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Paradoxical reduction and the bifurcations of neuronal bursting activity modulated by positive self-feedback

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Abstract

Paradoxical enhancement rather than reduction in firing activity induced by inhibitory effect is very important for both nonlinear dynamics and neuroscience. In the present paper, to build a more comprehensive viewpoint related to excitatory effect, a paradoxical phenomenon modulated by the excitatory self-feedback of autapse is extended to bursting activity in a modified Morris–Lecar model. With increasing the strength of excitatory autapse, the bursting patterns exhibit inverse period-adding bifurcations to spiking pattern via chaos, and the burst duration, the spike number per burst, and the firing frequency of bursting decrease, which is different from the traditional view that firing frequency should be enhanced. Furthermore, the paradoxical reduction in bursting activity is well explained with the nonlinear dynamics of the fast subsystem. The burst begins from neighborhood of a fold bifurcation of equilibrium point and terminates at a saddle homoclinic (SH) bifurcation. With increasing conductance of autapse, the fold bifurcation point remains unchanged, which is due to too small autaptic current near the fold point to influence the dynamics, and the SH point shifts left, which follows from the enhanced potassium current at the minimal value of the stable limit cycle. Therefore, the range between the fold and SH point becomes narrower to shorten the burst duration to reduce spike number per burst and firing frequency. The novel example of paradoxical reduction modulated by positive self-feedback and the associated bifurcation mechanism enrich the phenomena of nonlinear science and present the potential functions of excitatory autapse in the brain neurons with bursting behavior.

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References

  1. Glass, L.: Synchronization and rhythmic processes in physiology. Nature 410(6825), 277–284 (2001)

    Google Scholar 

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

    Google Scholar 

  3. Jia, Y., Gu, H.G.: Identifying nonlinear dynamics of brain functional networks of patients with schizophrenia by sample entropy. Nonlinear Dyn. 96(4), 2327–2340 (2019)

    Google Scholar 

  4. Yao, C.G., He, Z.W., Nakano, T., Qian, Y., Shuai, J.W.: Inhibitory-autapse-enhanced signal transmission in neural networks. Nonlinear Dyn. 97(2), 1425–1437 (2019)

    Google Scholar 

  5. Izhikevich, E.M.: Neural excitability, spiking and bursting. Int. J. Bifurc. Chaos 10(6), 1171–1266 (2000)

    MathSciNet  MATH  Google Scholar 

  6. Izhikevich, E.M.: Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting. MIT Press, Cambridge, MA (2007)

    Google Scholar 

  7. Feudel, U., Neiman, A., Pei, X., Wojtenek, W., Braun, H., Huber, M., Moss, F.: Homoclinic bifurcation in a Hodgkin-Huxley model of thermally sensitive neurons. Chaos 10(1), 231–239 (2000)

    MathSciNet  MATH  Google Scholar 

  8. 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)

    MathSciNet  Google Scholar 

  9. Jia, B., Gu, H.G., Xue, L.: A basic bifurcation structure from bursting to spiking of the injured nerve fibers in a two-dimensional parameter space. Cogn. Neurodyn. 11(2), 189–200 (2017)

    Google Scholar 

  10. Lu, B., Liu, S., Liu, X.L., Jiang, X.F., Wang, X.H.: Bifurcation and spike adding transition in Chay-Keizer model. Int. J. Bifurc. Chaos 26(05), 1650090 (2016)

    MathSciNet  MATH  Google Scholar 

  11. Perkel, D.H., Schulman, J.H., Bullock, T.H.: Pacemaker neurons: effects of regularly spaced synaptic input. Science 145, 61–63 (1964)

    Google Scholar 

  12. Wu, F.Q., Gu, H.G., Li, Y.Y.: Inhibitory electromagnetic induction current induced enhancement instead of reduction of neural bursting activities. Commun. Nonlinear Sci. Numer. Simulat. 79, 104924 (2019)

    Google Scholar 

  13. Wu, F.Q., Gu, H.G.: Bifurcations of negative responses to positive feedback current mediated by memristor in neuron model with bursting patterns. Int. J. Bifurc. Chaos 30(4), 2030009 (2020)

    MathSciNet  MATH  Google Scholar 

  14. Braun, H.A., Wissing, H., Schäfer, K.: Oscillation and noise determine signal transduction in shark multimodal sensory cells. Nature 367, 270–273 (1994)

