搜索

x

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于抑制性突触可塑性的反馈神经回路兴奋性与抑制性动态平衡

王美丽 王俊松

引用本文:
Citation:

基于抑制性突触可塑性的反馈神经回路兴奋性与抑制性动态平衡

王美丽, 王俊松

Dynamical balance between excitation and inhibition of feedback neural circuit via inhibitory synaptic plasticity

Wang Mei-Li, Wang Jun-Song
PDF
导出引用
  • 大脑皮层的兴奋性与抑制性平衡是维持正常脑功能的前提, 而其失衡会诱发癫痫、帕金森、抑郁症等多种神经疾病, 因此兴奋性与抑制性平衡的研究是脑科学领域的核心科学问题. 反馈神经回路是脑皮层网络的典型连接模式, 抑制性突触可塑性在兴奋性与抑制性平衡中扮演关键角色. 本文首先构建具有抑制性突触可塑性的反馈神经回路模型; 然后通过计算模拟研究揭示在抑制性突触可塑性的调控下反馈神经回路的兴奋性与抑制性可取得较高程度的动态平衡, 并且二者的平衡对输入扰动具有较强的鲁棒性; 其次给出了基于抑制性突触可塑性的反馈神经回路兴奋性与抑制性平衡机理的解释; 最后发现反馈回路神经元数目有利于提高兴奋性与抑制性平衡的程度, 这在一定程度上解释了为何神经元之间会存在较多的连接. 本文的研究对于理解脑皮层的兴奋性与抑制性动态平衡机理具有重要的参考价值.
    Cortical cortex is mainly composed of excitatory and inhibitory neurons. Balance between excitation and inhibition is a ubiquitous experimental phenomenon in brain. On the one hand, balanced excitation and inhibition plays a crucial role in maintaining normal brain functions; on the other hand, the loss of balance between the two opposing forces will cause neural diseases, such as epilepsy, Parkinson, schizophrenia, etc. Thus the research on balance between excitation and inhibition increasingly focuses on the field of neuroscience. Feedback neural circuit with recurrent excitatory and inhibitory connections is ubiquitous in cortical cortex. However, it is still little known how to achieve and maintain the balance between excitation and inhibition in feedback neural circuit. In this study it is proposed that inhibitory synaptic plasticity should play a key role in regulating the balance between excitation and inhibition. Firstly, the feedback neural circuit model is constructed using leaky integrate-and-fire neuron model, mainly composed of excitatory feed-forward loop, and excitatory and inhibitory recurrent connections. The proposed inhibitory synaptic model is incorporated into the feedback neural circuit model, and whose mathematical formulation is presented in detail. Secondly, the excitatory and inhibitory synaptic currents are obtained through numerical simulations, which demonstrate that the precise balance between excitation and inhibition is achieved under the regulation of inhibitory synaptic plasticity. Furthermore, the research results show that this balance is robust to the fluctuation inputs and disturbances. Thirdly, the balance mechanism underlined by inhibitory synaptic plasticity is elucidated through theoretical and simulation analysis, separately, which provides a clear explanation and an insight into how to achieve and maintain the balance between excitation and inhibition in a feedback neural circuit. Finally, the numerical results reveal that the neuron numbers in excitatory and inhibitory feedback loop exert an influence on the balance, and the larger number can enhance the balance between excitation and inhibition, which explains, to some extent, why there are dense connections between neurons in brain. The results in this study shed light on the balance mechanism of feedback neural circuit, and provide some clues for understanding the mechanism of balance between excitation and inhibition in the brain area.
    • 基金项目: 国家自然科学基金(批准号: 61473208)和国家自然科学基金重大研究计划(批准号: 91132722)资助的课题.
    • Funds: Project supported by the National Natural Science Foundation of China (Grant No. 61473208), and the Major Research Plan of the National Natural Science Foundation of China (Grant No. 91132722).
    [1]

    Hu H, Gan J, Jonas P 2014 Science 345 1255263

    [2]

    Yizhar O, Fenno L E, Prigge M, Schneider F, Davidson T J, O’Shea D J, Sohal V S, Goshen I, Finkelstein J, Paz J T, Stehfest K, Fudim R, Ramakrishnan C, Huguenard J R, Hegemann P, Deisseroth K 2011 Nature 477 171

    [3]

    Litwin-Kumar A, Oswald A M, Urban N N, Doiron B 2011 PLoS Comput. Biol. 7 e1002305

    [4]

    Lombardi F, Herrmann H J, Perrone-Capano C, Plenz D, de Arcangelis L 2012 Phys. Rev. Lett. 108 228703

    [5]

    Vogels T P, Sprekeler H, Zenke F, Clopath C, Gerstner W 2011 Science 334 1569

    [6]

    Atallah B V, Scanziani M 2009 Neuron 62 566

    [7]

