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An electronic implementation for Morris–Lecar neuron model

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Abstract

In this paper, the threshold dynamics of Morris–Lecar neuron model is firstly analyzed by bifurcation diagram of interspike interval as a function of external forcing current, and then the discharge series, phase portraits and nullclines of the neuron under different conditions are investigated in a numerical way. The results show that the electrical activities, such as quiescent state, spiking and bursting, can be observed when the values of external forcing current beyond certain thresholds. Finally, based on the 2-D nonlinear differential equations of Morris–Lecar neuron model, a complete electronic implementation of this model is proposed and studied in detail. At the same time, a circuitry realization of the hyperbolic cosine function \(\tau _W (V)\) in the Morris–Lecar neuron model is put forward and described carefully. The outputs of designed circuits are consistent well with the theoretical predictions, which validate the design methods. Moreover, the circuit presented in this paper can be used as an experimental unit to investigate the dynamics of a single neuron or collective behaviors of a large-scale neural network.

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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. Lond. 117(4), 500–544 (1952)

    Article  Google Scholar 

  2. Fitzhugh, R.: Impulses and physiological states in theoretical models of nerve membrane. Biophys. J. 1(6), 445 (1961)

    Article  Google Scholar 

  3. Nagumo, J., Arimoto, S., Yoshizawa, S.: Active pulse transmission line simulating nerve axon. Proc. Inst. Radio Eng. 50(10), 2061 (1962)

    Google Scholar 

  4. Rinzel, J.: Repetitive activity and hopf bifurcation under point-stimulation for a simple Fitzhugh-Nagumo nerve-conduction model. J. Math. Biol. 5(4), 363–382 (1978)

    MathSciNet  MATH  Google Scholar 

  5. Nagumo, J., Sato, S.: Response characteristic of a mathematical neuron model. Kybernetik 10(3), 155 (1972)

    Article  MATH  Google Scholar 

  6. Wilson, H.R., Cowan, J.D.: Excitatory and inhibitory interactions in localized populations of model neurons. Biophys. J. 12(1), 1 (1972)

    Article  Google Scholar 

  7. 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 

  8. Shilnikov, A., Kolomiets, M.: Methods of the qualitative theory for the Hindmarsh–Rose model: a case study. A tutorial. Int. J. Bifurcat. Chaos 18(8), 2141–2168 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  9. Storace, M., Linaro, D., De Lange, E.: The Hindmarsh-Rose neuron model: bifurcation analysis and piecewise-linear approximations. Chaos. 18(3), 033128 (2008)

    Article  MathSciNet  Google Scholar 

  10. 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 

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

    Article  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  14. Shi, M., Wang, Z.: Abundant bursting patterns of a fractional-order Morris–Lecar neuron model. Commun. Nonlinear Sci. Numer. Simul. 19(6), 1956–1969 (2014)

    Article  MathSciNet  Google Scholar 

  15. González-Miranda, J.M.: Pacemaker dynamics in the full Morris–Lecar model. Commun. Nonlinear Sci. Numer. Simul. 19(9), 3229–3241 (2014)

    Article  MathSciNet  Google Scholar 

  16. Wang, H., Wang, L., Yu, L., Chen, Y.: Response of Morris–Lecar neurons to various stimuli. Phys. Rev. E Stat. Nonlinear Soft Matter Phys. 83(2 Pt 1), 021915 (2011)

    Article  Google Scholar 

  17. Wang, F.Q., Liu, C.X.: Study on the critical chaotic system with fractional order and circuit experiment. Acta Phys. Sin. Ed. 55(8), 3922–3927 (2006)

    Google Scholar 

  18. Wang, F.Q., Liu, C.X., Lu, J.-J.: Emulation of multi-scroll chaotic attractors in four-dimensional systems. Acta Phys. Sin. Ed. 55(7), 3289–3294 (2006)

    Google Scholar 

  19. Duan, S., Liao, X.: An electronic implementation for Liao’s chaotic delayed neuron model with non-monotonous activation function. Phys. Lett. A 369(1–2), 37–43 (2007)

    Article  Google Scholar 

  20. Conti, M., Turchetti, C.: Approximate identity neural networks for analog synthesis of nonlinear dynamical-systems. IEEE Trans. Circuits Syst. I-Fundam. Theory Appl. 41(12), 841–858 (1994)

    Article  MATH  Google Scholar 

  21. Nouri, M., Karimi, G.R., Ahmadi, A., Abbott, D.: Digital multiplierless implementation of the biological Fitzhugh–Nagumo model. Neurocomputing 165, 468–476 (2015)

