-
The fine structure of multi-compartment neurons captures both temporal and spatial characteristics, offering rich responses and intrinsic mechanisms. However, current studies on the effects of channel blocking and noise on neuronal response states are predominantly limited to single-compartment neurons. This study introduces an analytical method to explore the internal mechanisms of channel blocking and noise effects on the response states of multi-compartment neurons, using the smooth Pinsky-Rinzel two-compartment neuron model as a case study. Potassium, sodium, and calcium ion channel blocking coefficients were separately introduced to develop a smooth Pinsky-Rinzel neuron model with ion channel blocking. Methods such as single-parameter bifurcation analysis, double-parameter bifurcation analysis, coefficient of variation, and frequency characteristics analysis were employed to examine the effects of various ion channel blockings on neuronal response states. Additionally, smooth Pinsky-Rinzel neuron subunit noise models and Conductance noise models were constructed to investigate their response characteristics using interspike interval analysis and coefficient of variation indicators. Subthreshold stimulation was used to explore the presence of stochastic resonance phenomena. Single-parameter bifurcation analysis of the ion channel blocking model elucidated the dynamic processes of two torus bifurcations and limit point bifurcations in Pinsky-Rinzel neuron firing under potassium ion blocking. Double-parameter bifurcation analysis revealed a near-linear increase in the Hopf bifurcation node of potassium ions with input current, whereas sodium ions exhibited a two-stage pattern of linear decline followed by exponential rise. Analysis of average firing frequency and coefficient of variation indicated that moderate potassium channel blocking promoted firing, sodium channel blocking inhibited firing, and calcium channel blocking showed complex characteristics but primarily promoted firing. Subthreshold stimulation of the channel noise model demonstrated stochastic resonance phenomena in both models, accompanied by more intense chaotic firing, highlighting the positive role of noise in neural signal transmission. Interspike interval and coefficient of variation indicators showed consistent variation levels for both noise models, with the conductance model displaying greater sensitivity to membrane area and stronger encoding capabilities. This study analyzed the general frequency characteristics of potassium and sodium ions on a multi-compartment neuron model through ion channel blocking models, providing particular insights into the unique effects of calcium ions. Further, the study explored stochastic resonance using ion channel noise models, supporting the theory of noise-enhanced signal processing and offering new perspectives and tools for future research on complex information encoding in neural systems. By constructing ion channel blocking models, the research analyzed the effects of potassium and sodium ions on the frequency characteristics of multi-compartment neurons and revealed the special influences of calcium ions. Using ion channel noise models, the study investigated stochastic resonance, supporting the theory that noise enhances signal processing. This research offers new perspectives and tools for studying complex information encoding in neural systems.
-
Keywords:
- Pinsky-Rinzel neuron /
- channel noise /
- Channel blocking /
- Hopf bifurcation analysis
-
[1] Yan H R, Liu Q S, Zhang X L, Zhao X, Wang Y 2024 Chin. Phys. B 33 058801.
[2] Wu J, Pan C Y 2022 Acta Phys. Sin. 71 048701（in Chinese） [吴静, 潘春宇 2022 物理学报 71 048701].
[3] Xu Y, Jia Y, Ge M, Lu L, Yang L, Zhan X 2018 Neurocomputing 283 196.
[4] Zhou X Y, Xu Y, Wang G W, Jia Y 2020 Cogn. Neurodyn. 14 569.
[5] Zhu J L, Qiu H, Guo W L 2023 Biophys. J. 122 496.
[6] Narahashi T, Moore J W 1968 J. Gen. Physiol. 51 93.
[7] Gong Y B, Hao Y H, Lin X, Wang L, Ma X G 2011 BioSystems 106 76.
[8] Longtin A 1993 J. Stat. Phys. 70 309.
[9] Faisal A A, Selen L P J, Wolpert D M 2008 Nat. Rev. Neurosci. 9 292.
[10] Ermentrout G B, Galán R F, Urban N N 2008 Trends Neurosci. 31 428.
[11] Chow C C, White J A 1996 Biophys. J. 71 3013.
[12] Mahapatra C, Samuilik I 2024 Mathematics 12 1149.
[13] van Rossum M C W, O'Brien B J, Smith R G 2003 J. Neurophysiol. 89 2406.
[14] Chen Y, Yu L C, Qin S M 2008 Phys. Rev. E 78 051909.
[15] Stacey W C, Durand D M 2001 J. Neurophysiol. 86 1104.
[16] Lu L, Jia Y, Kirunda J B, Xu Y, Ge M Y, Pei Q M, Yang L J 2019 Nonlinear Dyn. 95 1673.
[17] Sengupta B, Laughlin S B, Niven J E 2010 Phys. Rev. E 81 011918.
[18] Maisel B, Lindenberg K 2017 Phys. Rev. E 95 022414.
[19] Anderson D F, Ermentrout B, Thomas P J 2015 J. Comput. Neurosci. 38 67.
[20] Kilinc D, Demir A 2017 IEEE Trans. Biomed. Circuits Syst. 11 958.
