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具有时滞的抑制性自突触诱发的神经放电的加周期分岔

丁学利 李玉叶

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具有时滞的抑制性自突触诱发的神经放电的加周期分岔

丁学利, 李玉叶

Period-adding bifurcation of neural firings induced by inhibitory autapses with time-delay

Ding Xue-Li, Li Yu-Ye
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  • 神经放电节律在神经系统功能实现中起着重要的作用.具有自突触(起始和结束于同一细胞的突触)的神经元普遍存在于神经系统,本文研究了单神经元模型在抑制性自突触作用下的放电节律.结果发现,随着时滞和/或耦合强度的增加,可以诱发Rulkov神经元模型放电节律的加周期分岔.随着放电节律的周期数的增加,平均放电频率增大,当时滞和/或耦合强度大于某一阈值时,频率大于没有自突触时的放电频率.用快慢变量分离方法可以获得没有自突触的神经放电节律的分岔结构,可用于认识外界负向脉冲诱发的新节律.这些新的节律模式与加周期分岔中的节律模式一致.研究结果不仅揭示了抑制性自突触可以诱发典型的非线性现象加周期分岔,还给出了抑制性自突触可以提高放电频率的新现象,与以前的自突触压制放电的观点不同,进一步丰富了对抑制性自突触诱发的非线性现象的认识.
    Neural firing rhythm plays an important role in achieving the function of a nervous system. Neurons with autapse, which starts and ends in the same cell, are widespread in the nervous system. Previous results of both experimental and theoretical studies have shown that autaptic connection plays a role in influencing dynamics of neural firing patterns and has a significant physiological function. In the present study, the dynamics of a neuronal model, i.e., Rulkov model with inhibitory autapse and time delay, is investigated, and compared with the dynamics of neurons without autapse. The bifurcations with respect to time-delay and the coupling strength are extensively studied, and the time series of membrane potentials is also calculated to confirm the bifurcation analysis. It can be found that with the increase of time-delay and/or the coupling strength, the period-adding bifurcation of neural firing patterns can be induced in the Rulkov neuron model. With the increase of the period number of the firing rhythm, the average firing frequency increases. When time-delay and/or coupling strength are/is greater than their/its corresponding certain thresholds/threshold, the average firing frequency is higher than that of the neuron without autapse. Furthermore, new bursting patterns, which appear at suitable time delays and coupling strengths, can be well interpreted with the dynamic responses of an isolated single neuron to a negative square current whose action time, duration, and strength are similar to those of the inhibitory coupling current modulated by the coupling strength and time delay. The bursts of neurons with autapse show the same pattern as the square negative current-induced burst of the isolated single neuron when the time delay corresponds to the phase. The bifurcation structure of the neural firing rhythm of the neuron without autapse can be obtained with the fast-slow dissection method. The dynamic responses of the isolated bursting neuron to the negative square current are acquired by using the fast-slow variable dissection method, which can help to recognize the new rhythms induced by the external negative pulse current applied at different phases. The new rhythm patterns are consistent with those lying in the period-adding bifurcations. The results not only reveal that the inhibitory autapse can induce typical nonlinear phenomena such as the period-adding bifurcations, but also provide the new phenomenon that the inhibitory autapse can enhance the firing frequency, which is different from previous viewpoint that inhibitory effect often reduces the firing frequency. These findings further enrich the understanding of the nonlinear phenomena induced by inhibitory autapse.
      通信作者: 李玉叶, liyuye2000@163.com
    • 基金项目: 国家自然科学基金青年科学基金(批准号:11402039)和安徽省自然科学基金一般项目(批准号:KJ2015B008)资助的课题.
      Corresponding author: Li Yu-Ye, liyuye2000@163.com
    • Funds: Project supported by the Young Scientists Fund of the National Natural Science Foundation of China(Grant No. 11402039) and the Natural Science Foundation of Anhui Province, China(Grant No. KJ2015B008).
    [1]

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    [2]

    Clay J R 2003 J. Comput. Neurosci. 15 43

    [3]

    Gu H G, Yang M H, Li L, Liu Z Q, Ren W 2003 Phys. Lett. A 319 89

    [4]