    Lim S, Goldman M S 2013 Nat. Neurosci. 16 1306

    [8]

    Xia X F, Wang J S 2014 Acta Phys. Sin. 63 140503 (in Chinese) [夏小飞,王俊松 2014 物理学报 63 140503]

    [9]

    López-Huerta V G, Carrillo-Reid L, Galarraga E, Tapia D, Fiordelisio T, Drucker-Colin R, Bargas J 2013 J. Neurosci. 33 4964

    [10]

    van Vreeswijk C, Sompolinsky H 1996 Science 274 1724

    [11]

    Deco G, Corbetta M 2011 The Neuroscientist 17 107

    [12]

    Park H J, Friston K 2013 Science 342 1238411

    [13]

    Isaacson J S, Scanziani M 2011 Neuron 72 231

    [14]

    Maass W, Joshi P, Sontag E D 2007 PLoS Comput. Biol. 3 e165

    [15]

    Shu Y, Hasenstaub A, McCormick D A 2003 Nature 423 288

    [16]

    Luz Y, Shamir M 2012 PLoS Comput. Biol. 8 e1002334

    [17]

    Turrigiano G G 2008 Cell 135 422

    [18]

    Woodin M A, Ganguly K, Poo M M 2003 Neuron 39 807

    [19]

    Jansen B H, Rit V G 1995 Biol. Cybern. 73 357

    [20]

    Wang J S, Xu Y 2014 Acta Phys. Sin. 63 068701 (in Chinese) [王俊松, 徐瑶 2014 物理学报 63 068701]

    [21]

    Chacron M J, Longtin A, Maler L 2005 Phys. Rev. E: Stat. Nonlinear Soft Matter Phys. 72 051917

    [22]

    Dayan P, Abbott L F 2001 Theoretical Neuroscience (Cambridge, MA: MIT Press) pp195-250

  • [1]

    Hu H, Gan J, Jonas P 2014 Science 345 1255263

    [2]

    Yizhar O, Fenno L E, Prigge M, Schneider F, Davidson T J, O’Shea D J, Sohal V S, Goshen I, Finkelstein J, Paz J T, Stehfest K, Fudim R, Ramakrishnan C, Huguenard J R, Hegemann P, Deisseroth K 2011 Nature 477 171

    [3]

    Litwin-Kumar A, Oswald A M, Urban N N, Doiron B 2011 PLoS Comput. Biol. 7 e1002305

    [4]

    Lombardi F, Herrmann H J, Perrone-Capano C, Plenz D, de Arcangelis L 2012 Phys. Rev. Lett. 108 228703

    [5]

    Vogels T P, Sprekeler H, Zenke F, Clopath C, Gerstner W 2011 Science 334 1569

    [6]

    Atallah B V, Scanziani M 2009 Neuron 62 566

    [7]

    Lim S, Goldman M S 2013 Nat. Neurosci. 16 1306

    [8]

    Xia X F, Wang J S 2014 Acta Phys. Sin. 63 140503 (in Chinese) [夏小飞,王俊松 2014 物理学报 63 140503]

    [9]

    López-Huerta V G, Carrillo-Reid L, Galarraga E, Tapia D, Fiordelisio T, Drucker-Colin R, Bargas J 2013 J. Neurosci. 33 4964

    [10]

    van Vreeswijk C, Sompolinsky H 1996 Science 274 1724

    [11]

    Deco G, Corbetta M 2011 The Neuroscientist 17 107

    [12]

    Park H J, Friston K 2013 Science 342 1238411

    [13]

    Isaacson J S, Scanziani M 2011 Neuron 72 231

    [14]

    Maass W, Joshi P, Sontag E D 2007 PLoS Comput. Biol. 3 e165

    [15]

    Shu Y, Hasenstaub A, McCormick D A 2003 Nature 423 288

    [16]

    Luz Y, Shamir M 2012 PLoS Comput. Biol. 8 e1002334

    [17]

    Turrigiano G G 2008 Cell 135 422

    [18]

    Woodin M A, Ganguly K, Poo M M 2003 Neuron 39 807

    [19]

    Jansen B H, Rit V G 1995 Biol. Cybern. 73 357

    [20]

    Wang J S, Xu Y 2014 Acta Phys. Sin. 63 068701 (in Chinese) [王俊松, 徐瑶 2014 物理学报 63 068701]

    [21]

    Chacron M J, Longtin A, Maler L 2005 Phys. Rev. E: Stat. Nonlinear Soft Matter Phys. 72 051917

    [22]

    Dayan P, Abbott L F 2001 Theoretical Neuroscience (Cambridge, MA: MIT Press) pp195-250