    Google Scholar 

  15. Gu, H.G., Pan, B.B.: Identification of neural firing patterns, frequency and temporal coding mechanisms in individual aortic baroreceptors. Front. Comput. Neurosci. 9, 108 (2015)

    Google Scholar 

  16. Jia, B., Gu, H.G.: Dynamics and physiological roles of stochastic neural firing patterns near bifurcation points. Int. J. Bifurc. Chaos 27(7), 1750113 (2017)

    MATH  Google Scholar 

  17. Wang, X.J., Rinzel, J.: Alternating and synchronous rhythms in reciprocally inhibitory model neurons. Neural. Comput. 4(1), 84–97 (1992)

    Google Scholar 

  18. Wang, X.J., Rinzel, J.: Spindle rhythmicity in the reticularis thalami nucleus: synchronization among mutually inhibitory neurons. Neuroscience 53(4), 899–904 (1993)

    Google Scholar 

  19. Jia, B., Wu, Y., He, D., Guo, B., Xue, L.: Dynamics of transitions from anti-phase to multiple in-phase synchronizations in inhibitory coupled bursting neurons. Nonlinear Dyn. 93(3), 1599–1618 (2018)

    Google Scholar 

  20. Jalil, S., Belykh, I., Shilnikov, A.: Fast reciprocal inhibition can synchronize bursting neurons. Phys. Rev. E 81, 045201 (2010)

    MathSciNet  Google Scholar 

  21. Gu, H.G., Zhao, Z.G.: Dynamics of time delay-induced multiple synchronous behaviors in inhibitory coupled neurons. PLoS One 10(9), e0138593 (2015)

    Google Scholar 

  22. Elson, R.C., Selverston, A.I., Abarbanel, H.D.I., Rabinovich, M.I.: Inhibitory synchronization of bursting in biological neurons: dependence on synaptic time constant. J. Neurophysiol. 88(3), 1166–1176 (2002)

    Google Scholar 

  23. Belykh, I., Shilnikov, A.: When weak inhibition synchronizes strongly desynchronizing networks of bursting neurons. Phys. Rev. Lett. 101(7), 078102 (2008)

    Google Scholar 

  24. Satterlie, R.A.: Reciprocal inhibition and post inhibitory rebound produce reverberation in a locomotor pattern generator. Science 229, 402–404 (1985)

    Google Scholar 

  25. Zhao, Z.G., Li, L., Gu, H.G.: Dynamical mechanism of hyperpolarization-activated non-specific cation current induced resonance and spike-timing precision in a neuronal model. Front. Cell. Neurosci. 12, 62 (2018)

    Google Scholar 

  26. Guan, L.N., Jia, B., Gu, H.G.: A novel threshold across which negative stimulation evokes action potential near a saddle-node bifurcation in a neuronal model with \(I_h\) current. Int. J. Bifurc. Chaos 19(14), 1950198 (2019)

    MATH  Google Scholar 

  27. Dodla, R., Rinzel, J.: Enhanced neuronal response induced by fast inhibition. Phys. Rev. E 73(1), 010903 (2006)

    Google Scholar 

  28. Dodla, R., Svirskis, G., Rinzel, J.: Well-timed, brief inhibition can promote spiking: post inhibitory facilitation. J. Neurophysiol. 95(4), 2664–2677 (2006)

    Google Scholar 

  29. Beiderbeck, B., Myoga, M.H., Müller, N., Callan, A.R., Friauf, E., Grothe, B., Pecka, M.: Precisely timed inhibition facilitates action potential firing for spatial coding in the auditory brainstem. Nat. Commun. 9(1), 1771 (2018)

    Google Scholar 

  30. Van Der Loos, H., Glaser, E.M.: Autapses in neocortex cerebri: synapses between a pyramidal cells axon and its own dendrites. Brain Res. 48, 355–360 (1972)

    Google Scholar 

  31. Tikidji-Hamburyan, R.A., Martinez, J.J., White, J.A., Canavier, C.C.: Resonant interneurons can increase robustness of gamma oscillations. J. Neurosci. 35(47), 15682–15695 (2015)

