Skip to main content
Log in

Firing activities analysis of a novel small heterogeneous coupled network through a memristive synapse

  • Original Paper
  • Published:
Nonlinear Dynamics Aims and scope Submit manuscript

Abstract

The heterogeneous coupled network has shown its research values in brain science and bionics. In this paper, a novel small heterogeneous coupled network through a memristive synapse is proposed and investigated. The model is constructed by coupling a 2D Hindmarsh–Rose (HR) neuron with a 3D Hopfield neural network (HNN) through a memristive synapse. Subsequently, the complex dynamical behaviors exhibited by the network were investigated. Specifically, we varied the coupling parameter k12 of the 3D HNN and found that the membrane potentials of the 2D HR neuron possess seven stable firing patterns. Then, by varying the coupling parameter k1 between the 2D HR neuron and the 3D HNN, the membrane potentials of the 2D HR neuron generated five stable firing patterns. Furthermore, by fixing the coupling parameters and varying the initial values of the network, multistability behavior was exhibited by the network. These findings have potential applications in brain science and bionics. Finally, a circuit is designed to realize the network in both simulation and experiments.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

Similar content being viewed by others

Data availability

The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. Hodgkin, A.L., Huxley, A.F.: A quantitative description of membrane current and its application to conduction and excitation in nerve. J. Physiol. 117(4), 500 (1952)

    Article  Google Scholar 

  2. Tsumoto, K., Kitajima, H., Yoshinaga, T., et al.: Bifurcations in Morris-Lecar neuron model. Neurocomputing 69(4–6), 293–316 (2006)

    Article  Google Scholar 

  3. Hindmarsh, J.L., Rose, R.M.: A model of the nerve impulse using two first-order differential equations. Nature 296(5853), 162–164 (1982)

    Article  Google Scholar 

  4. Hindmarsh, J.L., Rose, R.M.: A model of neuronal bursting using three coupled first order differential equations. Proc. R. Soc. Lond. Series B Biol. Sci. 221(1222), 87–102 (1984)

    Google Scholar 

  5. Izhikevich, E.M., FitzHugh, R.: Fitzhugh-nagumo model. Scholarpedia. 1(9), 1349 (2006)

    Article  Google Scholar 

  6. Pham, V.T., Volos, C., Jafari, S., et al.: Coexistence of hidden chaotic attractors in a novel no-equilibrium system. Nonlinear Dyn. 87(3), 2001–2010 (2017)

    Article  Google Scholar 

  7. Cang, S., Li, Y., Zhang, R., et al.: Hidden and self-excited coexisting attractors in a Lorenz-like system with two equilibrium points. Nonlinear Dyn. 95(1), 381–390 (2019)

    Article  MATH  Google Scholar 

  8. Pisarchik, A.N., Feudel, U.: Control of multistability. Phys. Rep. 540(4), 167–218 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  9. Parastesh, F., Jafari, S., Azarnoush, H.: Traveling patterns in a network of memristor-based oscillators with extreme multistability. Eur. Phys. J. Spec. Top. 228(10), 2123–2131 (2019)

    Article  Google Scholar 

  10. Xu, Y., Jia, Y., Ma, J., et al.: Synchronization between neurons coupled by memristor. Chaos Solitons Fractals 104, 435–442 (2017)

    Article  Google Scholar 

  11. Lu, L., Jia, Y., Liu, W., et al.: Mixed stimulus-induced mode selection in neural activity driven by high and low frequency current under electromagnetic radiation. Complexity (2017). https://doi.org/10.1155/2017/7628537

    Article  MathSciNet  MATH  Google Scholar 

  12. Ge, M., Jia, Y., Xu, Y., et al.: Mode transition in electrical activities of neuron driven by high and low frequency stimulus in the presence of electromagnetic induction and radiation. Nonlinear Dyn. 91(1), 515–523 (2018)

