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Coexisting multiple firing patterns in two adjacent neurons coupled by memristive electromagnetic induction

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Abstract

The membrane potential difference between two adjacent neurons can induce an electromagnetic induction current that behaves like a memristor synapse effect using to couple these two neurons. Based on two-dimensional (2D) Hindmarsh–Rose (HR) neuron and non-ideal threshold memristor, this paper presents a five-dimensional (5D) neuron model of two adjacent neurons coupled by memristive electromagnetic induction. With the 5D memristor-coupled neuron model, the equilibrium states and their stabilities are investigated by qualitative analyses, and the coexisting phenomena of multiple firing patterns and initials-depending bifurcation routes along with extreme events are then uncovered by numerical simulations. Due to complex stabilities of the seven equilibrium states, the attraction basin of one neuron in the 5D memristor-coupled neuron model is not only related to the coupling strength of memristor synapse but also associated with another neuron initials, leading to that the coexisting multiple firing patterns are emerged from different regions in the attraction basin and the bifurcation routes are closely dependent to the initials. Furthermore, circuit syntheses using the off-the-shelf components and breadboard experiments for the proposed 5D memristor-coupled neuron model are executed so that the coexisting multiple firing patterns and extreme events are then conformed perfectly, which have not yet been previously reported in the coupled HR neuron model.

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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, 500–544 (1952)

    Article  Google Scholar 

  2. Xu, Y., Jia, Y., Ge, M.Y., Lu, L.L., Yang, L.J., Zhan, X.: Effects of ion channel blocks on electrical activity of stochastic Hodgkin–Huxley neural network under electromagnetic induction. Neurocomputing 283, 196–204 (2018)

    Article  Google Scholar 

  3. Morris, C., Lecar, H.: Voltage oscillations in the barnacle giant muscle fiber. Biophys. J. 35(1), 193–213 (1981)

    Article  Google Scholar 

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

    Article  Google Scholar 

  5. Wu, X.Y., Ma, J., Yuan, L.H., Liu, Y.: Simulating electric activities of neurons by using PSPICE. Nonlinear Dyn. 75(1–2), 113–126 (2014)

    Article  MathSciNet  Google Scholar 

  6. Izhikevich, E.M.: Simple model of spiking neurons. IEEE Trans. Neural Netw. 14(6), 1569–1572 (2003)

    Article  MathSciNet  Google Scholar 

  7. Mineeja, K.K., Ignatius, R.P.: Spatiotemporal activities of a pulse-coupled biological neural network. Nonlinear Dyn. 92(4), 1881–1897 (2018)

    Article  Google Scholar 

  8. 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 

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

    Article  Google Scholar 

  10. Innocenti, G., Genesio, R.: On the dynamics of chaotic spiking-bursting transition in the Hindmarsh–Rose neuron. Chaos 19(2), 023124 (2009)

    Article  MathSciNet  Google Scholar 

  11. Innocenti, G., Morelli, A., Genesio, R., Torcini, A.: Dynamical phases of the Hindmarsh–Rose neuronal model: studies of the transition from bursting to spiking chaos. Chaos 17(4), 043128 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  12. Gu, H.: Biological experimental observations of an unnoticed chaos as simulated by the Hindmarsh–Rose model. PLoS ONE 8(12), e81759 (2013)

    Article  Google Scholar 

  13. Gu, H.G., Pan, B.B., Chen, G.R., Duan, L.X.: Biological experimental demonstration of bifurcations from bursting to spiking predicted by theoretical models. Nonlinear Dyn. 78(1), 391–407 (2014)

    Article  MathSciNet  Google Scholar 

  14. Chay, T.R.: Chaos in a three-variable model of an excitable cell. Physica D 16, 233–242 (1985)

    Article  MATH  Google Scholar 

  15. Chay, T.R., Fan, Y.S., Lee, S.: Bursting, spiking, chaos, fractals and universality in biological rhythms. Int. J. Bifurcat. Chaos 5, 595–635 (1995)

