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Stochastic resonance in coupled small-world neural networks

Yu Hai-Tao Wang Jiang Liu Chen Che Yan-Qiu Deng Bin Wei Xi-Le

Stochastic resonance in coupled small-world neural networks

Yu Hai-Tao, Wang Jiang, Liu Chen, Che Yan-Qiu, Deng Bin, Wei Xi-Le
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  • Noise exists widely in biological neural systems, and plays an important role in system functions. A complex neural network, which contains several small-world subnetworks, is constructed based on a two-dimensional neural map. The phenomenon of stochastic resonance induced by Gaussian white noise is studied. It is found that only with an appropriate noise, can the frequency response of the network to input signal reach a peak value. Moreover, network structure has an important influence on the stochastic resonance of the neural system. With a fixed coupling strength, there exists an optimal local small-world topology, which can offer the best frequency response of the network.
      Corresponding author: Wang Jiang, jiangwang@tju.edu.cn
    • Funds: Project supported by the National Natural Science Foundation of China (Grant No. 61072012) and the Young Scientists Fund of the National Natural Science Foundation of China (Grant Nos. 50907044, 60901035).
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    Kang Y M, Xu J X, Xie Y 2005 Chaos, Solitons and Fractals 25 165

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    Wang M S 2009 Acta Phys. Sin. 58 6833 (in Chinese) [汪茂胜 2009 物理学报 58 6833]

    [40]
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    Perc M 2007 Phys. Rev. E 76 066203

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    Zhou X R, Luo X S, Jiang P Q, Yuan W J 2007 Acta Phys. Sin. 56 5679 (in Chinese) [周小荣, 罗晓曙, 蒋品群, 袁五届 2007 物理学报 56 5679]

    [44]

    Ozer M, Perc M, Uzuntarla M 2009 Phys. Lett. A 373 964

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    Bullmore E, Sporns O 2009 Nature 10 186

    [48]
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    Zamora-Lopez G, Zhou C, Kurths J 2010 Front Neuroinformatics 41

    [50]

    Rulkov N F 2001 Phys. Rev. Lett. 86 183

    [51]
    [52]

    Rulkov N F 2002 Phys. Rev. E 65 041922

    [53]
    [54]

    Watts D J, Strogatz S H 1998 Nature 393 440

    [55]
    [56]
    [57]

    Pikovsky A, Zaikin A 2002 Phys. Rev. Lett. 88 050601

    [58]
    [59]

    Barlow H B, Levick W R, Yoon M 1971 Vision Res. 11 87

  • [1]

    Gammaitoni L, Hanggi P, Jung P, Marchesoni F 1998 Rev. Mod. Phys. 70 233

    [2]

    Wang B H, Lu Q S, Lu S J 2009 Acta Phys. Sin. 58 7458 (in Chinese) [王宝华, 陆启韶, 吕淑娟 2009 物理学报 58

    [3]
    [4]

    Benzi R, Sutera A, Vulpiani A 1981 J. Phys. A 14 453

    [5]
    [6]
    [7]

    Zhang X Y, Xu W, Zhou B C 2011 Acta Phys. Sin. 60 060514 (in Chinese) [张晓燕, 徐伟, 周丙常 2011 物理学报 60 060514]

    [8]

    Schwarzkopf D S, Silvanto J, Rees G 2011 J. Neurosci. 31 3143

    [9]
    [10]

    White J A, Rubinstein J T, Kay A R 2000 Trends Neurosci. 23 131

    [11]
    [12]
    [13]

    Song Y, Zhao T J, Liu J W, Wang X Q, Zhan Y 2006 Acta Phys. Sin. 55 4020 (in Chinese) [宋扬, 赵同军, 刘金伟, 王向群, 展永 2006 物理学报 55 4020]

    [14]
    [15]

    Wang M S, Huang W X, Cui Z F 2010 Acta Phys. Sin. 59 4485 (in Chinese) [汪茂胜, 黄万霞, 崔执凤 2010 物理学报 59 4485]

    [16]
    [17]

    Kosko B, Mitaim S 2003 Neural Networks 16 755

    [18]

    Levin J E, Miller J P 1996 Nature 380 165

    [19]
    [20]

    Kanamaru T, Okabe Y 2000 Phys. Rev. E 62 2629

    [21]
    [22]

    Mato G 1998 Phys. Rev. E 58 876

    [23]
    [24]
    [25]

    Lee S G, Kim S 1999 Phys. Rev. E 60 826

    [26]

    Hasefawa H 2002 Phys. Rev. E 66 021902

    [27]
    [28]

    Patel A, Kosko B 2005 Neural Networks 18 467

    [29]
    [30]
    [31]

    Stocks N G, Mannella R 2001 Phys. Rev. E 64 30902

    [32]
    [33]

    Zhang G J, Xu J X 2005 Acta Phys. Sin. 54 557 (in Chinese) [张广军, 徐建学 2005 物理学报 54 557]

    [34]
    [35]

    Shimokawa T, Pakdaman K, Sato S 1999 Phys. Rev. E 59 3427

    [36]

    Kang Y M, Xu J X, Xie Y 2005 Chaos, Solitons and Fractals 25 165

    [37]
    [38]
    [39]

    Wang M S 2009 Acta Phys. Sin. 58 6833 (in Chinese) [汪茂胜 2009 物理学报 58 6833]

    [40]
    [41]

    Perc M 2007 Phys. Rev. E 76 066203

    [42]
    [43]

    Zhou X R, Luo X S, Jiang P Q, Yuan W J 2007 Acta Phys. Sin. 56 5679 (in Chinese) [周小荣, 罗晓曙, 蒋品群, 袁五届 2007 物理学报 56 5679]

    [44]

    Ozer M, Perc M, Uzuntarla M 2009 Phys. Lett. A 373 964

    [45]
    [46]
    [47]

    Bullmore E, Sporns O 2009 Nature 10 186

    [48]
    [49]

    Zamora-Lopez G, Zhou C, Kurths J 2010 Front Neuroinformatics 41

    [50]

    Rulkov N F 2001 Phys. Rev. Lett. 86 183

    [51]
    [52]

    Rulkov N F 2002 Phys. Rev. E 65 041922

    [53]
    [54]

    Watts D J, Strogatz S H 1998 Nature 393 440

    [55]
    [56]
    [57]

    Pikovsky A, Zaikin A 2002 Phys. Rev. Lett. 88 050601

    [58]
    [59]

    Barlow H B, Levick W R, Yoon M 1971 Vision Res. 11 87

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Publishing process
  • Received Date:  01 May 2011
  • Accepted Date:  08 August 2011
  • Published Online:  20 March 2012

Stochastic resonance in coupled small-world neural networks

    Corresponding author: Wang Jiang, jiangwang@tju.edu.cn
  • 1. School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China
Fund Project:  Project supported by the National Natural Science Foundation of China (Grant No. 61072012) and the Young Scientists Fund of the National Natural Science Foundation of China (Grant Nos. 50907044, 60901035).

Abstract: Noise exists widely in biological neural systems, and plays an important role in system functions. A complex neural network, which contains several small-world subnetworks, is constructed based on a two-dimensional neural map. The phenomenon of stochastic resonance induced by Gaussian white noise is studied. It is found that only with an appropriate noise, can the frequency response of the network to input signal reach a peak value. Moreover, network structure has an important influence on the stochastic resonance of the neural system. With a fixed coupling strength, there exists an optimal local small-world topology, which can offer the best frequency response of the network.

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