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Electrical activity and synchronization of HR-tabu neuron network coupled by Chua Corsage Memristor

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Abstract

The processing and transmission of biological neural information are jointly completed by the electromagnetic activities of neurons in different brain regions. And memristor is the appropriate candidate for mimicking synapse due to its unique memory function and synapse-like plasticity. Therefore, it is of great significance to explore the electrical behavior of heterogeneous neuron network coupled by memristor. This paper focuses on the electrical activity and synchronization of a bi-neuron network (HR-tabu neuron network) built by coupling Hindmarsh–Rose and tabu learning models with Chua Corsage Memristor (CCM). The electrical activities of HR-tabu network, such as spiking discharge and bursting discharge, are revealed under appropriate external stimuli and coupling strength. Interestingly, we find that the initial value-related state switching of HR-tabu network is associated with the equilibrium states of CCM. In addition, the synchronization behavior of HR-tabu network depending on the coupling strength, external stimuli and system parameters is investigated in detail by analyzing the phase difference and synchronization factor. It is shown that phase synchronization of HR-tabu neuron network can be achieved under small coupling strength, and that complete synchronization can be achieved when the coupling strength is large enough. The obtained results provide possible guidance for regulating the firing activity and synchronous behavior of artificial neurons, and therefore have potential applications in brain science and biomimetics.

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References

  1. Won, U.Y., An Vu, Q., Park, S.B., Park, M.H., Dam Do, V., Park, H.J., Yang, H., Lee, Y.H., Yu, W.J.: Multi-neuron connection using multi-terminal floating–gate memristor for unsupervised learning. Nat. Commun. 14, 3070 (2023)

    Google Scholar 

  2. Xu, Y., Jia, Y., Ma, J., Hayat, T., Alsaedi, A.: Collective responses in electrical activities of neurons under field coupling. Sci. Rep. 8, 1349 (2018)

    Google Scholar 

  3. Xu, C., Wang, C.H., Sun, J.R.: A memristor-based RBM circuit implementation and application in license plate image processing. Sci Sin Inform. 53, 164–177 (2023)

    Google Scholar 

  4. Ma, M., Xiong, K., Li, Z., He, S.: Dynamical behavior of memristor-coupled heterogeneous discrete neural networks with synaptic crosstalk. Chin. Phys. B (2023). https://doi.org/10.1088/1674-1056/aceee9

    Article  Google Scholar 

  5. Moffat, J.J., Ka, M., Jung, E.M., Kim, W.Y.: Genes and brain malformations associated with abnormal neuron positioning. Mol. Brain 8, 1–12 (2015)

    Google Scholar 

  6. Tan, F., Zhou, L., Lu, J., Chu, Y., Li, Y.: Fixed-time outer synchronization under double-layered multiplex networks with hybrid links and time-varying delays via delayed feedback control. Asian J. Control 24, 137–148 (2022)

    MathSciNet  Google Scholar 

  7. Ma, M.L., Xie, X.H., Yang, Y., Li, Z.J., Sun, Y.C.: Synchronization coexistence in a Rulkov neural network based on locally active discrete memristor. Chin. Phys. B 32, 058701 (2023)

    Google Scholar 

  8. Zhou, L., Tan, F., Li, X., Zhou, L.: A fixed-time synchronization-based secure communication scheme for two-layer hybrid coupled networks. Neurocomputing 433, 131–141 (2021)

    Google Scholar 

  9. Wang, S., Wei, Z.: Synchronization of coupled memristive Hindmarsh-Rose maps under different coupling conditions. AEU-Int. J. Electron. Commun. 161, 154561 (2023)

    Google Scholar 

  10. Ma, M., Lu, Y., Li, Z., Sun, Y., Wang, C.: Multistability and phase synchronization of Rulkov neurons coupled with a locally active discrete memristor. Fractal Fract. 7, 82 (2023)

    Google Scholar 

  11. He, S., Liu, J., Wang, H., Sun, K.: A discrete memristive neural network and its application for character recognition. Neurocomputing 523, 1–8 (2023)

    Google Scholar 

  12. Tian, Z.Q.K., Zhou, D.: Exponential time differencing algorithm for pulse-coupled Hodgkin–Huxley neural networks. Front. Comput. Neurosci. 14, 40 (2020)

    Google Scholar 

  13. Bashkirtseva, I., Nasyrova, V., Ryashko, L.: Noise-induced bursting and chaos in the two-dimensional Rulkov model. Chaos Solitons Fractals 110, 76–81 (2018)

    MathSciNet  MATH  Google Scholar 

  14. Wang, H., Lu, Q., Wang, Q.: Bursting and synchronization transition in the coupled modified ML neurons. Commun. Nonlinear Sci. Numer. Simul. 13, 1668–1675 (2008)

    MathSciNet  MATH  Google Scholar 

  15. Carletti, T., Nakao, H.: Turing patterns in a network-reduced FitzHugh–Nagumo model. Phys. Rev. E 101, 022203 (2020)

    MathSciNet  Google Scholar 

  16. Lu, Y., Li, H., Li, C.: Electrical activity and synchronization of memristor synapse-coupled HR network based on energy method. Neurocomputing 544, 126246 (2023)