    Article  Google Scholar 

  13. Xu, F., Zhang, J., Fang, T., et al.: Synchronous dynamics in neural system coupled with memristive synapse. Nonlinear Dyn. 92(3), 1395–1402 (2018)

    Article  Google Scholar 

  14. Zhang, G., Wang, C., Alzahrani, F., et al.: Investigation of dynamical behaviors of neurons driven by memristive synapse. Chaos Solitons Fractals 108, 15–24 (2018)

    Article  MathSciNet  Google Scholar 

  15. Zhang, S., Zheng, J., Wang, X., et al.: A novel no-equilibrium HR neuron model with hidden homogeneous extreme multistability. Chaos Solitons Fractals. 145, 110761 (2021)

    Article  MathSciNet  MATH  Google Scholar 

  16. Zhang, S., Zheng, J., Wang, X., et al.: Multi-scroll hidden attractor in memristive HR neuron model under electromagnetic radiation and its applications. Chaos Interdiscip. J. Nonlinear Sci. 31(1), 011101 (2021)

    Article  MathSciNet  MATH  Google Scholar 

  17. Li, Z., Guo, Z., Wang, M., et al.: Firing activities induced by memristive autapse in Fitzhugh-Nagumo neuron with time delay. AEU-Int. J. Electron. Commun. 142, 153995 (2021)

    Article  Google Scholar 

  18. Bao, B., Qian, H., Xu, Q., et al.: Coexisting behaviors of asymmetric attractors in hyperbolic-type memristor based Hopfield neural network. Front. Comput. Neurosci. 11, 81 (2017)

    Article  Google Scholar 

  19. Li, C., Yang, Y., Yang, X., et al.: A tristable locally active memristor and its application in Hopfield neural network. Nonlinear Dyn. 108(2), 1697–1717 (2022)

    Article  MathSciNet  Google Scholar 

  20. Bao, B., Qian, H., Wang, J., et al.: Numerical analyses and experimental validations of coexisting multiple attractors in Hopfield neural network. Nonlinear Dyn. 90(4), 2359–2369 (2017)

    Article  MathSciNet  Google Scholar 

  21. Chen, C., Bao, H., Chen, M., et al.: Non-ideal memristor synapse-coupled bi-neuron Hopfield neural network: numerical simulations and breadboard experiments. AEU-Int. J. Electron. Commun. 111, 152894 (2019)

    Article  Google Scholar 

  22. Chen, C., Chen, J., Bao, H., et al.: Coexisting multi-stable patterns in memristor synapse-coupled Hopfield neural network with two neurons. Nonlinear Dyn. 95(4), 3385–3399 (2019)

    Article  MATH  Google Scholar 

  23. Lin, H., Wang, C., Tan, Y.: Hidden extreme multistability with hyperchaos and transient chaos in a Hopfield neural network affected by electromagnetic radiation. Nonlinear Dyn. 99(3), 2369–2386 (2020)

    Article  Google Scholar 

  24. Lin, H., Wang, C.: Influences of electromagnetic radiation distribution on chaotic dynamics of a neural network. Appl. Math. Comput. 369, 124840 (2020)

    MathSciNet  MATH  Google Scholar 

  25. Lin, H., Wang, C., Yao, W., et al.: Chaotic dynamics in a neural network with different types of external stimuli. Commun. Nonlinear Sci. Numer. Simul. 90, 105390 (2020)

    Article  MathSciNet  MATH  Google Scholar 

  26. Li, R., Ding, R.: A novel locally active time-delay memristive Hopfield neural network and its application. Eur. Phys. J. Spec. Top. 231, 3005–3017 (2022)

    Article  Google Scholar 

  27. Shen, H., Yu, F., Kong, X., et al.: Dynamics study on the effect of memristive autapse distribution on Hopfield neural network. Chaos Interdiscip. J. Nonlinear Sci. 32(8), 083133 (2022)

