Spiking sychronization regulated by noise in three types of Hodgkin–Huxley neuronal networks

doi:10.1088/1674-1056/21/10/108701

Abstract In this paper, we study spiking synchronization in three different types of Hodgkin-Huxley neuronal networks, which are the small-world, regular, and random neuronal networks. All the neurons are subjected to subthreshold stimulus and external noise. It is found that in each of all the neuronal networks there is an optimal strength of noise to induce the maximal spiking synchronization. We further demonstrate that in each of the neuronal networks there is a range of synaptic conductance to induce the effect that an optimal strength of noise maximizes the spiking synchronization. Only when the magnitude of the synaptic conductance is moderate, will the effect be considerable. However, if the synaptic conductance is small or large, the effect vanishes. As the connections between neurons increase, the synaptic conductance to maximize the effect decreases. Therefore, we show quantitatively that the noise-induced maximal synchronization in the Hodgkin-Huxley neuronal network is a general effect, regardless of the specific type of neuronal network.

Keywords: spiking synchronization neuronal network noise

Received: 03 December 2011
Revised: 03 May 2012
Accepted manuscript online:

PACS:	87.19.lm	(Synchronization in the nervous system)
	87.19.L-	(Neuroscience)
	87.19.lj	(Neuronal network dynamics)

Fund: Project supported by the National Natural Science Foundation of China (Grant No. 11065003), the Natural Science Foundation of Guangxi Zhuang Autonomous Region, China (Grant No. 2011GXNSFA018129), and the Research Funding of Guangxi Provincial Education Department, China (Grant No. 201012MS026).

Corresponding Authors: Zeng Shang-You
E-mail: zsy@mailbox.gxnu.edu.cn

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Zhang Zheng-Zhen (张争珍), Zeng Shang-You (曾上游), Tang Wen-Yan (唐文艳), Hu Jin-Lin (胡锦霖), Zeng Shao-Wen (曾紹稳), Ning Wei-Lian (宁维莲), Qiu Yi (邱怡), Wu Hui-Si (吴慧思) Spiking sychronization regulated by noise in three types of Hodgkin–Huxley neuronal networks 2012 Chin. Phys. B 21 108701

[1]	Huang L, Feng R and Wang M 2004 Phys. Lett. A 320 271
[2]	Dibner C, Schibler U and Albrecht U 2010 Ann. Rev. Physiol. 72 517
[3]	Rossoni E, Chen Y, Ding M and Feng J 2005 Phys. Rev. E 71 061904
[4]	Ward L M 2003 Trends in Cognitive Sciences 7 553
[5]	Melloni L, Molina C, Pena M, Torres D, Singer W and Rodriguez E 2007 J. Neurosci. 27 2858
[6]	Wu X, Chen X, Li Z, Han S and Zhang D 2007 NeuroImage 35 1654
[7]	Klimesch W, Freunberger R and Sauseng P 2010 Neurosci. Biobehav. Rev. 34 1002
[8]	Arecchi F T 2004 Physica A 338 218
[9]	Watts D J and Strogaz H S 1998 Nature 393 440
[10]	Qian Y, Huang X, Hu G and Liao X 2010 Phys. Rev. E 81 036101
[11]	Liao W, Ding J, Marinazzo D, Xu Q, Wang Z, Yuan C, Zhang Z, Lu G and Chen H 2011 NeuroImage 54 2683
[12]	Bullmore E and Sporns O 2009 Nat. Rev. Neurosci. 10 187
[13]	He Y, Chen Z J and Evans A C 2007 Cereb. Cortex 17 2407
[14]	Timme M, Wolf F and Geisel T 2002 Phys. Rev. Lett. 89 258701
[15]	Gelenbe E and Timotheou S 2008 Neural Comp. 20 2308
[16]	Börgers C and Kopell N 2003 Neural Comp. 15 509
[17]	Zeng S Y, Tang Y and Jung P 2007 Phys. Rev. E 76 011905
[18]	Hänggi P 2002 Chem. Phys. Chem. 3 285
[19]	Lindner B, García-Ojalvo J, Neiman A and Schimansky-Geier L 2004 Phys. Rep. 392 321
[20]	Liu S B, Wu Y, Hao Z W, Li Y J and Jia N 2012 Acta Phys. Sin. 61 020503 (in Chinese)
[21]	Xu C and Kang Y M 2011 Acta Phys. Sin. 60 108701 (in Chinese)
[22]	Liu S J, Wang Q, Liu B, Yan S W and Fumihiko S 2011 Chin. Phys. B 20 128703
[23]	Li L, Jin Z L and Li B 2011 Acta Phys. Sin. 60 048703 (in Chinese)
[24]	Lindner J F, Bennett M and Wiesenfeld K 2006 Phys. Rev. E 73 031107
[25]	Toral R, Mirasso C R and Gunton J D 2003 Europhys. Lett. 61 162
[26]	Perc M 2008 Phys. Rev. E 78 036105
[27]	Gao Z, Hu B and Hu G 2001 Phys. Rev. E 65 016209
[28]	Cao J, Li P and Wang W 2006 Phys. Lett. A 353 318
[29]	Li P, Cao J and Wang Z 2007 Physica A 373 261
[30]	Chen M and Chen W 2009 Chaos, Solitons & Fractals 41 2716
[31]	Han F, Lu Q S, Wiercigroch M and Ji Q B 2009 Chin. Phys. B 18 482
[32]	Gao M and Cui B 2009 Chin. Phys. B 18 76
[33]	Tang Y, Zhong H and Fang J 2008 Chin. Phys. B 17 4080
[34]	Shi X and Lu Q 2005 Chin. Phys. 14 77
[35]	Gong Y, Wang M, Hou Z and Xin H 2005 Chem. Phys. Chem. 6 1042
[36]	Masuda N and Aihara K 2004 Biological Cybernetics 90 302
[37]	Yu Y, Wang W, Wang J and Liu F 2001 Phys. Rev. E 63 021907
[38]	Zeng S and Jung P 2004 Phys. Rev. E 70 011903
[39]	Bernander O, Douglas R J, Martin K A and Koch C 1991 Proc. Natl. Acad. Sci. 88 11569
[40]	Megias M, Emri Z S, Ereund T F and Gulyas A I 2001 Neuroscience 102 527
[41]	Newman M E J and Watts D J 1999 Phys. Lett. A 360 341
[42]	Ozer M and Uzuntarla M 2008 Phys. Lett. A 372 4603
[43]	Neiman A, Silchenko A, Anishchenko V and Schimansky-Geier L 1998 Phys. Rev. E 58 7118
[44]	Hu B and Zhou C 2000 Phys. Rev. E 61 R1001
[45]	Osipov G V, Pikovsky A S, Rosenblum M G and Kurths J 1997 Phys. Rev. E 55 2353
[46]	Neiman A, Schimansky-Geier L, Cornell-Bell A and Moss F 1999 Phys. Rev. Lett. 83 4896
[47]	Gang H, Ditzinger T, Ning C Z and Haken H 1993 Phys. Rev. Lett. 71 807
[48]	Yu Y, Wang W, Wang J and Liu F 2001 Phys. Rev. E 63 021907
[49]	Li S G, Neiman A and Kim S 1998 Phys. Rev. E 57 3292
[50]	Kwon O and Moon H T 2002 Phys. Lett. A 298 319

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Spiking sychronization regulated by noise in three types of Hodgkin–Huxley neuronal networks

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