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Stochastic resonance and synchronization behaviors of excitatory-inhibitory small-world network subjected to electromagnetic induction |
| Xiao-Han Zhang(张晓函), Shen-Quan Liu(刘深泉) |
| School of Mathematics, South China University of Technology, Guangzhou 510640, China |
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Abstract The phenomenon of stochastic resonance and synchronization on some complex neuronal networks have been investigated extensively. These studies are of great significance for us to understand the weak signal detection and information transmission in neural systems. Moreover, the complex electrical activities of a cell can induce time-varying electromagnetic fields, of which the internal fluctuation can change collective electrical activities of neuronal networks. However, in the past there have been a few corresponding research papers on the influence of the electromagnetic induction among neurons on the collective dynamics of the complex system. Therefore, modeling each node by imposing electromagnetic radiation on the networks and investigating stochastic resonance in a hybrid network can extend the interest of the work to the understanding of these network dynamics. In this paper, we construct a small-world network consisting of excitatory neurons and inhibitory neurons, in which the effect of electromagnetic induction that is considered by using magnetic flow and the modulation of magnetic flow on membrane potential is described by using memristor coupling. According to our proposed network model, we investigate the effect of induced electric field generated by magnetic stimulation on the transition of bursting phase synchronization of neuronal system under electromagnetic radiation. It is shown that the intensity and frequency of the electric field can induce the transition of the network bursting phase synchronization. Moreover, we also analyze the effect of magnetic flow on the detection of weak signals and stochastic resonance by introducing a subthreshold pacemaker into a single cell of the network and we find that there is an optimal electromagnetic radiation intensity, where the phenomenon of stochastic resonance occurs and the degree of response to the weak signal is maximized. Simulation results show that the extension of the subthreshold pacemaker in the network also depends greatly on coupling strength. The presented results may have important implications for the theoretical study of magnetic stimulation technology, thus promoting further development of transcranial magnetic stimulation (TMS) as an effective means of treating certain neurological diseases.
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Received: 23 October 2017
Revised: 09 January 2018
Accepted manuscript online:
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PACS:
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05.40.-a
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(Fluctuation phenomena, random processes, noise, and Brownian motion)
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05.45.-a
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(Nonlinear dynamics and chaos)
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87.85.Ng
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(Biological signal processing)
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87.85.dq
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(Neural networks)
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| Fund: Project supported by the National Natural Science Foundation of China (Grant No. 11172103). |