    Li L, Gu H G, Yang M H, Liu Z Q, Ren W 2004 Int. J. Bifurcat. Chaos 14 1813

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    Gu H G, Zhu Z, Jia B 2011 Acta Phys. Sin. 60 100505(in Chinese)[古华光, 朱洲, 贾冰2011物理学报60 100505]

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    Gu H G, Chen S G 2014 Sci. China:Tech. Sci. 57 864

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    Braun H A, Huber M T, Anthes N, Voigt K, Neiman A, Pei X, Moss F 2000 Neurocomputing 32-33 51

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    Ren W, Hu S J, Zhang B J, Wang F Z, Gong Y F, Xu J X 1997 Int. J. Bifurcat. Chaos 7 1867

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    Holden A V, Fan Y S 1992 Chaos Soliton. Fractal. 2 221

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    Holden A V, Fan Y S 1992 Chaos Soliton. Fractal. 2 349

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    Holden A V, Fan Y S 1992 Chaos Soliton. Fractal. 2 583

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    Fan Y S, Holden A V 1993 Chaos Soliton. Fractal. 3 439

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    Gu H G, Pan B B, Chen G R, Duan L X 2014 Nonlinear Dyn. 78 391

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    Pouzat C, Marty A 1998 J. Physiol. 509 777

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    Bekkers J M 2003 Curr. Biol. 13 R433

    [22]

    Saada R, Miller N, Hurwitz I, Susswein A J 2009 Curr. Biol. 19 479

    [23]

    Bacci A, Huguenard J R, Prince D A 2003 J. Neurosci. 23 859

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    [26]

    Cobb S R, Halasy K, Vida I, Nyiri G, Tamás G, Buhl E H, Somogyi P 1997 Neuroscience 79 629

    [27]

    Bacci A, Huguenard J R 2006 Neuron 49 119

    [28]

    Bacci A, Huguenard J R, Prince D A 2005 Trends Neurosci. 28 602

    [29]

    Ren G D, Wu G, Ma J, Chen Y 2015 Acta Phys. Sin. 64 058702(in Chinese)[任国栋, 武刚, 马军, 陈旸2015物理学报64 058702]

    [30]

    Yilmaz E, Baysal V, Perc M, Ozer M 2016 Sci. China:Tech. Sci. 59 364

    [31]

    Song X L, Wang C N, Ma J, Tang J 2015 Sci. China:Tech. Sci. 58 1007

    [32]

    Qin H, Ma J, Wang C, Wu Y 2014 Plos One 9 e100849

    [33]

    Connelly W M 2014 Plos One 9 e89995

    [34]

    Wu Y N, Gong Y B, Wang Q 2015 Chaos 25 245

    [35]

    Qin H X, Ma J, Jin W Y, Wang C N 2010 Phys. Rev. E 82 061907

    [36]

    Hashemi M, Valizadeh A, Azizi Y 2012 Phys. Rev. E 85 021917

    [37]

    Wang H T, Ma J, Chen Y L, Chen Y 2014 Commun. Nonlinear Sci. Numer. Simulat. 19 3242

    [38]

    Wang H T, Chen Y 2015 Chin. Phys. B 24 128709

    [39]

    Wang H T, Wang L F, Chen Y L, Chen Y 2014 Chaos 24 033122

    [40]

    Wang L, Zeng Y J 2013 Neurol. Sci. 34 1977

    [41]

    Yilmaz E, Baysal V, Ozer M, Perc M 2016 Physica A 444 538

    [42]

    Ikeda K, Bekkers J M 2006 Curr. Biol. 16 R308

    [43]

    Gaudreault M, Drolet F, Vials J 2012 Phys. Rev. E 85 056214

    [44]

    Ahlborn A, Parlitz U 2004 Phys. Rev. Lett. 93 264101

    [45]

    Balanov A G, Janson N B, Schöll E 2005 Phys. Rev. E 71 016222

    [46]

    Rulkov N F 2002 Phys. Rev. E 65 041922

    [47]