[21] Fox R F, Lu Y 1994, Phys. Rev. E 49 3421.
[22] Goldwyn J H, Shea-Brown E 2011 PloS Comput. Biol. 7 e1002247.
[23] Linaro D, Storace M, Giugliano M 2011 PloS Comput. Biol. 7 e1001102.
[24] Goldwyn J H, Imennov N S, Famulare M, Shea-Brown E 2011 Phys. Rev. E 83 041908.
[25] Guckenheimer J, Labouriau J S 1993 Bull. Math. Biol. 55 937.
[26] Xie Y, Chen L N, Kang Y M, Aihara K 2008 Phys. Rev. E 77 061921.
[27] Erhardt A H, Mardal K A, Schreiner J E 2020 J. Comput. Neurosci. 48 229.
[28] Hu B, Xu M B, Zhu L Y, Lin J H, Wang Z Z, Wang D J, Zhang D M 2022 J. Theor. Biol. 536 110979.
[29] Wang Z Z, Liu Q S, Bi Y H, Zhang X L, Chen J Y, Li F J 2022 Commun. Nonlinear Sci. Numer. Simul. 114 106614.
[30] Huang Y D, Rüdiger S, Shuai J W 2015 Phys. Biol. 12 061001.
[31] Cox D R 2017 The Theory of Stochastic Processes （New York: Routledge）pp1-408.
[32] Tuckerman L S, Barkley D 2000 Bifurcation Analysis for Timesteppers （New York: Springer）pp453-466.
[33] Ward M, Rhodes O 2022 Front. Neurosci. 16 881598.
[34] Stöckel A, Eliasmith C 2022 Neuromorph. Comput. Eng. 2 024011.
[35] Nomura M, Chen T Q, Tang C, Todo Y, Sun R, Li B, Tang Z 2024 Electronics 13 1367.
[36] Kühn S, Gallinat J 2014 Hum. Brain Mapp. 35 1129.
[37] Biagini G, D’Arcangelo G, Baldelli E, D’Antuono M, Tancredi V, Avoli M 2005 Neuromol. Med. 7 325.
[38] Sendrowski K, Sobaniec W 2013 Pharmacol. Rep. 65 555.
[39] Pinsky P F, Rinzel J 1994 J. Comput. Neurosci. 1 39.
[40] Taxidis J, Coombes S, Mason R, Owen M R 2012 Hippocampus 22 995.
[41] Kamondi A, Acsády L, Wang X J, Buzsáki G 1998 Hippocampus 8 244.
[42] Booth V, Bose A 2001 J. Neurophysiol. 85 2432.
[43] Mainen Z F, Sejnowski T J 1996 Nature 382 363.
[44] Hahn P J, Durand D M 2001 J. Comput. Neurosci. 11 5.
[45] Atherton L A, Prince L Y, Tsaneva A K 2016 J. Comput. Neurosci. 41 91.
[46] Zhang S M, Yang Q, Ma C X, Wu J B, Li H Z, Tan K C 2024 Proceedings of the AAAI Conference on Artificial Intelligence Vancouver Canada, February 20-27, 2024 p16838.
[47] Koudriavtsev A B, Jameson R F, Linert W 2011 The Law of Mass Action （Berlin: Springer Science & Business Media）pp1-441.
[48] Johnston D, Wu S M S 1994 Foundations of Cellular Neurophysiology （Cambridge, MA: MIT Press）pp1-710.
[49] Wang R, Wu Y, Liu S B 2013 Acta Phys. Sin. 62 220504（in Chinese） [王荣, 吴莹, 刘少宝 2013 物理学报 62 220504].
[50] Clarke S G, Scarnati M S, Paradiso K G 2016 J. Neurosci. 36 11559.
[51] Harnett M T, Makara J K, Spruston N, Kath W L, Magee J C 2012 Nature 491 599.
[52] Liu S B, Wu Y, Hao Z W, Li Y J, Jia N 2012 Acta Phys. Sin. 61 020503（in Chinese） [刘少宝, 吴莹, 郝忠文, 李银军, 贾宁 2012 物理学报 61 020503].
[53] Liang Y M, Lu B, Gu H G 2022 Acta Phys. Sin. 71 230502（in Chinese） [梁艳美, 陆博, 古华光 2022 物理学报 71 230502].
[54] Adair R K 2003 Proc. Natl. Acad. Sci. USA 100 12099.
[55] Xiao F L, Zhang J, Wang Z H, Li S, Liu M H, Chen Y J, Wang X R 2023 Chaos Solitons Fractals 166 112969.
[56] Li L, Zhao Z G, Gu H G 2022 Acta Phys. Sin. 71 050504（in Chinese） [黎丽, 赵志国, 古华光 2022 物理学报 71 050504].
[57] Guo Z H, Liu Q, Zhang X L, Wang Y, Zhao X 2023 Chin. Phys. B 32 038701.
Metrics
- Abstract views: 164
- PDF Downloads: 4
- Cited By: 0
下载:
