|
|
Negative self-feedback induced enhancement and transition of spiking activity for class-3 excitability |
Li Li(黎丽)1, Zhiguo Zhao(赵志国)2,†, and Huaguang Gu(古华光)3 |
1 Guangdong Key Laboratory of Modern Control Technology, Institute of Intelligent Manufacturing, Guangdong Academy of Sciences, Guangzhou 510070, China; 2 School of Science, Henan Institute of Technology, Xinxiang 453003, China; 3 School of Aerospace Engineering and Applied Mechanics, Tongji University, Shanghai 200092, China |
|
|
Abstract Post-inhibitory rebound (PIR) spike, which has been widely observed in diverse nervous systems with different physiological functions and simulated in theoretical models with class-2 excitability, presents a counterintuitive nonlinear phenomenon in that the inhibitory effect can facilitate neural firing behavior. In this study, a PIR spike induced by inhibitory stimulation from the resting state corresponding to class-3 excitability that is not related to bifurcation is simulated in the Morris-Lecar neuron. Additionally, the inhibitory self-feedback mediated by an autapse with time delay can evoke tonic/repetitive spiking from phasic/transient spiking. The dynamical mechanism for the PIR spike and the tonic/repetitive spiking is acquired with the phase plane analysis and the shape of the quasi-separatrix curve. The result extends the counterintuitive phenomenon induced by inhibition to class-3 excitability, which presents a potential function of inhibitory autapse and class-3 neuron in many neuronal systems such as the auditory system.
|
Received: 26 October 2021
Revised: 01 January 2022
Accepted manuscript online: 12 January 2022
|
PACS:
|
05.45.-a
|
(Nonlinear dynamics and chaos)
|
|
87.19.lg
|
(Synapses: chemical and electrical (gap junctions))
|
|
Fund: Project supported by the National Natural Science Foundation of China (Grant Nos. 11802085, 11872276, and 12072236), the Science and Technology Project of Guangzhou (Grant No. 202102021167), GDAS' Project of Science and Technology Development (Grant No. 2021GDASYL-20210103088), and the Science and Technology Development Program of Henan Province, China (Grant No. 212102310827). |
Corresponding Authors:
Zhiguo Zhao
E-mail: zzg164637758@163.com
|
Cite this article:
Li Li(黎丽), Zhiguo Zhao(赵志国), and Huaguang Gu(古华光) Negative self-feedback induced enhancement and transition of spiking activity for class-3 excitability 2022 Chin. Phys. B 31 070506
|
[1] Bean B P 2007 Nat. Rev. Neurosci. 8 451 [2] Ratté S, Hong S H, De Schutter E and Prescott S A 2013 Neuron 78 758 [3] Hodgkin A L 1948 J. Physiol. 107 165 [4] Hodgkin A L and Huxley A F 1952 J. Physiol. 117 500 [5] Prescott S A, Koninck Y D and Sejnowski T J 2008 PLoS Comput. Biol. 4 e1000198 [6] Zhao Z G and Gu H G 2017 Sci. Rep. 7 6760 [7] Izhikevich E M 2007 Dynamical systems in neuroscience:The geometry of excitability and bursting (Cambridge:MIT) [8] Chen A N and Meliza C D 2018 J. Neurophysiol. 119 1127 [9] Huguet G, Meng X Y and Rinzel J 2017 Front. Comput. Neurosci. 11 3 [10] Zhao Z G, Li L and Gu H G 2020 Sci. Rep. 10 3646 [11] Cook D L, Schwindt P C, Grande L A and Spain W J 2003 Nature 421 66 [12] MacGregor D J and Leng G 2013 PLoS Comput. Biol. 9 e1003187 [13] Smith T C and Jahr C E 2002 Nat. Neurosci. 5 760 [14] Chen A N and Meliza C D 2020 J. Neurosci. 40 2047 [15] Prescott S A, Ratté S, De Koninck Y and Sejnowski T J 2008 J. Neurophys. 100 3030 [16] Izhikevich E M 2000 Int. J. Bifurcat. Chaos 10 1171 [17] Franci A, Drion G and Sepulchre R 2012 SIAM J. App. Dyn. Syst. 11 1698 [18] Song X L, Wang H T and Chen Y 2019 Nonlinear Dynam. 96 2341 [19] Dodla R and Rinzel J 2006 Phys. Rev. E 73 010903 [20] Silver R A 2010 Nat. Rev. Neurosci. 