    Google Scholar 

  17. Li, C., Yang, Y., Yang, X., Zi, X., Xiao, F.: A tristable locally active memristor and its application in Hopfield neural network. Nonlinear Dyn. 108, 1697–1717 (2022)

    Google Scholar 

  18. Ding, D., Chen, X., Yang, Z., Hu, Y., Wang, M., Niu, Y.: Dynamics of stimuli-based fractional-order memristor-coupled tabu learning two-neuron model and its engineering applications. Nonlinear Dyn. 111, 1791–1817 (2023)

    Google Scholar 

  19. Nando Tezoh, F.K., Dang Koko, A., Ekobena Fouda, H.P.: Modes of electrical activities and energy of Hindmarsh–Rose model coupled by memristive synapses. Eur. Phys. J. Plus 138, 267 (2023)

    Google Scholar 

  20. Njitacke, Z.T., Muni, S.S., Seth, S., Awrejcewicz, J., Kengne, J.: Complex dynamics of a heterogeneous network of Hindmarsh–Rose neurons. Phys. Scr. 98, 045210 (2023)

    Google Scholar 

  21. Bao, H., Hu, A., Liu, W., Bao, B.: Hidden bursting firings and bifurcation mechanisms in memristive neuron model with threshold electromagnetic induction. IEEE Trans. Neural Netw. Learn. Syst. 31, 502–511 (2019)

    Google Scholar 

  22. Li, Y., Zhou, X., Wu, Y., Zhou, M.: Hopf bifurcation analysis of a tabu learning two-neuron model. Chaos Solitons Fractals 29, 190–197 (2006)

    MathSciNet  MATH  Google Scholar 

  23. Li, Y.: Hopf bifurcation analysis in a tabu learning neuron model with two delays. ISRN Appl. Math. 2011, 1060–1065 (2011)

    MathSciNet  Google Scholar 

  24. Bao, B., Hou, L., Zhu, Y., Wu, H., Chen, M.: Bifurcation analysis and circuit implementation for a tabu learning neuron model. AEU-Int. J. Electron. Commun. 121, 153235 (2020)

    Google Scholar 

  25. Li, H., Lu, Y., Li, C.: Dynamics in stimulation-based tabu learning neuron model. AEU-Int. J. Electron. Commun. 142, 153983 (2021)

    Google Scholar 

  26. Mannan, Z.I., Adhikari, S.P., Yang, C., Budhathoki, R.K., Kim, H., Chua, L.: Memristive imitation of synaptic transmission and plasticity. IEEE Trans. Neural Netw. Learn. Syst. 30, 3458–3470 (2019)

    Google Scholar 

  27. Peng, Y., Liu, J., He, S., Sun, K.: Discrete fracmemristor-based chaotic map by Grunwald-Letnikov difference and its circuit implementation. Chaos Solitons Fractals 171, 113429 (2023)

    MathSciNet  Google Scholar 

  28. Shadizadeh, S.M., Nazarimehr, F., Jafari, S., Rajagopal, K.: Investigating different synaptic connections of the Chay neuron model. Physica A 607, 128242 (2022)

    MathSciNet  MATH  Google Scholar 

  29. Liang, Y., Wang, S., Dong, Y., Lu, Z., Wang, G.: Locally-Active Memristors-Based Reactance-Less Oscillator. IEEE Trans. Circuits Syst. II Express Briefs 70, 321–325 (2022)

    Google Scholar 

  30. Li, H., Li, C., Du, J.: Discretized locally active memristor and application in logarithmic map. Nonlinear Dyn. 111, 2895–2915 (2023)

    Google Scholar 

  31. Du, S., Zhang, Z., Li, J., Sun, C., Sun, J., Hong, Q.: Multidirectional associative memory neural network circuit based on memristor. IEEE Trans. Biomed. Circuits Syst. 17, 433–445 (2023)

    Google Scholar 

  32. Ding, D., Chen, X., Yang, Z., Hu, Y., Wang, M., Zhang, H., Zhang, X.: Coexisting multiple firing behaviors of fractional-order memristor-coupled HR neuron considering synaptic crosstalk and its ARM-based implementation. Chaos Solitons Fractals 158, 112014 (2022)

    MathSciNet  Google Scholar 

  33. Fida, A.A., Khanday, F.A., Mittal, S.: An active memristor based rate-coded spiking neural network. Neurocomputing 533, 61–71 (2023)

    Google Scholar 

  34. Sun, J., Li, C., Wang, Z., Wang, Y.: Dynamic analysis of HR-FN-HR neural network coupled by locally active hyperbolic memristors and encryption application based on Knuth-Durstenfeld algorithm. Appl. Math. Modell. 121, 463–483 (2023)