    Article  MATH  Google Scholar 

  16. Ngouonkadi, E.B.M., Fotsin, H.B., Fotso, P.L., Tamba, V.K., Cerdeira, H.A.: Bifurcations and multistability in the extended Hindmarsh–Rose neuronal oscillator. Chaos Solitons Fractals 85, 151–163 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  17. Wu, K.J., Luo, T.Q., Lu, H.W., Wang, Y.: Bifurcation study of neuron firing activity of the modified Hindmarsh–Rose model. Neural Comput. Appl. 27(3), 739–747 (2016)

    Article  Google Scholar 

  18. Kaslik, E.: Analysis of two- and three-dimensional fractional-order Hindmarsh–Rose type neuronal models. Fract. Calc. Appl. Anal. 20(3), 623–645 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  19. Dong, J., Zhang, G.J., Xie, Y., Yao, H., Wang, J.: Dynamic behavior analysis of fractional-order Hindmarsh–Rose neuronal model. Cogn. Neurodyn. 8(2), 167–175 (2014)

    Article  Google Scholar 

  20. Wang, H., Zheng, Y., Lu, Q.: Stability and bifurcation analysis in the coupled HR neurons with delayed synaptic connection. Nonlinear Dyn. 88(3), 2091–2100 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  21. Lakshmanan, S., Lim, C.P., Nahavandi, S., Prakash, M., Balasubramaniam, P.: Dynamical analysis of the Hindmarsh–Rose neuron with time delays. IEEE Trans. Neural Netw. Learn. 28(8), 1953–1958 (2017)

    Article  MathSciNet  Google Scholar 

  22. Thottil, S.K., Ignatius, R.P.: Nonlinear feedback coupling in Hindmarsh–Rose neurons. Nonlinear Dyn. 87(3), 1879–1899 (2017)

    Article  Google Scholar 

  23. Ma, J., Tang, J.: A review for dynamics of collective behaviors of network of neurons. Sci. China Technol. Sci 58(12), 2038–2045 (2015)

    Article  Google Scholar 

  24. Wu, J., Xu, Y., Ma, J.: Lévy noise improves the electrical activity in a neuron under electromagnetic radiation. PLoS ONE 12, e0174330 (2017)

    Article  Google Scholar 

  25. Lv, M., Wang, C., Ren, G., Ma, J., Song, X.: Model of electrical activity in a neuron under magnetic flow effect. Nonlinear Dyn. 85(3), 1479–1490 (2016)

    Article  Google Scholar 

  26. Wang, Y., Ma, J., Xu, Y., Wu, F., Zhou, P.: The electrical activity of neurons subject to electromagnetic induction and Gaussian white noise. Int. J. Bifurc. Chaos 27(2), 1750030 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  27. Wu, F.Q., Wang, C.N., Jin, W.Y., Ma, J.: Dynamical responses in a new neuron model subjected to electromagnetic induction and phase noise. Physica A 469, 81–88 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  28. Xu, Y., Jia, Y., Ma, J., Alsaedi, A., Ahmad, B.: Synchronization between neurons coupled by memristor. Chaos Soliton Fractals 104, 435–442 (2017)

    Article  Google Scholar 

  29. Ma, J., Lv, M., Zhou, P., Xu, Y., Hayat, T.: Phase synchronization between two neurons induced by coupling of electromagnetic field. Appl. Math. Comput. 307, 321–328 (2017)

    MathSciNet  Google Scholar 

  30. Ren, G.D., Xu, Y., Wang, C.N.: Synchronization behavior of coupled neuron circuits composed of memristors. Nonlinear Dyn. 88(2), 893–901 (2017)

    Article  Google Scholar 

  31. Corinto, F., Ascoli, A., Lanza, V., Gilli, M.: Memristor synaptic dynamics’ influence on synchronous behavior of two Hindmarsh–Rose neurons. In: The 2011 International Joint Conference on Neural Networks (IJCNN), pp. 2403–2408. IEEE (2011)

  32. Xu, F., Zhang, J., Fang, T., Huang, S., Wang, M.: Synchronous dynamics in neural system coupled with memristive synapse. Nonlinear Dyn. 92(3), 1395–1402 (2018)