  • [1] 李瑞, 徐邦林, 周建芳, 姜恩华, 汪秉宏, 袁五届. 一种突触可塑性导致的觉醒-睡眠周期中突触强度变化和神经动力学转变. 物理学报, 2023, 72(24): 248706. doi: 10.7498/aps.72.20231037
    [2] 黎丽, 赵志国, 古华光. 兴奋性和抑制性自反馈压制靠近Hopf分岔的神经电活动比较. 物理学报, 2022, 71(5): 050504. doi: 10.7498/aps.71.20211829
    [3] 丁学利, 古华光, 贾冰, 李玉叶. 抑制性自突触诱发耦合Morris-Lecar神经元电活动的超前同步. 物理学报, 2021, 70(21): 218701. doi: 10.7498/aps.70.20210912
    [4] 华洪涛, 陆博, 古华光. 兴奋性自突触引起神经簇放电频率降低或增加的非线性机制. 物理学报, 2020, 69(9): 090502. doi: 10.7498/aps.69.20191709
    [5] 丁学利, 贾冰, 李玉叶. 利用相位响应曲线解释抑制性反馈增强神经电活动. 物理学报, 2019, 68(18): 180502. doi: 10.7498/aps.68.20190197
    [6] 陈义豪, 徐威, 王钰琪, 万相, 李岳峰, 梁定康, 陆立群, 刘鑫伟, 连晓娟, 胡二涛, 郭宇锋, 许剑光, 童祎, 肖建. 基于二维材料MXene的仿神经突触忆阻器的制备和长/短时程突触可塑性的实现. 物理学报, 2019, 68(9): 098501. doi: 10.7498/aps.68.20182306
    [7] 薛晓丹, 王美丽, 邵雨竹, 王俊松. 基于抑制性突触可塑性的神经元放电率自稳态机制. 物理学报, 2019, 68(7): 078701. doi: 10.7498/aps.68.20182234
    [8] 汪芃, 李倩昀, 黄志精, 唐国宁. 在兴奋-抑制混沌神经元网络中有序波的自发形成. 物理学报, 2018, 67(17): 170501. doi: 10.7498/aps.67.20180506
    [9] 曹奔, 关利南, 古华光. 兴奋性作用诱发神经簇放电个数不增反降的分岔机制. 物理学报, 2018, 67(24): 240502. doi: 10.7498/aps.67.20181675
    [10] 李国芳, 孙晓娟. 小世界神经元网络随机共振现象:混合突触和部分时滞的影响. 物理学报, 2017, 66(24): 240501. doi: 10.7498/aps.66.240501
    [11] 于文婷, 张娟, 唐军. 动态突触、神经耦合与时间延迟对神经元发放的影响. 物理学报, 2017, 66(20): 200201. doi: 10.7498/aps.66.200201
    [12] 丁学利, 李玉叶. 具有时滞的抑制性自突触诱发的神经放电的加周期分岔. 物理学报, 2016, 65(21): 210502. doi: 10.7498/aps.65.210502
    [13] 任国栋, 武刚, 马军, 陈旸. 一类自突触作用下神经元电路的设计和模拟. 物理学报, 2015, 64(5): 058702. doi: 10.7498/aps.64.058702
    [14] 夏小飞, 王俊松. 基于分岔理论的突触可塑性对神经群动力学特性调控规律研究. 物理学报, 2014, 63(14): 140503. doi: 10.7498/aps.63.140503
    [15] 陈军, 李春光. 具有自适应反馈突触的神经元模型中的混沌:电路设计. 物理学报, 2011, 60(5): 050503. doi: 10.7498/aps.60.050503
    [16] 于洪洁, 童伟君. 延迟自反馈控制Hindmarsh-Rose神经元的混沌运动. 物理学报, 2009, 58(5): 2977-2982. doi: 10.7498/aps.58.2977
    [17] 王朝庆, 徐 伟, 张娜敏, 李海泉. 色噪声激励下的FHN神经元系统. 物理学报, 2008, 57(2): 749-755. doi: 10.7498/aps.57.749
    [18] 乔晓艳, 李 刚, 董有尔, 贺秉军. 弱激光诱导神经元兴奋性改变的实验研究. 物理学报, 2008, 57(2): 1259-1265. doi: 10.7498/aps.57.1259
    [19] 王占山, 张化光. 时滞递归神经网络中神经抑制的作用. 物理学报, 2006, 55(11): 5674-5680. doi: 10.7498/aps.55.5674
    [20] 谢 勇, 徐健学, 康艳梅, 胡三觉, 段玉斌. 可兴奋性细胞混沌放电区间的识别机理. 物理学报, 2003, 52(5): 1112-1120. doi: 10.7498/aps.52.1112
计量
  • 文章访问数:  6783
  • PDF下载量:  468
  • 被引次数: 0
出版历程
  • 收稿日期:  2014-11-02
  • 修回日期:  2014-12-09
  • 刊出日期:  2015-05-05

/

返回文章
返回