    Izhikevich E M 2000 Int. J. Bifurcat. Chaos 10 1171

    [48]

    Ibarz B, Cao H J, Sanjuán M A F 2008 Phys. Rev. E 77 051918

    [49]

    Gu H G, Zhao Z G 2015 Plos One 10 e0138593

    [50]

    Belykh I, Shilnikov A 2008 Phys. Rev. Lett. 101 078102

    [51]

    Zhao Z G, Gu H G 2015 Chaos Soliton. Fractal. 80 96

  • [1]

    Coombes S, Osbaldestin A H 2000 Phys. Rev. E 62 4057

    [2]

    Clay J R 2003 J. Comput. Neurosci. 15 43

    [3]

    Gu H G, Yang M H, Li L, Liu Z Q, Ren W 2003 Phys. Lett. A 319 89

    [4]

    Li L, Gu H G, Yang M H, Liu Z Q, Ren W 2004 Int. J. Bifurcat. Chaos 14 1813

    [5]

    Gu H G, Zhu Z, Jia B 2011 Acta Phys. Sin. 60 100505(in Chinese)[古华光, 朱洲, 贾冰2011物理学报60 100505]

    [6]

    Gu H G, Chen S G 2014 Sci. China:Tech. Sci. 57 864

    [7]

    Braun H A, Huber M T, Dewald M, Schäfer K, Voigt K 1998 Int. J. Bifurcat. Chaos 8 881

    [8]

    Braun H A, Huber M T, Anthes N, Voigt K, Neiman A, Pei X, Moss F 2000 Neurocomputing 32-33 51

    [9]

    Ren W, Hu S J, Zhang B J, Wang F Z, Gong Y F, Xu J X 1997 Int. J. Bifurcat. Chaos 7 1867

    [10]

    Holden A V, Fan Y S 1992 Chaos Soliton. Fractal. 2 221

    [11]

    Holden A V, Fan Y S 1992 Chaos Soliton. Fractal. 2 349

    [12]

    Holden A V, Fan Y S 1992 Chaos Soliton. Fractal. 2 583

    [13]

    Fan Y S, Holden A V 1993 Chaos Soliton. Fractal. 3 439

    [14]

    Mo J, Li Y Y, Wei C L, Yang M H, Gu H G, Qu S X, Ren W 2010 Chin. Phys. B 19 050513

    [15]

    Gu H G, Xi L, Jia B 2012 Acta Phys. Sin. 61 080504(in Chinese)[古华光, 惠磊, 贾冰2012物理学报61 080504]

    [16]

    Tan N, Xu J X, Yang H J, Hu S J 2003 Acta Bioph. Sin. 19 395(in Chinese)[谭宁, 徐健学, 杨红军, 胡三觉2003生物物理学报19 395]

    [17]

    Yang J, Duan Y B, Xing J L, Zhu J L, Duan J H, Hu S J 2006 Neurosci. Lett. 392 105

    [18]

    Gu H G, Pan B B, Chen G R, Duan L X 2014 Nonlinear Dyn. 78 391

    [19]

    Loos H V D, Glaser E M 1973 Brain Res. 48 355

    [20]

    Pouzat C, Marty A 1998 J. Physiol. 509 777

    [21]

    Bekkers J M 2003 Curr. Biol. 13 R433

    [22]

    Saada R, Miller N, Hurwitz I, Susswein A J 2009 Curr. Biol. 19 479

    [23]

    Bacci A, Huguenard J R, Prince D A 2003 J. Neurosci. 23 859

    [24]

    Lbke J, Markram H, Frotscher M, Sakmann B 1996 Ann. Anatomy. 178 309

    [25]

    Tamás G, Buhl E H, Somogyi P 1997 J. Neurosci. 17 6352

    [26]

    Cobb S R, Halasy K, Vida I, Nyiri G, Tamás G, Buhl E H, Somogyi P 1997 Neuroscience 79 629

    [27]

    Bacci A, Huguenard J R 2006 Neuron 49 119

    [28]

    Bacci A, Huguenard J R, Prince D A 2005 Trends Neurosci. 28 602

    [29]