    Article  Google Scholar 

  22. Cosp, J., Binczak, S., Madrenas, J., Fernandez, D.: Realistic model of compact VLSI Fitzhugh–Nagumo oscillators. Int. J. Electron. 101(2), 220–230 (2014)

    Article  Google Scholar 

  23. Dana, S.K., Sengupta, D.C., Hu, C.K.: Spiking and bursting in Josephson junction. IEEE Trans. Circuits Syst. II-Express Br. 53(10), 1031–1034 (2006)

    Article  Google Scholar 

  24. Jun, M., Long, H., Zhen-Bo, X., Wang, C.: Simulated test of electric activity of neurons by using Josephson junction based on synchronization scheme. Commun. Nonlinear Sci. Numer. Simul. 17(6), 2659–2669 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  25. Li, F., Liu, Q., Guo, H., Zhao, Y.: Simulating the electric activity of Fitzhugh–Nagumo neuron by using Josephson junction model. Nonlinear Dyn. 69(4), 2169–2179 (2012)

    Article  MathSciNet  Google Scholar 

  26. Ren, G., Tang, J., Ma, J., Xu, Y.: Detection of noise effect on coupled neuronal circuits. Commun. Nonlinear Sci. Numer. Simul. 29(1–3), 170–178 (2015)

    Article  MathSciNet  Google Scholar 

  27. Dahasert, N., Ztürk, İ., 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 

  28. Lee, Y.J., Lee, J., Kim, K.K., Kim, Y.-B.: Low power CMOS electronic central pattern generator design for a biomimetic underwater robot. Neurocomputing 71(1–3), 284–296 (2007)

    Article  Google Scholar 

  29. Bin, L., Yibin, L., Xuewen, R.: Gait generation and transitions of quadruped robot based on Wilson-Cowan weakly neural networks. 2010 IEEE Int. Conf. Robot. Biomim. (ROBIO). 19–24 (2010)

  30. Wu, X., Ma, J., Yuan, L., Liu, Y.: Simulating electric activities of neurons by using PSPICE. Nonlinear Dyn. 75(1–2), 113–126 (2013)

    MathSciNet  Google Scholar 

  31. Wagemakers, A., Sanjun, M.A.F., Casado, J.M., Aihara, K.: Building electronic bursters with the Morris–Lecar neuron model. Int. J. Bifurc. Chaos 16(12), 3617–3630 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  32. Behdad, R., Binczak, S., Dmitrichev, A.S., Nekorkin, V.I.: Artificial electrical Morris–Lecar neuron. IEEE Trans. Neural Netw. Learn. Syst. 26(9), 1875–1884 (2015)

    Article  MathSciNet  Google Scholar 

  33. http://en.wikipedia.org/wiki/PSpice

  34. Ozkurt, N., Savaci, F.A., Gunduzalp, M.: The circuit implementation of a wavelet function approximator. Analog Integr. Circuit Signal Process. 32(2), 171–175 (2002)

    Article  Google Scholar 

  35. Riehle, A., Grun, S., Diesmann, M., Aertsen, A.: Spike synchronization and rate modulation differentially involved in motor cortical function. Science 278(5345), 1950–1953 (1997)

    Article  Google Scholar 

  36. Liu, C., Wang, J., Chen, Y.-Y., Deng, B.: Closed-loop control of the thalamocortical relay neuron’s Parkinsonian state based on slow variable. Int. J. Neural Syst. 23(4), 1350017 (2013)

    Article  Google Scholar 

  37. Su, F., Wang, J., Deng, B., Wei, X.-L.: Adaptive control of Parkinson’s state based on a nonlinear computational model with unknown parameters. Int. J. Neural Syst. 25(1), 1450030 (2015)

    Article  MathSciNet  Google Scholar 

  38. Traub, R.D., Wong, R.K.S.: Cellular mechanism of neuronal synchronization in epilepsy. Science 216(4547), 745–747 (1982)

    Article  Google Scholar 

  39. Tsakiridou, E., Bertollini, L., Decurtis, M., Avanzini, G.: Selective increase in t-type calcium conductance of reticular thalamic neurons in a rat model of absence epilepsy. J. Neurosci. 15(4), 3110–3117 (1995)

    Google Scholar 

Download references

Acknowledgments

The authors thank the anonymous referees for their very constructive and helpful suggestions. This work is partially supported by the National Nature Science Foundation of China under the Grant Nos. 51177117 and 51307130.

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Correspondence to Xiaoyu Hu.

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Hu, X., Liu, C., Liu, L. et al. An electronic implementation for Morris–Lecar neuron model. Nonlinear Dyn 84, 2317–2332 (2016). https://doi.org/10.1007/s11071-016-2647-y

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