Corresponding Authors:
Shen-Quan Liu
E-mail: mashqliu@scut.edu.cn
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Cite this article:
Xiao-Han Zhang(张晓函), Shen-Quan Liu(刘深泉) Stochastic resonance and synchronization behaviors of excitatory-inhibitory small-world network subjected to electromagnetic induction 2018 Chin. Phys. B 27 040501
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| [1] |
Hodgkin A L and Huxley A F 1990 Bull. Math. Biol. 52 25
|
| [2] |
Morris and Lecar 1981 Biophys. J. 35 193
|
| [3] |
Zhang J, Huang S, Pang S, Wang M and Gao S 2015 Chin. Phys. Lett. 32 120502
|
| [4] |
Rulkov N F, Timofeev I and Bazhenov M 2004 J. Comput. Neurosci. 17 203
|
| [5] |
Shi X and Lu Q 2005 Chin. Phys. B 14 77
|
| [6] |
Gu H, Pan B, Chen G and Duan L 2014 Nonlinear Dyn. 78 391
|
| [7] |
Izhikevich, E M 2004 IEEE Trans. Neural Networks 15 1063
|
| [8] |
Lv M, Wang C, Ren G, Ma J and Song X 2016 Nonlinear Dyn. 85 1479
|
| [9] |
Lv M and Ma J 1981 Biophys. J. 205 375
|
| [10] |
Han F, Wang Z, Fan H and Gong T 2015 Chin. Phys. Lett. 32 040502
|
| [11] |
Gu H, Pan B and Li Y 2015 Nonlinear Dyn. 82 1191
|
| [12] |
Gu H, Chen S G, Curtu R and Li Y 2015 Chin. Phys. B 24 050505
|
| [13] |
Ventriglia F 2008 Cognitive Neurodynamics 2 335
|
| [14] |
Cheyne D, Bells S, Ferrari P, Gaetz W and Bostan A C 2008 Neuroimage 42 332
|
| [15] |
Koenig T, Prichep L, Dierks T, Hubl D, Wahlund L O, John E R and Jelic V 2005 Neurobiology of Aging 26 165
|
| [16] |
Gorsev G, Y and Erol B 2010 Cognitive Neurodynamics 4 263
|
| [17] |
Wilson T W, Rojas D C, Reite M L, Teale P D and Rogers S J 2007 Biological Psychiatry 62 192
|
| [18] |
Wang R, Zhang Z, Qu J and Cao J 2011 IEEE Trans. Neural Networks 22 1097
|
| [19] |
Jiao X and Wang R 2010 Int. J. Nonlinear Mech. 45 647
|
| [20] |
Yuan L, Liu Z, Zhang H, Ding X, Yang M, Gu H and Ren W 2011 Chin. Phys. B 20 020508
|
| [21] |
Li H and Wang Y 2014 Acta Phys. Sin. 63 120506(in Chinese)
|
| [22] |
Yu H, Wang J, Liu C, Che Y, Deng B and Wei X 2012 Acta Phys. Sin. 61 068702(in Chinese)
|
| [23] |
Gong Y, Wang M, Hou Z and Xin H 2005 Chemphyschem 6 1042
|
| [24] |
Gao Z, Hu B and Hu G 2002 Phys. Rev. E 65 016209
|
| [25] |
Ozer M, Perc M, Uzuntarla M and Koklukaya E 2010 Neuroreport 21 338
|
| [26] |
Bezrukov S M and Vodyanoy I 1995 Nature 378 362
|
| [27] |
Levin Amp J E and Miller J P 1996 Nature 380 165
|
| [28] |
Perc M 2007 Phys. Rev. E 76 066203
|
| [29] |
Ozer M, Perc M and Uzuntarla M 2009 Phys. Lett. A 373 964
|
| [30] |
Yu H, Wang J, Liu C, Deng B and Wei X 2011 Chaos 21 047502
|
| [31] |
Pashut T, Wolfus S, Friedman A, Lavidor M, Bargad I, Yeshurun Y and Korngreen A 2011 Plos Comput. Biology 7 e1002022
|
| [32] |
Devos D and Defebvre L 2006 Prog. Brain Res. 159 331
|
| [33] |
Ma J, Wu F and Wang C 2017 Int. J. Mod. Phys. B 31 391
|
| [34] |
Watts D J and Strogatz S H 1998 Nature 393 6684
|
| [35] |
Izhikevich E M 2003 IEEE Trans. Neural Networks 14 1569
|
| [36] |
Bedard C, KroGer H and Destexhe A 2006 Phys. Rev. E 73 051911
|
| [37] |
Gianni M, Liberti M, Apollonio F and Inzeo G 2006 Biological Cybernetics 94 118
|
| [38] |
Bao B C, Liu Z and Xu J P 2010 Electron. Lett. 46 237
|
| [39] |
Li Q, Zeng H and Li J 2015 Nonlinear Dyn. 79 2295
|
| [40] |
Golomb D, Shedmi A, Curtu R and Ermentrout G B 2006 J. Neurophysiology 95 1049
|
| [41] |
Jung P, Behn U, Pantazelou E and Moss F 1992 Phys. Rev. A 46 R1709
|
| [42] |
Jung P and Mayer-Kress G 1995 Phys. Rev. Lett. 74 2130
|
| [43] |
Mato G 1998 Phys. Rev. E 58 876
|
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