    Ren G D, Wu G, Ma J, Chen Y 2015 Acta Phys. Sin. 64 058702(in Chinese)[任国栋, 武刚, 马军, 陈旸2015物理学报64 058702]

    [30]

    Yilmaz E, Baysal V, Perc M, Ozer M 2016 Sci. China:Tech. Sci. 59 364

    [31]

    Song X L, Wang C N, Ma J, Tang J 2015 Sci. China:Tech. Sci. 58 1007

    [32]

    Qin H, Ma J, Wang C, Wu Y 2014 Plos One 9 e100849

    [33]

    Connelly W M 2014 Plos One 9 e89995

    [34]

    Wu Y N, Gong Y B, Wang Q 2015 Chaos 25 245

    [35]

    Qin H X, Ma J, Jin W Y, Wang C N 2010 Phys. Rev. E 82 061907

    [36]

    Hashemi M, Valizadeh A, Azizi Y 2012 Phys. Rev. E 85 021917

    [37]

    Wang H T, Ma J, Chen Y L, Chen Y 2014 Commun. Nonlinear Sci. Numer. Simulat. 19 3242

    [38]

    Wang H T, Chen Y 2015 Chin. Phys. B 24 128709

    [39]

    Wang H T, Wang L F, Chen Y L, Chen Y 2014 Chaos 24 033122

    [40]

    Wang L, Zeng Y J 2013 Neurol. Sci. 34 1977

    [41]

    Yilmaz E, Baysal V, Ozer M, Perc M 2016 Physica A 444 538

    [42]

    Ikeda K, Bekkers J M 2006 Curr. Biol. 16 R308

    [43]

    Gaudreault M, Drolet F, Vials J 2012 Phys. Rev. E 85 056214

    [44]

    Ahlborn A, Parlitz U 2004 Phys. Rev. Lett. 93 264101

    [45]

    Balanov A G, Janson N B, Schöll E 2005 Phys. Rev. E 71 016222

    [46]

    Rulkov N F 2002 Phys. Rev. E 65 041922

    [47]

    Izhikevich E M 2000 Int. J. Bifurcat. Chaos 10 1171

    [48]

    Ibarz B, Cao H J, Sanjuán M A F 2008 Phys. Rev. E 77 051918

    [49]

    Gu H G, Zhao Z G 2015 Plos One 10 e0138593

    [50]

    Belykh I, Shilnikov A 2008 Phys. Rev. Lett. 101 078102

    [51]

    Zhao Z G, Gu H G 2015 Chaos Soliton. Fractal. 80 96

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出版历程
  • 收稿日期:  2016-06-02
  • 修回日期:  2016-07-01
  • 刊出日期:  2016-11-05

具有时滞的抑制性自突触诱发的神经放电的加周期分岔

  • 1. 阜阳职业技术学院基础教学部, 阜阳 236031;
  • 2. 赤峰学院数学与统计学院, 赤峰 024000
  • 通信作者: 李玉叶, liyuye2000@163.com
    基金项目: 国家自然科学基金青年科学基金(批准号:11402039)和安徽省自然科学基金一般项目(批准号:KJ2015B008)资助的课题.

摘要: 神经放电节律在神经系统功能实现中起着重要的作用.具有自突触(起始和结束于同一细胞的突触)的神经元普遍存在于神经系统,本文研究了单神经元模型在抑制性自突触作用下的放电节律.结果发现,随着时滞和/或耦合强度的增加,可以诱发Rulkov神经元模型放电节律的加周期分岔.随着放电节律的周期数的增加,平均放电频率增大,当时滞和/或耦合强度大于某一阈值时,频率大于没有自突触时的放电频率.用快慢变量分离方法可以获得没有自突触的神经放电节律的分岔结构,可用于认识外界负向脉冲诱发的新节律.这些新的节律模式与加周期分岔中的节律模式一致.研究结果不仅揭示了抑制性自突触可以诱发典型的非线性现象加周期分岔,还给出了抑制性自突触可以提高放电频率的新现象,与以前的自突触压制放电的观点不同,进一步丰富了对抑制性自突触诱发的非线性现象的认识.

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