    MathSciNet  MATH  Google Scholar 

  35. Bao, B., Zhu, Y., Ma, J., Bao, H., Wu, H., Chen, M.: Memristive neuron model with an adapting synapse and its hardware experiments. Sci. China Technol. Sci. 64, 1107–1117 (2021)

    Google Scholar 

  36. Li, Z., Zhou, H., Wang, M., Ma, M.: Coexisting firing patterns and phase synchronization in locally active memristor coupled neurons with HR and FN models. Nonlinear Dyn. 104, 1455–1473 (2021)

    Google Scholar 

  37. Wu, F., Guo, Y., Ma, J.: Reproduce the biophysical function of chemical synapse by using a memristive synapse. Nonlinear Dyn. 109, 2063–2084 (2022)

    Google Scholar 

  38. Feali, M.S., Ahmadi, A.: Transient response characteristic of memristor circuits and biological-like current spikes. Neural Comput. Appl. 28, 3295–3305 (2017)

    Google Scholar 

  39. Wan, Q., Li, F., Chen, S., Yang, Q.: Symmetric multi-scroll attractors in magnetized Hopfield neural network under pulse controlled memristor and pulse current stimulation. Chaos Solitons Fractals 169, 113259 (2023)

    Google Scholar 

  40. Lohn, A.J., Mickel, P.R., Aimone, J.B., Debenedictis, E.P., Marinella, M.J.: Memristors as synapses in artificial neural networks: Biomimicry beyond weight change. Cybersecur. Syst. Hum. Cogn. Augment. 61, 135–150 (2014)

    Google Scholar 

  41. Li, H., Li, C., He, S.: Locally active memristor with variable parameters and its oscillation circuit. Int. J. Bifur. Chaos 33, 2350032 (2023)

    MathSciNet  Google Scholar 

  42. Hua, M., Zhang, Y., Chen, M., Xu, Q., Bao, B.: Memristive single-neuron model and its memristor-coupled network: homogenously coexisting attractors and parallel-offset synchronization. Int. J. Bifurc. Chaos 32, 2250225 (2022)

    MathSciNet  Google Scholar 

  43. Bao, B., Yang, Q., Zhu, D., Zhang, Y., Xu, Q., Chen, M.: Initial-induced coexisting and synchronous firing activities in memristor synapse-coupled Morris-Lecar bi-neuron network. Nonlinear Dyn. 99, 2339–2354 (2020)

    MATH  Google Scholar 

  44. Zhou, Q., Wei, D.Q.: Collective dynamics of neuronal network under synapse and field coupling. Nonlinear Dyn. 105, 753–765 (2021)

    Google Scholar 

  45. Shang, C., He, S., Rajagopal, K., Wang, H., Sun, K.: Dynamics and chimera state in a neural network with discrete memristor coupling. Eur. Phys. J. Spec. Top. 231, 4065–4076 (2022)

    Google Scholar 

  46. Wu, F., Guo, Y., Ma, J., Jin, W.: Synchronization of bursting memristive Josephson junctions via resistive and magnetic coupling. Appl. Math. Comput. 455, 128131 (2023)

    MathSciNet  MATH  Google Scholar 

  47. Peng, C., Li, Z., Wang, M., Ma, M.: Dynamics in a memristor-coupled heterogeneous neuron network under electromagnetic radiation. Res. Sq. (2023). https://doi.org/10.21203/rs.3.rs-2868552/v1

    Article  Google Scholar 

  48. Yao, Z., Zhou, P., Zhu, Z., Ma, J.: Phase synchronization between a light-dependent neuron and a thermosensitive neuron. Neurocomputing 423, 518–534 (2021)

    Google Scholar 

  49. Hindmarsh, J.L., Rose, R.M.: A model of the nerve impulse using two first-order differential equations. Nature 296, 162–164 (1982)

    Google Scholar 

  50. Mannan, Z.I., Kim, H.: Nonlinear dynamics, switching kinetics and physical realization of the family of Chua corsage memristors. Electronics 9, 369 (2020)

    Google Scholar 

  51. Kingston, S.L., Thamilmaran, K.: Bursting oscillations and mixed-mode oscillations in driven Liénard system. Int. J. Bifurc. Chaos 27, 1730025 (2017)

    MATH  Google Scholar 

  52. Ramakrishnan, B., Durdu, A., Rajagopal, K., Akgul, A.: Infinite attractors in a chaotic circuit with exponential memristor and Josephson junction resonator. AEU-Int. J. Electron. Commun. 123, 153319 (2020)

    Google Scholar 

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Funding

This work was supported by Hunan Provincial Natural Science Foundation of China (Nos. 2019JJ40109, 2020JJ4337) and National Natural Science Foundations of China (No. 62171401).

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Correspondence to Chunlai Li.

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Li, C., Wang, X., Du, J. et al. Electrical activity and synchronization of HR-tabu neuron network coupled by Chua Corsage Memristor. Nonlinear Dyn 111, 21333–21350 (2023). https://doi.org/10.1007/s11071-023-08998-3

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  • DOI: https://doi.org/10.1007/s11071-023-08998-3

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