & Ma, Jun & Zhang, Ge, 2019. "A new neuron model under electromagnetic field," Applied Mathematics and Computation, Elsevier, vol. 347(C), pages 590-599.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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).
    2. Guo, Yitong & Xie, Ying & Ma, Jun, 2023. "Nonlinear responses in a neural network under spatial electromagnetic radiation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 626(C).
    3. Njitacke, Zeric Tabekoueng & Ramadoss, Janarthanan & Takembo, Clovis Ntahkie & Rajagopal, Karthikeyan & Awrejcewicz, Jan, 2023. "An enhanced FitzHugh–Nagumo neuron circuit, microcontroller-based hardware implementation: Light illumination and magnetic field effects on information patterns," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).
    4. Yu, Xihong & Bao, Han & Chen, Mo & Bao, Bocheng, 2023. "Energy balance via memristor synapse in Morris-Lecar two-neuron network with FPGA implementation," Chaos, Solitons & Fractals, Elsevier, vol. 171(C).
    5. Shao, Yan & Wu, Fuqiang & Wang, Qingyun, 2024. "Dynamics and stability of neural systems with indirect interactions involved energy levels," Chaos, Solitons & Fractals, Elsevier, vol. 183(C).
    6. Xu, Ying & Ren, Guodong & Ma, Jun, 2023. "Patterns stability in cardiac tissue under spatial electromagnetic radiation," Chaos, Solitons & Fractals, Elsevier, vol. 171(C).
    7. 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.

    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. Zhang, Ge & Ma, Jun & Alsaedi, Ahmed & Ahmad, Bashir & Alzahrani, Faris, 2018. "Dynamical behavior and application in Josephson Junction coupled by memristor," Applied Mathematics and Computation, Elsevier, vol. 321(C), pages 290-299.
    2. 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.
    3. Li, Fan, 2020. "Effect of field coupling on the wave propagation in the neuronal network," Chaos, Solitons & Fractals, Elsevier, vol. 141(C).
    4. 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.
    5. Kafraj, Mohadeseh Shafiei & Parastesh, Fatemeh & Jafari, Sajad, 2020. "Firing patterns of an improved Izhikevich neuron model under the effect of electromagnetic induction and noise," Chaos, Solitons & Fractals, Elsevier, vol. 137(C).
    6. Das, Saureesh, 2022. "Recurrence quantification and bifurcation analysis of electrical activity in resistive/memristive synapse coupled Fitzhugh–Nagumo type neurons," Chaos, Solitons & Fractals, Elsevier, vol. 165(P1).
    7. Hou, Zhangliang & Ma, Jun & Zhan, Xuan & Yang, Lijian & Jia, Ya, 2021. "Estimate the electrical activity in a neuron under depolarization field," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    8. Wu, Fuqiang & Zhou, Ping & Alsaedi, Ahmed & Hayat, Tasawar & Ma, Jun, 2018. "Synchronization dependence on initial setting of chaotic systems without equilibria," Chaos, Solitons & Fractals, Elsevier, vol. 110(C), pages 124-132.
    9. Ke Ding & Zahra Rostami & Sajad Jafari & Boshra Hatef, 2018. "Investigation of Cortical Signal Propagation and the Resulting Spatiotemporal Patterns in Memristor-Based Neuronal Network," Complexity, Hindawi, vol. 2018, pages 1-20, June.
    10. Min, Fuhong & Zhang, Wen & Ji, Ziyi & Zhang, Lei, 2021. "Switching dynamics of a non-autonomous FitzHugh-Nagumo circuit with piecewise-linear flux-controlled memristor," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    11. Feng, Liang & Hu, Cheng & Yu, Juan & Jiang, Haijun & Wen, Shiping, 2021. "Fixed-time Synchronization of Coupled Memristive Complex-valued Neural Networks," Chaos, Solitons & Fractals, Elsevier, vol. 148(C).
    12. Hu, Yongbing & Li, Qian & Ding, Dawei & Jiang, Li & Yang, Zongli & Zhang, Hongwei & Zhang, Zhixin, 2021. "Multiple coexisting analysis of a fractional-order coupled memristive system and its application in image encryption," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    13. Yan, Dengwei & Wang, Lidan & Duan, Shukai & Chen, Jiaojiao & Chen, Jiahao, 2021. "Chaotic Attractors Generated by a Memristor-Based Chaotic System and Julia Fractal," Chaos, Solitons & Fractals, Elsevier, vol. 146(C).
    14. Luo, Mengzhuo & Cheng, Jun & Liu, Xinzhi & Zhong, Shouming, 2019. "An extended synchronization analysis for memristor-based coupled neural networks via aperiodically intermittent control," Applied Mathematics and Computation, Elsevier, vol. 344, pages 163-182.
    15. Liu, Shuxin & Yu, Yongguang & Zhang, Shuo & Zhang, Yuting, 2018. "Robust stability of fractional-order memristor-based Hopfield neural networks with parameter disturbances," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 845-854.
    16. Chen, Qun & Li, Bo & Yin, Wei & Jiang, Xiaowei & Chen, Xiangyong, 2023. "Bifurcation, chaos and fixed-time synchronization of memristor cellular neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 171(C).
    17. Stavrinides, Stavros G. & Hanias, Michael P. & Gonzalez, Mireia B. & Campabadal, Francesca & Contoyiannis, Yiannis & Potirakis, Stelios M. & Al Chawa, Mohamad Moner & de Benito, Carol & Tetzlaff, Rona, 2022. "On the chaotic nature of random telegraph noise in unipolar RRAM memristor devices," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    18. Li, Liangchen & Xu, Rui & Lin, Jiazhe, 2020. "Lagrange stability for uncertain memristive neural networks with Lévy noise and leakage delay," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    19. Rohit Abraham John & Yiğit Demirağ & Yevhen Shynkarenko & Yuliia Berezovska & Natacha Ohannessian & Melika Payvand & Peng Zeng & Maryna I. Bodnarchuk & Frank Krumeich & Gökhan Kara & Ivan Shorubalko &, 2022. "Reconfigurable halide perovskite nanocrystal memristors for neuromorphic computing," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    20. Sakthivel, R. & Anbuvithya, R. & Mathiyalagan, K. & Ma, Yong-Ki & Prakash, P., 2016. "Reliable anti-synchronization conditions for BAM memristive neural networks with different memductance functions," Applied Mathematics and Computation, Elsevier, vol. 275(C), pages 213-228.

    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:eee:apmaco:v:432:y:2022:i:c:s0096300322004404. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/applied-mathematics-and-computation .

    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.