    Article  MathSciNet  Google Scholar 

  28. Li, Z., Zhou, H., Wang, M., et al.: Coexisting firing patterns and phase synchronization in locally active memristor coupled neurons with HR and FN models. Nonlinear Dyn. 104(2), 1455–1473 (2021)

    Article  Google Scholar 

  29. Njitacke, Z.T., Tsafack, N., Ramakrishnan, B., et al.: Complex dynamics from heterogeneous coupling and electromagnetic effect on two neurons: application in images encryption. Chaos Solitons Fractals. 153, 111577 (2021)

    Article  MathSciNet  Google Scholar 

  30. Yu, F., Shen, H., Wang, C., et al.: Firing mechanism based on single memristive neuron and double memristive coupled neurons. Nonlinear Dyn. 110, 3807–3822 (2022)

    Article  Google Scholar 

  31. Njitacke Tabekoueng, Z., Shankar Muni, S., Fonzin Fozin, T., et al.: Coexistence of infinitely many patterns and their control in heterogeneous coupled neurons through a multistable memristive synapse. Chaos Interdiscip. J. Nonlinear Sci. 32(5), 053114 (2022)

    Article  MathSciNet  Google Scholar 

  32. Njitacke, Z.T., Awrejcewicz, J., Ramakrishnan, B., et al.: Hamiltonian energy computation and complex behavior of a small heterogeneous network of three neurons: circuit implementation. Nonlinear Dyn. 107(3), 2867–2886 (2022)

    Article  Google Scholar 

  33. Uzuntarla, M.: Firing dynamics in hybrid coupled populations of bistable neurons. Neurocomputing 367, 328–336 (2019)

    Article  Google Scholar 

  34. Calim, A., Torres, J.J., Ozer, M., et al.: Chimera states in hybrid coupled neuron populations. Neural Netw. 126, 108–117 (2020)

    Article  Google Scholar 

  35. Palabas, T., Longtin, A., Ghosh, D., et al.: Controlling the spontaneous firing behavior of a neuron with astrocyte. Chaos Interdiscip. J. Nonlinear Sci. 32(5), 051101 (2022)

    Article  MathSciNet  Google Scholar 

  36. Ma, J.: Biophysical neurons, energy, and synapse controllability: a review. J. Zhejiang Univ. Sci. A. 24(2), 109–129 (2023)

    Article  Google Scholar 

  37. Xie, Y., Zhou, P., Ma, J.: Energy balance and synchronization via inductive-coupling in functional neural circuits. Appl. Math. Model. 113, 175–187 (2023)

    Article  MathSciNet  MATH  Google Scholar 

  38. Xie, Y., Yao, Z., Ren, G., et al.: Estimate physical reliability in Hindmarsh–Rose neuron. Phys. Lett. A 464, 128693 (2023)

    Article  MathSciNet  MATH  Google Scholar 

  39. Zhou, Y., Li, C., Li, W., Li, H., Feng, W., Qian, K.: Image encryption algorithm with circle index table scrambling and partition diffusion. Nonlinear Dyn. 103(2), 2043–2061 (2021)

    Article  Google Scholar 

  40. Li, C.L., Zhou, Y., Li, H.M., Feng, W., Du, J.R.: Image encryption scheme with bit-level scrambling and multiplication diffusion. Multimed. Tools Appl. 80, 18479–18501 (2021)

    Article  Google Scholar 

  41. Dou, G., Zhao, K., Guo, M., Mou, J. et al.: Memristor-based LSTM network for text classification. Fractals. 2340040 (2023)

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 62071411) and the Research Foundation of Education Department of Hunan Province, China (Grant No.20B567).

Funding

The authors have not disclosed any funding.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mengjiao Wang.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, M., Peng, J., Zhang, X. et al. Firing activities analysis of a novel small heterogeneous coupled network through a memristive synapse. Nonlinear Dyn 111, 15397–15415 (2023). https://doi.org/10.1007/s11071-023-08626-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11071-023-08626-0

Keywords

Navigation