Abstract
Inverse stochastic resonance (ISR) is a phenomenon in which the firing activity of a neuron is inhibited at a certain noise level. In this paper, the effects of potassium channel blockage on ISR in single Hodgkin-Huxley neurons and in small-world networks were investigated. For the single neuron, the ion channel noise-induced ISR phenomenon can occur only in a certain small range of potassium channel blockage ratio. Bifurcation analysis showed that this small range is the bistable region regulated by the external bias current. For small-world networks, the effect of non-homogeneous network blockage on ISR was investigated. The network blockage ratio was used to represent the proportion of potassium-channel-blocked neurons to total network neurons. It is found that an increase in network blockage ratio at small coupling strengths results in shorter ISR duration. When the coupling strength is increased, the ISR is more significant in the case of a large network blockage ratio. The ISR phenomenon is determined by the network blockage ratio, the coupling strength, and the ion channel noise. Our results will provide new perspectives on the observation of ISR in neuroscience experiments.
概要
目的
研究钾离子通道阻塞对单个Hodgkin-Huxley神经元和小世界网络中反随机共振(ISR)的影响,并分析背后的动力学机制。
创新点
1. 探究钾离子通道阻塞对反随机共振的影响;2. 在小世界网络中考虑阻塞不均匀对反随机共振的影响。
方法
1. 以放电率作为统计量,研究钾离子通道阻塞对神经元放电率的影响(图2和3);2. 通过分岔分析,探究钾 离子通道阻塞对反随机共振所产生影响背后的动力学机制(图4);3. 探究部分阻塞的神经元网络中反随 机共振对耦合强度的依赖性(图8和13);4. 通过相空间和网络中神经元的放电分布等分析不同反随机共 振曲线背后的动力学机制(图10~12)。
结论
1. 对于单个神经元,离子通道噪声引起的ISR现象只发生在较小的钾离子通道阻塞率范围内;分岔分析表明这 一小范围是受外部偏置电流影响的双稳态区域。2. 对于小世界神经元网络,ISR存在的原因是在中等强度 噪声下双稳态神经元的放电受到抑制。3. 当双稳态和单稳态神经元之间存在电位差,且耦合强度增加时, 抑制作用被抵消。4. 在较小的耦合强度下,网络阻塞比的增加会导致ISR持续时间变短;当耦合强度增加 时,ISR在网络阻塞比较大的情况下更为明显;ISR现象由网络阻塞比、耦合强度和离子通道噪声共同决 定。
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References
Buchin A, Rieubland S, Häusser M, et al., 2016. Inverse stochastic resonance in cerebellar Purkinje cells. PLoS Computational Biology, 12(8):e1005000. https://doi.org/10.1371/journal.pcbi.1005000
Chik DTW, Wang YQ, Wang ZD, 2001. Stochastic resonance in a Hodgkin-Huxley neuron in the absence of external noise. Physical Review E, 64(2):021913. https://doi.org/10.1103/PhysRevE.64.021913
Ding QM, Jia Y, 2021. Effects of temperature and ion channel blocks on propagation of action potential in myelinated axons. Chaos: An Interdisciplinary Journal of Nonlinear Science, 31(5):053102. https://doi.org/10.1063/5.0044874
Dipoppa M, Gutkin BS, 2013. Flexible frequency control of cortical oscillations enables computations required for working memory. Proceedings of the National Academy of Sciences of the United States of America, 110(31): 12828–12833. https://doi.org/10.1073/pnas.1303270110
Faisal AA, Selen LPJ, Wolpert DM, 2008. Noise in the nervous system. Nature Reviews Neuroscience, 9(4):292–303. https://doi.org/10.1038/nrn2258
Fox RF, 1997. Stochastic versions of the Hodgkin-Huxley equations. Biophysical Journal, 72(5):2068–2074. https://doi.org/10.1016/S0006-3495(97)78850-7
Gailey PC, Neiman A, Collins JJ, et al., 1997. Stochastic resonance in ensembles of nondynamical elements: the role of internal noise. Physical Review Letters, 79(23):4701–4704. https://doi.org/10.1103/PhysRevLett.79.4701
Gluckman BJ, Netoff TI, Neel EJ, et al., 1996. Stochastic resonance in a neuronal network from mammalian brain. Physical Review Letters, 77(19):4098–4101. https://doi.org/10.1103/PhysRevLett.77.4098
Goldwyn JH, Imennov NS, Famulare M, et al., 2011. Stochastic differential equation models for ion channel noise in Hodgkin–Huxley neurons. Physical Review E, 83(4): 041908. https://doi.org/10.1103/PhysRevE.83.041908
Gutkin BS, Jost J, Tuckwell HC, 2009. Inhibition of rhythmic neural spiking by noise: the occurrence of a minimum in activity with increasing noise. Naturwissenschaften, 96(9): 1091–1097. https://doi.org/10.1007/s00114-009-0570-5