    Google Scholar 

  32. Ding, X.L., Li, Y.Y.: Period-adding bifurcation of neural firings induced by inhibitory autapses with time-delay. Acta. Phys. Sin. 65(21), 210502 (2016). (In chinese)

    Google Scholar 

  33. Zhao, Z.G., Li, L., Gu, H.G., Gao, Y.: Different dynamics of repetitive neural spiking induced by inhibitory and excitatory autapses near subcritical Hopf bifurcation. Nonlinear Dyn. 99(2), 1129–1154 (2020)

    Google Scholar 

  34. Li, Y.Y., Gu, H.G., Ding, X.L.: Bifurcations of enhanced neuronal bursting activities induced by the negative current mediated by inhibitory autapse. Nonlinear Dyn. 97(4), 2091–2105 (2019)

    Google Scholar 

  35. Ding, X.L., Jia, B., Li, Y.Y.: Explanation to negative feedback induced-enhancement of neural electronic activities with phase response curve. Acta. Phys. Sin. 68(18), 180502 (2019). (in chinese)

    Google Scholar 

  36. Cobb, S.R., Halasy, K., Vida, I., NyiRi, G., Tamás, G., Buhl, E.H., Somogyi, P.: Synaptic effects of identified inter neurons innervating both interneurons and pyramidal cells in the rat hippocampus. Neuroscience 79, 629–648 (1997)

    Google Scholar 

  37. Tamás, G., Buhl, E.H., Somogyi, P.: Massive autaptic selfinnervation of GABAergic neurons in cat visual cortex. J. Neurosci. 17, 6352–6364 (1997)

    Google Scholar 

  38. Pouzat, C., Marty, A.: Autaptic inhibitory currents recorded from interneurons in rat cerebellar slices. J. Physiol. 509, 777–783 (1998)

    Google Scholar 

  39. Bacci, A., Huguenard, J.R., Prince, D.A.: Functional autaptic neurotransmission in fast-spiking interneurons: a novel form of feedback inhibition in the neocortex. J. Neurosci. 23, 859–866 (2003)

    Google Scholar 

  40. Bacci, A., Huguenard, J.R.: Enhancement of spike-timing precision by autaptic transmission in neocortical inhibitory interneurons. Neuron 49(1), 119–130 (2006)

    Google Scholar 

  41. Saada, R., Miller, N., Hurwitz, I., Susswein, A.J.: Autaptic excitation elicits persistent activity and a plateau potential in a neuron of known behavioral function. Curr. Biol. 19, 479–684 (2009)

    Google Scholar 

  42. Jiang, M., Zhu, J., Liu, Y.P., Yang, M.P., Tian, C.P., Jiang, S., Wang, Y., Guo, H., Wang, K., Shu, Y.: Enhancement of asynchronous release from fast-spiking interneuron in human and rat epileptic neocortex. PLoS Biol. 10(5), e1001324 (2012)

    Google Scholar 

  43. Yin, L.P., Zheng, R., Ke, W., He, Q.S., Zhang, Y., Li, J.L., Wang, B., Mi, Z., Long, Y.S., Rasch, M.J.: Autapses enhance bursting and coincidence detection in neocortical pyramidal cells. Nat. Commun. 9, 4890 (2018)

    Google Scholar 

  44. Li, Y.Y., Schmid, G., Hanggi, P., Schimansky-Geier, L.: Spontaneous spiking in an autaptic Hodgkin-Huxley setup. Phys. Rev. E 82(6), 061907 (2010)

    MathSciNet  Google Scholar 

  45. Hashemi, M., Valizadeh, A., Azizi, Y.: Effect of duration of synaptic activity on spike rate of a Hodgkin-Huxley neuron with delayed feedback. Phys. Rev. E 85(2), 021917 (2012)

    Google Scholar 

  46. Wang, L., Zeng, Y.J.: Control of bursting behavior in neurons by autaptic modulation. Neurol. Sci. 34, 1977–1984 (2013)

    Google Scholar 

  47. Wang, H.T., Ma, J., Chen, Y.L., Chen, Y.: Effect of an autapse on the firing pattern transition in a bursting neuron. Commun. Nonlinear Sci. Numer. Simul. 19, 3242–3254 (2014)

    MathSciNet  MATH  Google Scholar 

  48. Wang, H.T., Wang, L.F., Chen, Y.L., Chen, Y.: Effect of autaptic activity on the response of a Hodgkin-Huxley neuron. Chaos 24, 033122 (2014)