11 474 [21] Goldwyn J H, Slabe B R, Travers J B and Terman D 2018 PLoS Comput. Biol. 14 e1006292 [22] Ferrante M, Shay C F, Tsuno Y, Chapman G W and Hasselmo M E 2017 Cereb. Cortex 27 2111 [23] Tikidji-Hamburyan R A, Martínez J J, White J A and Canavier C C 2015 J. Neurosci. 35 15682 [24] Adhikari M H, Quilichini P P, Roy D, Jirsa V and Bernard C 2012 J. Neurosci. 32 6501 [25] Shay C F, Ferrante M, Chapman IV G W and Hasselmo M E 2016 Neurobiol Learn. Mem. 129 83 [26] Felix R A, Fridberger A, Leijon S, Berrebi A S and Magnusson A K 2011 J. Neurosci. 31 12566 [27] Beiderbeck B, Myoga M H, Müller N I C, Callan A R, Friauf E, Grothe B and Pecka M 2018 Nat. Commun. 9 1771 [28] Higgs M H, Kuznetsova M S and Spain W J 2012 J. Neurosci. 32 15489 [29] Dodla R, Svirskis G and Rinzel J 2006 J. Neurophys. 95 2664 [30] Van Der Loos H and Glaser E M 1972 Brain Res. 48 355 [31] Bacci A, Huguenard J R and Prince D A 2003 J. Neurosci. 23 859 [32] Bacci A and Huguenard J R 2006 Neuron 49 119 [33] 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, Li T F, Luan G M and Shu Y S 2018 Nat. Commun. 9 4890 [34] Pouzat C and Marty A 1998 J. Physiol. 509 777 [35] Cobb S R, Halasy K, Vida I, Nyiri G, Tamas G, Buhl E H and Somogyi P 1997 Neuroscience 79 629 [36] Saada R, Miller N, Hurwitz I and Susswein A J 2009 Curr. Biol. 19 479 [37] Manseau F, Marinelli S, Méndez P, Schwaller B, Prince D A, Huguenard J R and Bacci A 2010 PLoS Biol. 8 e1000492 [38] Uzun R, Yilmaz E and Ozer M 2017 Physica A 486 386 [39] Guo D Q, Wu S D, Chen M M, Perc M, Zhang Y S, Ma J L, Cui Y, Xu P, Xia Y and Yao D Z 2016 Sci. Rep. 6 26096 [40] Baysal V, Erkan E and Yilmaz E 2021 Phil. Trans. R. Soc. A 379 20200237 [41] Uzun R 2017 Appl. Math. Comput. 315 203 [42] Song X L, Wang H T and Chen Y 2018 Nonlinear Dynam. 94 141 [43] Pinto M A, Rosso O A and Matias F S 2019 Phys. Rev. E 99 062411 [44] Ge M Y, Jia Y, Xu Y, Lu L L, Wang H W and Zhao Y J 2019 Appl. Math. Comput. 352 136 [45] Lin H R, Wang C H, Sun Y C and Yao W 2020 Nonlinear Dynam. 100 3667 [46] Yao C G, He Z W, Nakano T, Qian Y and Shuai J W 2019 Nonlinear Dynam. 97 1425 [47] Ren G D, Zhou P, Ma J, Cai N, Alsaedi A and Ahmad B 2017 Int. J. Bifurcat. Chaos 27 1750187 [48] Wang H T, Ma J, Chen Y L and Chen Y 2014 Commun. Nonlinear Sci. Numer. Simul. 19 3242 [49] Cao B, Gu H G and Li Y Y 2021 Chin. Phys. B 30 050502 [50] Zhao Z G, Li L and Gu H G 2020 Commun. Nonlinear Sci. Numer. Simul. 85 105250 [51] Li Y Y, Gu H G and Ding X L 2019 Nonlinear Dynam. 97 2091 [52] Zhao Z G, Li L, Gu H G and Gao Y 2020 Nonlinear Dynam. 99 1129 [53] Ma H Q, Jia B, Li Y Y and Gu H G 2021 Neural Plast. 2021 6692411 [54] Liu C M, Liu X L and Liu S Q 2014 Biol. Cybern. 108 75 [55] Ermentrout B 2002 Simulating, analyzing, and animating dynamical systems:A guide to XPPAUT for researchers and students (Philadelphia:SIAM) pp. 12-15 [56] Goaillard J M, Taylor A L, Pulver S R and Marder E 2010 J. Neurosci. 30 4687 [57] Li W C, Merrison-Hort R, Zhang H Y and Borisyuk R 2014 J. Neurosci. 34 6065 [58] Guan L N, Jia B and Gu H G 2019 Int. J. Bifurcat. Chaos 29 1950198 |
No Suggested Reading articles found! |
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
Altmetric
|
blogs
Facebook pages
Wikipedia page
Google+ users
|
Online attention
Altmetric calculates a score based on the online attention an article receives. Each coloured thread in the circle represents a different type of online attention. The number in the centre is the Altmetric score. Social media and mainstream news media are the main sources that calculate the score. Reference managers such as Mendeley are also tracked but do not contribute to the score. Older articles often score higher because they have had more time to get noticed. To account for this, Altmetric has included the context data for other articles of a similar age.
View more on Altmetrics
|
|
|