Heiss JE, Katz Y, Ganmor E, et al., 2008. Shift in the balance between excitation and inhibition during sensory adaptation of S1 neurons. Journal of Neuroscience, 28(49):13320–13330. https://doi.org/10.1523/JNEUROSCI.2646-08.2008
Hodgkin AL, Huxley AF, 1952a. The dual effect of membrane potential on sodium conductance in the giant axon of Loligo. The Journal of Physiology, 116(4):497–506. https://doi.org/10.1113/jphysiol.1952.sp004719
Hodgkin AL, Huxley AF, 1952b. A quantitative description of membrane current and its application to conduction and excitation in nerve. The Journal of Physiology, 117(4): 500–544. https://doi.org/10.1113/jphysiol.1952.sp004764
Hoyt RC, Strieb JD, 1971. A stored charge model for the sodium channel. Biophysical Journal, 11(11):868–885. https://doi.org/10.1016/S0006-3495(71)86261-6
Huh JH, 2016. Inverse stochastic resonance in electroconvection by multiplicative colored noise. Physical Review E, 94(5):052702. https://doi.org/10.1103/PhysRevE.94.052702
Jin WY, Wang A, Ma J, et al., 2019. Effects of electromagnetic induction and noise on the regulation of sleep wake cycle. Science China Technological Sciences, 62(12):2113–2119. https://doi.org/10.1007/s11431-018-9423-x
Kawaguchi M, Mino H, Durand DM, 2011. Stochastic resonance can enhance information transmission in neural networks. IEEE Transactions on Biomedical Engineering, 58(7):1950–1958. https://doi.org/10.1109/TBME.2011.2126571
Kosko B, Mitaim S, 2003. Stochastic resonance in noisy threshold neurons. Neural Networks, 16(5–6):755–761. https://doi.org/10.1016/S0893-6080(03)00128-X
Li DX, Cui XW, Yang YC, 2018. Inverse stochastic resonance induced by non-Gaussian colored noise. Neurocomputing, 287:52–57. https://doi.org/10.1016/j.neucom.2018.01.078
Li DX, Song SL, Zhang N, 2020. Lévy noise-induced inverse stochastic resonance on Newman–Watts networks of Hodgkin–Huxley neurons. International Journal of Modern Physics B, 34(19):2050185. https://doi.org/10.1142/S0217979220501854
Liu SB, Wu Y, Li JJ, et al., 2013. The dynamic behavior of spiral waves in stochastic Hodgkin–Huxley neuronal networks with ion channel blocks. Nonlinear Dynamics, 73(1–2):1055–1063. https://doi.org/10.1007/s11071-013-0852-5
Liu Y, Li CG, 2013. Stochastic resonance in feedforward-loop neuronal network motifs in astrocyte field. Journal of Theoretical Biology, 335:265–275. https://doi.org/10.1016/j.jtbi.2013.07.007
Liu Y, Ma J, Xu Y, et al., 2019. Electrical mode transition of hybrid neuronal model induced by external stimulus and electromagnetic induction. International Journal of Bifurcation and Chaos, 29(11):1950156. https://doi.org/10.1142/S0218127419501566
Lu LL, Jia Y, Liu WH, et al., 2017. Mixed stimulus-induced mode selection in neural activity driven by high and low frequency current under electromagnetic radiation. Complexity, 2017:7628537. https://doi.org/10.1155/2017/7628537
Lu LL, Jia Y, Ge MY, et al., 2020. Inverse stochastic resonance in Hodgkin–Huxley neural system driven by Gaussian and non-Gaussian colored noises. Nonlinear Dynamics, 100(1):877–889. https://doi.org/10.1007/s11071-020-05492-y
Ma J, 2023. Biophysical neurons, energy, and synapse controllability: a review. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 24(2): 109–129. https://doi.org/10.1631/jzus.A2200469
Ma J, Wu Y, Ying HP, et al., 2011. Channel noise-induced phase transition of spiral wave in networks of Hodgkin-Huxley neurons. Chinese Science Bulletin, 56(2): 151–157. https://doi.org/10.1007/s11434-010-4281-2
Ma J, Yang ZQ, Yang LJ, et al., 2019. A physical view of computational neurodynamics. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 20(9):639–659. https://doi.org/10.1631/jzus.A1900273
Maisel B, Lindenberg K, 2020. Channel noise effects on neural synchronization. Physica A: Statistical Mechanics and Its Applications, 552:123186. https://doi.org/10.1016/j.physa.2019.123186
McDonnell MD, Abbott D, 2009. What is stochastic resonance? Definitions, misconceptions, debates, and its relevance to biology. PLoS Computational Biology, 5(5):e1000348. https://doi.org/10.1371/journal.pcbi.1000348
Narahashi T, Moore JW, 1968. Neuroactive agents and nerve membrane conductances. Journal of General Physiology, 51(5):93–101. https://doi.org/10.1085/jgp.51.5.93
Paydarfar D, Forger DB, Clay JR, 2006. Noisy inputs and the induction of on–off switching behavior in a neuronal pacemaker. Journal of Neurophysiology, 96(6):3338–3348. https://doi.org/10.1152/jn.00486.2006
Perc M, 2007. Stochastic resonance on excitable small-world networks via a pacemaker. Physical Review E, 76(6):066203. https://doi.org/10.1103/PhysRevE.76.066203
Schmerl BA, McDonnell MD, 2013. Channel-noise-induced stochastic facilitation in an auditory brainstem neuron model. Physical Review E, 88(5):052722. https://doi.org/10.1103/PhysRevE.88.052722
Schmid G, Goychuk I, Hänggi P, 2004. Effect of channel block on the spiking activity of excitable membranes in a stochastic Hodgkin–Huxley model. Physical Biology, 1(2): 61–66. https://doi.org/10.1088/1478-3967/1/2/002
Shu YS, Hasenstaub A, McCormick DA, 2003. Turning on and off recurrent balanced cortical activity. Nature, 423(6937): 288–293. https://doi.org/10.1038/nature01616
Tuckwell HC, Jost J, Gutkin BS, 2009. Inhibition and modulation of rhythmic neuronal spiking by noise. Physical Review E, 80(3):031907. https://doi.org/10.1103/PhysRevE.80.031907
Uzun R, Yilmaz E, Ozer M, 2017. Effects of autapse and ion channel block on the collective firing activity of Newman–Watts small-world neuronal networks. Physica A: Statistical Mechanics and Its Applications, 486:386–396. https://doi.org/10.1016/j.physa.2017.05.049
Uzuntarla M, 2013. Inverse stochastic resonance induced by synaptic background activity with unreliable synapses. Physics Letters A, 377(38):2585–2589. https://doi.org/10.1016/j.physleta.2013.08.009
Uzuntarla M, Cressman JR, Ozer M, et al., 2013. Dynamical structure underlying inverse stochastic resonance and its implications. Physical Review E, 88(4):042712. https://doi.org/10.1103/PhysRevE.88.042712
Uzuntarla M, Torres JJ, So P, et al., 2017a. Double inverse stochastic resonance with dynamic synapses. Physical Review E, 95(1):012404. https://doi.org/10.1103/PhysRevE.95.012404
Uzuntarla M, Barreto E, Torres JJ, 2017b. Inverse stochastic resonance in networks of spiking neurons. PLoS Computational Biology, 13(7):e1005646. https://doi.org/10.1371/journal.pcbi.1005646
Wang GW, Wu Y, Xiao FL, et al., 2022. Non-Gaussian noise and autapse-induced inverse stochastic resonance in bistable Izhikevich neural system under electromagnetic induction. Physica A: Statistical Mechanics and Its Applications, 598: 127274. https://doi.org/10.1016/j.physa.2022.127274
Wang QY, Perc M, Duan ZS, et al., 2009. Delay-induced multiple stochastic resonances on scale-free neuronal networks. Chaos: An Interdisciplinary Journal of Nonlinear Science, 19(2):023112. https://doi.org/10.1063/1.3133126
Watts DJ, Strogatz SH, 1998. Collective dynamics of ‘small-world’ networks. Nature, 393(6684):440–442. https://doi.org/10.1038/30918
White JA, Rubinstein JT, Kay AR, 2000. Channel noise in neurons. Trends in Neurosciences, 23(3): 131–137. https://doi.org/10.1016/S0166-2236(99)01521-0
Wu FQ, Ma J, Ren GD, 2018. Synchronization stability between initial-dependent oscillators with periodical and chaotic oscillation. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 19(12):889–903. https://doi.org/10.1631/jzus.A1800334
Wu XY, Ma J, Li F, et al., 2013a. Development of spiral wave in a regular network of excitatory neurons due to stochastic poisoning of ion channels. Communications in Nonlinear Science and Numerical Simulation, 18(12):3350–3364. https://doi.org/10.1016/j.cnsns.2013.05.011
Wu XY, Ma J, Xie ZB, 2013b. Effect of inhomogeneous distribution of ion channels on collective electric activities of neurons in a ring network. Acta Physica Sinica, 62(24): 240507 (in Chinese). https://doi.org/10.7498/aps.62.240507
Xu Y, Jia Y, Ge MY, et al., 2018. Effects of ion channel blocks on electrical activity of stochastic Hodgkin–Huxley neural network under electromagnetic induction. Neurocomputing, 283:196–204. https://doi.org/10.1016/j.neucom.2017.12.036
Xu Y, Jia Y, Wang HW, et al., 2019. Spiking activities in chain neural network driven by channel noise with field coupling. Nonlinear Dynamics, 95(4):3237–3247. https://doi.org/10.1007/s11071-018-04752-2
Yu D, Lu LL, Wang GW, et al., 2021. Synchronization mode transition induced by bounded noise in multiple time-delays coupled FitzHugh–Nagumo model. Chaos, Solitons & Fractals, 147:111000. https://doi.org/10.1016/j.chaos.2021.111000
Yu D, Wang GW, Ding QM, et al., 2022a. Effects of bounded noise and time delay on signal transmission in excitable neural networks. Chaos, Solitons & Fractals, 157:111929. https://doi.org/10.1016/j.chaos.2022.111929
Yu D, Zhou XY, Wang GW, et al., 2022b. Effects of chaotic activity and time delay on signal transmission in FitzHugh-Nagumo neuronal system. Cognitive Neurodynamics, 16(4): 887–897. https://doi.org/10.1007/s11571-021-09743-5
Yu D, Wu Y, Ye ZQ, et al., 2022c. Inverse chaotic resonance in Hodgkin–Huxley neuronal system. The European Physical Journal Special Topics, 231(22–23):4097–4107. https://doi.org/10.1140/epjs/s11734-022-00629-z
Yu D, Wu Y, Yang LJ, et al., 2023a. Effect of topology on delay-induced multiple resonances in locally driven systems. Physica A: Statistical Mechanics and Its Applications, 609: 128330. https://doi.org/10.1016/j.physa.2022.128330
Yu D, Wang GW, Li TY, et al., 2023b. Filtering properties of Hodgkin-Huxley neuron on different time-scale signals. Communications in Nonlinear Science and Numerical Simulation, 117:106894. https://doi.org/10.1016/j.cnsns.2022.106894
Zhang XF, Ma J, 2021. Wave filtering and firing modes in a light-sensitive neural circuit. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 22(9):707–720. https://doi.org/10.1631/jzus.A2100323
Zhao Y, Li DX, 2019. Levy noise-induced inverse stochastic resonance in a single neuron. Modern Physics Letters B, 33(21):1950252. https://doi.org/10.1142/S021798491950252X
Zhou XY, Xu Y, Wang GW, et al., 2020. Ionic channel blockage in stochastic Hodgkin–Huxley neuronal model driven by multiple oscillatory signals. Cognitive Neurodynamics, 14(4):569–578. https://doi.org/10.1007/s11571-020-09593-7
Acknowledgments
This work is supported by the National Natural Science Foundation of China (No. 12175080) and the Fundamental Research Funds for the Central Universities (No. CCNU22JC009), China.
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Xueqing WANG and Dong YU designed the research. Xueqing WANG and Dong YU processed the corresponding data. Xueqing WANG wrote the first draft of the manuscript. Yong WU, Tianyu LI, and Qianming DING helped to organize the manuscript. Dong YU and Ya JIA revised and edited the final version.
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Xueqing WANG, Dong YU, Yong WU, Qianming DING, Tianyu LI, and Ya JIA declare that they have no conflict of interest.
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Wang, X., Yu, D., Wu, Y. et al. Effects of potassium channel blockage on inverse stochastic resonance in Hodgkin-Huxley neural systems. J. Zhejiang Univ. Sci. A 24, 735–748 (2023). https://doi.org/10.1631/jzus.A2200625
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DOI: https://doi.org/10.1631/jzus.A2200625
Key words
- Inverse stochastic resonance (ISR)
- Small-world neuronal network
- Potassium channel blockage
- Network blockage ratio