    MathSciNet  Google Scholar 

  49. Yilmaz, E., Baysal, V., Ozer, M., Perc, M.: Autaptic pacemaker mediated propagation of weak rhythmic activity across small-world neuronal networks. Physica A 444, 538–546 (2016)

    MathSciNet  MATH  Google Scholar 

  50. Guo, D.Q., Wu, S.D., Chen, M.M., Perc, M., Zhang, Y.S., Ma, J.L., Cui, Y., Xu, P., Xia, Y., Yao, D.Z.: Regulation of irregular neuronal firing by autaptic transmission. Sci. Rep. 6, 26096 (2016)

    Google Scholar 

  51. Guo, D.Q., Chen, M.M., Perc, M., Wu, S.D., Xia, C., Zhang, Y.S., Xu, P., Xia, Y., Yao, D.Z.: Firing regulation of fast spiking interneurons by autaptic inhibition. Europhys. Lett. 114(3), 30001 (2016)

    Google Scholar 

  52. Uzun, R.: Influences of autapse and channel blockage on multiple coherence resonance in a single neuron. Appl. Math. Comput. 315, 203–210 (2017)

    MathSciNet  MATH  Google Scholar 

  53. Song, X.L., Wang, H.T., Chen, Y.: Coherence resonance in an autaptic Hodgkin-Huxley neuron with time delay. Nonlinear Dyn. 94(1), 141–150 (2018)

    Google Scholar 

  54. Yao, Y.G., Ma, J.: Signal transmission by autapse with constant or time-periodic coupling intensity in the FitzHugh-Nagumo neuron. Eur. Phys. J. Spec. Top. 227(7–9), 757–766 (2018)

    Google Scholar 

  55. Song, X.L., Wang, H.T., Chen, Y.: Autapse-induced firing patterns transitions in the Morris-Lecar neuron model. Nonlinear Dyn. 96(4), 2341–2350 (2019)

    Google Scholar 

  56. Qin, H.X., Ma, J., Jin, W.Y., Wang, C.N.: Dynamics of electric activities in neuron and neurons of network induced by autapses. Sci. China Technol. Sci. 57(5), 936–946 (2014)

    Google Scholar 

  57. Qin, H.X., Wu, Y., Wang, C.N., Ma, J.: Emitting waves from defects in network with autapses. Commun. Nonlinear Sci. Numer. Simul. 23(1–3), 164–174 (2015)

    MathSciNet  MATH  Google Scholar 

  58. Ma, J., Song, X.L., Tang, J., Wang, C.N.: Wave emitting and propagation induced by autapse in a forward feedback neuronal network. Neurocomputing 167, 378–389 (2015)

    Google Scholar 

  59. Yang, X.L., Yu, Y.H., Sun, Z.K.: Autapse-induced multiple stochastic resonances in a modular neuronal network. Chaos 27(8), 083117 (2017)

    MathSciNet  Google Scholar 

  60. Ge, M.Y., Xu, Y., Zhang, Z.K., Peng, Y.X., Kang, W.J., Yang, L.J., Jia, Y.: Autaptic modulation-induced neuronal electrical activities and wave propagation on network under electromagnetic induction. Eur. Phys. J. Spec. Top. 227(7–9), 799–809 (2018)

    Google Scholar 

  61. Zhao, Z.G., Li, L., Gu, H.G.: Excitatory autapse induces different cases of reduced neuronal firing activities near Hopf bifurcation. Commun. Nonlinear Sci. Numer. Simulat. 85, 105250 (2020)

    MathSciNet  MATH  Google Scholar 

  62. Cao, B., Guan, L.N., Gu, H.G.: Bifurcation mechanism of not increase but decrease of spike number within a neural burst induced by excitatory effect. Acta. Phys. Sin. 67(24), 240502 (2018). (in chinese)

    Google Scholar 

  63. Zhao, Z.G., Li, L., Gu, H.G.: Different dynamical behaviors induced by slow excitatory feedback for type II and III excitabilities. Sci. Rep. 10, 3646 (2020)

    Google Scholar 

  64. Johnson, S.W., Seutin, V., North, R.A.: Bursting in dopamine neurons induced by N-methyl-D-aspartate: role of electrogenic sodium pump. Science 258(5082), 665–667 (1992)

    Google Scholar 

  65. Yang, Y., Cui, Y.H., Sang, K.N., Dong, Y.Y., Ni, Z.Y., Ma, S.S., Hu, H.L.: Ketamine blocks bursting in the lateral habenula to rapidly relieve depression. Nature 554, 317–322 (2018)

    Google Scholar 

  66. Wang, X.J.: Neurophysiological and computational principles of cortical rhythms in cognition. Physiol. Rev. 90, 1195–268 (2010)

    Google Scholar 

  67. Lisman, J.E.: Bursts as a unit of neural information: Making unreliable synapses reliable. Trends Neurosci. 20(1), 38–43 (1997)

    Google Scholar 

  68. González-Miranda, J.M.: Block structured dynamics and neuronal coding. Phys. Rev. E 72(5), 051922 (2005)

    MathSciNet  Google Scholar 

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

    Google Scholar 

  70. Hoppensteadt, F.C., Izhikevich, E.M.: Weakly Connected Neural Networks. Springer Press, NewYork (1997)

    MATH  Google Scholar 

  71. Ermentrout, B.: Simulating, Analyzing, and Animating Dynamical Systems. A Guide to XPPAUT for Researchers and Students. SIAM Press, Philadelphia (2002)

    MATH  Google Scholar 

  72. González-Miranda, J.M.: Observation of a continuous interior crisis in the Hindmarsh-Rose neuron model. Chaos 13, 845–852 (2003)

    MathSciNet  MATH  Google Scholar 

  73. Gu, H.G.: Experimental observation of transitions from chaotic bursting to chaotic spiking in a neural pacemaker. Chaos 23(2), 023126 (2013)

    Google Scholar 

  74. Uzuntarla, M., Torres, J.J., Calim, A., Barreto, E.: Synchronization-induced spike termination in networks of bistable neurons. Neural Netw. 110, 131–40 (2019)

    Google Scholar 

  75. Zhang, X.J., Gu, H.G., Guan, L.N.: Stochastic dynamics of conduction failure of action potential along nerve fiber with Hopf bifurcation. Sci. China Technol. Sci. 62(9), 1502–1511 (2019)

    Google Scholar 

  76. Ma, J., Tang, J.: A review for dynamics in neuron and neuronal network. Nonlinear Dyn. 89, 1569–1578 (2017)

    MathSciNet  Google Scholar 

  77. Ma, J., Yang, Zq, Yang, L.J., Tang, J.: A physical view of computational neurodynamics. J. Zhejiang Univ-Sc. A 20, 639–659 (2019)

    Google Scholar 

  78. Mondal, A., Upadhyay, R.K., Ma, J., Yadav, B.K., Sharma, K.S., Mondal, A.: Bifurcation analysis and diverse firing activities of a modified excitable neuron model. Cogn. Neurodyn. 13, 393–407 (2019)

    Google Scholar 

  79. He, Z.W., Yao, C.G.: The effect of oxygen concentration on the coupled neurons: rich spiking patterns and synchronization. China Technol. Sci, Sci (2020). https://doi.org/10.1007/s11431-020-1659-y

  80. Wang, J., Yang, X., Sun, Z.: Suppressing bursting synchronization in a modular neuronal network with synaptic plasticity. Cogn. Neurodyn. 12, 625–636 (2018)

    Google Scholar 

  81. Wang, Z., Shi, X.: Electric activities of time-delay memristive neuron disturbed by Gaussian white noise. Cogn. Neurodyn. 14(1), 115–124 (2020)

    Google Scholar 

  82. Kim, S.Y., Lim, W.: Cluster burst synchronization in a scale-free network of inhibitory bursting neurons. Cogn. Neurodyn. 14(1), 69–94 (2020)

    Google Scholar 

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This work was supported by the National Science Foundation of China (Grant Nos. 11872276 and 11572225) and Research Project of Henan Province Postdoctoral (No. 19030095).

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Wang, X., Gu, H. & Lu, B. Paradoxical reduction and the bifurcations of neuronal bursting activity modulated by positive self-feedback. Nonlinear Dyn 101, 2383–2399 (2020). https://doi.org/10.1007/s11071-020-05913-y

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