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Delayed excitatory self-feedback-induced negative responses of complex neuronal bursting patterns |
Ben Cao(曹奔)1, Huaguang Gu(古华光)1,†, and Yuye Li(李玉叶)2,3 |
1 School of Aerospace Engineering and Applied Mechanics, Tongji University, Shanghai 200092, China; 2 College of Mathematics and Computer Science, Chifeng University, Chifeng 024000, China; 3 Institute of Applied Mathematics, Chifeng University, Chifeng 024000, China |
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Abstract In traditional viewpoint, excitatory modulation always promotes neural firing activities. On contrary, the negative responses of complex bursting behaviors to excitatory self-feedback mediated by autapse with time delay are acquired in the present paper. Two representative bursting patterns which are identified respectively to be “Fold/Big Homoclinic” bursting and “Circle/Fold cycle” bursting with bifurcations are studied. For both burstings, excitatory modulation can induce less spikes per burst for suitable time delay and strength of the self-feedback/autapse, because the modulation can change the initial or termination phases of the burst. For the former bursting composed of quiescent state and burst, the mean firing frequency exhibits increase, due to that the quiescent state becomes much shorter than the burst. However, for the latter bursting pattern with more complex behavior which is depolarization block lying between burst and quiescent state, the firing frequency manifests decrease in a wide range of time delay and strength, because the duration of both depolarization block and quiescent state becomes long. Therefore, the decrease degree of spike number per burst is larger than that of the bursting period, which is the cause for the decrease of firing frequency. Such reduced bursting activity is explained with the relations between the bifurcation points of the fast subsystem and the bursting trajectory. The present paper provides novel examples of paradoxical phenomenon that the excitatory effect induces negative responses, which presents possible novel modulation measures and potential functions of excitatory self-feedback/autapse to reduce bursting activities.
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Received: 05 October 2020
Revised: 30 October 2020
Accepted manuscript online: 02 December 2020
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PACS:
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05.45.-a
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(Nonlinear dynamics and chaos)
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87.19.lg
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(Synapses: chemical and electrical (gap junctions))
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Fund: Project supported by the National Natural Science Foundation of China (Grant Nos. 11872276 and 11762001) and the Young Talents of Science and Technology in Universities of Inner Mongolia Autonomous Region, China (Grant No. NJYT-20-A09). |
Corresponding Authors:
Huaguang Gu
E-mail: guhuaguang@tongji.edu.cn
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Cite this article:
Ben Cao(曹奔), Huaguang Gu(古华光), and Yuye Li(李玉叶) Delayed excitatory self-feedback-induced negative responses of complex neuronal bursting patterns 2021 Chin. Phys. B 30 050502