    Article  Google Scholar 

  33. Bao, B.C., Hu, A.H., Bao, H., Xu, Q., Chen, M., Wu, H.G.: Three-dimensional memristive Hindmarsh–Rose neuron model with hidden coexisting asymmetric behaviors. Complexity 2018, 3872573 (2018)

    Google Scholar 

  34. Ge, M.Y., Jia, Y., Xu, Y., Yang, L.J.: 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 

  35. Lu, L.L., Jia, Y., Liu, W.H., Yang, L.J.: Mixed stimulus-induced mode selection in neural activity driven by high and low frequency current under electromagnetic radiation. Complexity 2017, 7628537 (2017)

    MathSciNet  MATH  Google Scholar 

  36. Lv, M., Ma, J.: Multiple modes of electrical activities in a new neuron model under electromagnetic radiation. Neurocomputing 205, 375–381 (2016)

    Article  Google Scholar 

  37. Faisal, A.A., Selen, L.P.J., Wolpert, D.M.: Noise in the nervous system. Nat. Rev. Neurosci. 9(4), 292–303 (2008)

    Article  Google Scholar 

  38. Bao, B.C., Hu, A.H., Xu, Q., Bao, H., Wu, H.G., Chen, M.: AC induced coexisting asymmetric bursters in the improved Hindmarsh–Rose model. Nonlinear Dyn. 92(1), 1695–1706 (2018)

    Article  Google Scholar 

  39. Pinto, R.D., Varona, P., Volkovskii, A.R., Szücs, A., Abarbanel, H.D., Rabinovich, M.I.: Synchronous behavior of two coupled electronic neurons. Phys. Rev. E 62(2), 2644–2656 (2000)

    Article  Google Scholar 

  40. De, L.E., Hasler, M.: Oscillations and oscillatory behavior in small neural circuits. Biol. Cybern. 95(6), 537–554 (2006)

    Article  Google Scholar 

  41. Linaro, D., Poggi, T., Storace, M.: Experimental bifurcation diagram of a circuit-implemented neuron model. Phys. Lett. A 374, 4589–4593 (2011)

    Article  Google Scholar 

  42. Dahasert, N., Öztürk, I., Kiliç, R.: Experimental realizations of the HR neuron model with programmable hardware and synchronization applications. Nonlinear Dyn. 70(4), 2343–2358 (2012)

    Article  MathSciNet  Google Scholar 

  43. Bao, B.C., Qian, H., Xu, Q., Chen, M., Wang, J., Yu, Y.J.: Coexisting behaviors of asymmetric attractors in hyperbolic-type memristor based Hopfield neural network. Front. Comput. Neurosci. 11(81), 1–14 (2017)

    Google Scholar 

  44. Saha, A., Feudel, U.: Riddled basins of attraction in systems exhibiting extreme events. Chaos 28(3), 033610 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  45. Chaudhuri, U., Prasad, A.: Complicated basins and the phenomenon of amplitude death in coupled hidden attractors. Phys. Lett. A 378(9), 713–718 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  46. Hu, X.Y., Liu, C.X., Liu, L., Ni, J.K., Li, S.L.: An electronic implementation for Morris–Lecar neuron model. Nonlinear Dyn. 84(4), 2317–2332 (2016)

    Article  MathSciNet  Google Scholar 

  47. Bao, B.C., Jiang, T., Wang, G.Y., Jin, P.P., Bao, H., Chen, M.: Two-memristor-based Chua’s hyperchaotic circuit with plane equilibrium and its extreme multistability. Nonlinear Dyn. 89(2), 1157–1171 (2017)

    Article  Google Scholar 

  48. Korn, H., Faure, P.: Is there chaos in the brain II. Experimental evidence and related models. C. R. Biol. 326(9), 787–840 (2003)

    Article  Google Scholar 

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Acknowledgements

This work was supported by the grants from the National Natural Science Foundations of China under 51777016, 61471191, and 61601062, and the Aeronautical Science Foundation of China under 20152052026.

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Correspondence to Han Bao.

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Bao, H., Liu, W. & Hu, A. Coexisting multiple firing patterns in two adjacent neurons coupled by memristive electromagnetic induction. Nonlinear Dyn 95, 43–56 (2019). https://doi.org/10.1007/s11071-018-4549-7

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