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[1] Glass L 2001 Nature 410 277 [2] Braun H A, Wissing H, Schäfer K and Hirsch M C 1994 Nature 367 270 [3] Yang Y, Cui Y H, Sang K N, Dong Y Y, NI Z Y, Ma S S and Hu H L 2018 Nature 554 317 [4] Mondal A, Upadhyay R K, Ma J, Yadav B K, Sharma S K and Mondal A 2019 Cogn. Neurodyn. 13 393 [5] Ma J, Yang Z Q, Yang L J and Tang J 2019 J. Zhejiang Univ. Sci. A 20 639 [6] Bacci A and Huguenard J R 2006 Neuron 49 119 [7] 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 [8] Kim S Y and Lim W 2020 Cogn. Neurodyn. 14 535 [9] Wu F Q, Gu H G and Li Y Y 2019 Commun. Nonlinear Sci. Numer. Simul. 79 104924 [10] Wu F Q and Gu H G 2020 Int. J. Bifur. Chaos 20 2030009 [11] Zhao Z G and Gu H G 2017 Sci. Rep. 7 7660 [12] Beiderbeck B, Myoga M H, Müller N, Callan A R, Friauf E, Grothe B and Pecka M 2018 Nat. Commun. 9 1771 [13] Dodla R and Rinzel J 2006 Phys. Rev. E 73 010903 [14] Goaillard J M, Taylor A L, Pulver S R and Marder E 2010 J. Neurosci. 30 4687 [15] Izhikevich E M 2000 Int. J. Bifurc. Chaos 10 1171 [16] Uzuntarla M, Torres J J, Calim A and Barreto E 2019 Neural Networks 110 131 [17] Franaszczuk P J, Kudela P and Bergey G K 2003 Epilepsy Res. 53 65 [18] Bacci A, Huguenard J R and Prince D A 2003 J. Neurosci. 23 859 [19] Saada R, Miller N, Hurwitz I and Susswein A J 2009 Curr. Biol. 19 479 [20] Ding X L, Jia B and Li Y Y 2019 Acta Phys. Sin. 68 180502 (in Chinese) [21] Wang H T, Ma J, Chen Y L and Chen Y 2014 Commun. Nonlinear Sci. Numer. Simul. 19 3242 [22] Guo D Q, Chen M M, Perc M, Wu S D, Xia C, Zhang Y S, Xu P, Xia Y and Yao D Z 2016 Europhys. Lett. 114 30001 [23] Song X L, Wang H T and Chen Y 2019 Nonlinear Dyn. 96 2341 [24] Uzun R 2017 Appl. Math. Comput. 315 203 [25] Song X L, Wang H T and Chen Y 2018 Nonlinear Dyn. 94 141 [26] 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 [27] Zhang X H and Liu S Q 2018 Chin. Phys. B 27 040501 [28] Yilmaz E, Ozer M, Baysal V and Perc M 2016 Sci. Rep. 6 30914 [29] Ge M Y, Xu Y, Zhang Z K, Peng Y X, Kang W J, Yang L J and Jia Y 2018 Eur. Phys. J. Spec. Top. 227 799 [30] Qin H X, Wu Y, Wang C N and Ma J 2015 Commun. Nonlinear Sci. Numer. Simul. 23 164 [31] Ma J, Song X L, Tang J and Wang C N 2015 Neurocomputing 167 378 [32] Yang X L, Yu Y H and Sun Z K 2017 Chaos 27 083117 [33] Wang Q Y, Murks A, Perc M and Lu Q S 2011 Chin. Phys. B 20 040504 [34] Tikidji-hamburyan R A, Martinez J J, White J A and Canavier C C 2015 J. Neurosci. 35 15682 [35] Zhao Z G, LI L, Gu H G and Gao Y 2020 Nonlinear Dyn. 99 1129 [36] Connelly W M 2014 PLoS ONE 9 e89995 [37] Deleuze C, Bhumbra G S, Pazienti A, Lourenco J, Mailhes C, Aguirre A, Beato M and Bacci A 2019 PLoS Biol. 17 e3000419 [38] Li Y Y, Gu H G and Ding X L 2019 Nonlinear Dyn. 97 2091 [39] Yao C G, He Z W, Nakano T, Qian Y and Shuai J W 2019 Nonlinear Dyn. 97 1425 [40] Zhao Z G, Li L and Gu H G 2020 Commun. Nonlinear Sci. Numer. Simul. 85 105250 [41] Cao B, Guan L N and Gu H G 2018 Acta Phys. Sin. 67 240502 (in Chinese) [42] Wang X J 2010 Physiol. Rev. 90 1195 [43] Bekkers J M and Stevens C F 1991 Proc. Natl. Acad. Sci. USA 88 7834 [44] Pouzat C and Marty A 1998 J. Physiol-London 509 777 [45] Ermentrout B 2002 Simulating, analyzing, and animating dynamical systems: A guide to XPPAUT for researchers and students. (Philadelphia: SIAM) [46] Ayata C and Lauritzen M 2015 Physiol. Rev. 95 953 [47] Ren G D, Zhou P, Ma J, Cai N, Alsaedi A and Ahmad B 2019 Int. J. Bifur. Chaos 27 1750187 [48] Zhang X J, Gu H G and Guan L N 2019 Sci. China Technol. Sci. 62 1502 [49] Guan L N, Gu H G and Jia Y B 2020 Nonlinear Dyn. 100 3645 [50] Wang Z L and Shi X R 2020 Cogn. Neurodyn. 14 115 [51] Xu Y, Liu M H, Zhu Z G and Ma J 2020 Chin. Phys. B 29 098704 [52] Jia B, Wu Y C, He D, Guo B H and Xue L 2018 Nonlinear Dyn. 93 1599 [53] Zhao Z G and Gu H G 2017 Procedia IUTAM 22 160 [54] Zhao Z G and Gu H G 2015 Chaos Soliton. Fract. 80 96 |
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