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神经系统疾病与认知动力学(II): 神经振荡与认知动力学

韩芳 樊登贵 张丽媛 王青云 顾晓春 王直杰

韩芳, 樊登贵, 张丽媛, 王青云, 顾晓春, 王直杰. 神经系统疾病与认知动力学(II): 神经振荡与认知动力学. 力学进展, 2022, 52(3): 587-622 doi: 10.6052/1000-0992-21-065
引用本文: 韩芳, 樊登贵, 张丽媛, 王青云, 顾晓春, 王直杰. 神经系统疾病与认知动力学(II): 神经振荡与认知动力学. 力学进展, 2022, 52(3): 587-622 doi: 10.6052/1000-0992-21-065
Han F, Fan D G, Zhang L Y, Wang Q Y, Gu X C, Wang Z J. Neurological disease and cognitive dynamics (II): Neural oscillations and cognitive dynamics. Advances in Mechanics, 2022, 52(3): 587-622 doi: 10.6052/1000-0992-21-065
Citation: Han F, Fan D G, Zhang L Y, Wang Q Y, Gu X C, Wang Z J. Neurological disease and cognitive dynamics (II): Neural oscillations and cognitive dynamics. Advances in Mechanics, 2022, 52(3): 587-622 doi: 10.6052/1000-0992-21-065

神经系统疾病与认知动力学(II): 神经振荡与认知动力学

doi: 10.6052/1000-0992-21-065
基金项目: 国家自然科学基金 (11972115, 11932003, 12072021, 12102014) 资助项目.
详细信息
    作者简介:

    王青云, 北京航空航天大学动力学与控制学科责任教授, 博导. 国家杰出青年科学基金获得者, 教育部“长江学者”特聘教授, 2011年入选教育部新世纪人才支持计划. 2020被评为爱思唯尔中国高被引学者, 2019年获得中国力学学会科学技术奖自然科学二等奖, 2012年获教育部高等学校科学研究优秀成果自然科学二等奖, 2009年获全国百篇优秀博士论文提名奖, 2009年获内蒙古自治区第七届青年科技奖, 2008年获北京大学优秀博士后奖. 中国力学学会第十、十一届动力学与控制专业委员会副主任, 神经动力学专业组组长, 中国力学学会理性力学专业委员会副主任, 北京市力学学会理事, 中国振动工程学会非线性振动专业委员会副主任, 生物控制和生物医学工程专业委员会委员, 国际杂志《International Journal of Bifurcation and Chaos》的Associate Editor, 《Applied Mathematics and Mechanics》特约编委, 《Cognitive Neurodynamics》编委, 北京航空航天大学学报编委, 高等教育出版社出版的《网络科学与工程丛书》编委. 长期从事神经动力学和调控方面的研究工作. 研究成果主要结合生物神经系统电生理活动和脑疾病临床表征的非线性特征, 建立理论模型揭示神经系统呈现的非线性动力学行为的本质机制, 发展精准定位脑疾病病灶的理论方法, 发展控制神经系统疾病的调控方法和技术, 为脑神经疾病临床诊疗提供可靠的理论基础

    通讯作者:

    nmqingyun@163.com

  • 中图分类号: R338.2+5, O415.6

Neurological disease and cognitive dynamics (II): Neural oscillations and cognitive dynamics

More Information
  • 摘要: 大脑神经系统具有从慢到快多种不同的振荡节律, 这些节律振荡被认为参与了大脑多种功能的实现, 其中高频的伽马同步振荡被认为与大脑的认知功能最为相关. 本文阐述了生物学实验方面关于伽马振荡及其功能的研究进展, 并针对实验中伽马振荡的频率敏感依赖于外部刺激特征的现象, 综述了基于神经网络模型进行变频伽马振荡及其认知功能的动力学建模研究工作, 解释了视觉刺激调控的变频率伽马振荡动力学产生机理, 提出了基于同步抑制增强全局放电率对比度的神经认知机制. 研究成果有助于理解神经系统同步振荡的产生机理及其认知作用, 为大脑认知原理以及类脑智能的研究奠定基础.

     

  • 图  1  神经振荡的脑电节律

    图  2  简单刺激调控的E/I神经网络. 红色和蓝色的圆分别表示E、I神经元集群 (400个兴奋性神经元和100个抑制性神经元) , 红色定向线表示兴奋性连接, 蓝色定向线表示抑制性连接. $ {w}_{\mathrm{E}\mathrm{E}} $$ {w}_{\mathrm{I}\mathrm{I}} $$ {w}_{\mathrm{E}\mathrm{I}} $$ {w}_{\mathrm{I}\mathrm{E}} $分别是E神经元连接到E神经元、I神经元连接到I神经元、E神经元连接到I神经元、I神经元连接到E神经元的突触权重. $ {I}_{\mathrm{E}}^{\mathrm{e}\mathrm{x}\mathrm{t}} $$ {I}_{\mathrm{I}}^{\mathrm{e}\mathrm{x}\mathrm{t}} $分别表示网络中E神经元和I神经元接收到的外部刺激输入 (Gu et al. 2021a)

    图  3  平衡和具有外部输入差异的E/I神经网络的仿真结果.左列、中列、右列图分别为$ \mathrm{\Delta }S=0 $ (S1 = S2 = 2.5 μA) 、$ \mathrm{\Delta }S < 0 $ (S1 = 2.5 μA, S2 = 3.1 μA) 和$ \mathrm{\Delta }S > 0 $ (S1 = 3.1 μA, S2 = 2.5 μA) 的情况. (a) (b) (c)神经元的放电时刻斑图, 编号为0 ~ 100的神经元是抑制性神经元, 编号为101 ~ 500的神经元是兴奋性神经元; (d) (e) (f)神经元平均群体活动的功率谱图 (这里采用的是相对功率, 即功率谱中每个频率分量的功率与所有频率分量的功率之和的比值) (Gu et al. 2021a)

    图  4  两种刺激调节方式下Gamma振荡和E、I神经元集群的外部输入差异之间的关系 (中心刺激对比度增加由增大S1、保持S2不变来实现, 周围刺激对比度增加由增大S2、保持S1不变来实现). (a) Gamma振荡的频率随外部输入差异的增大而增大; (b) Gamma振荡的峰值功率随外部输入差异的增大而增强 (Gu et al. 2021a)

    图  5  E/I 神经网络中 E 神经元集群和 I 神经元集群的平均放电率. (a) E 神经元具有不同输入时 E, I 神经元集群的放电率变化; (b) I 神经元具有不同输入时 E, I 神经元集群的放电率变化 (Gu et al. 2021a)

    图  6  E/I 神经网络中任意一个神经元的突触电流: (a) $ \mathrm{\Delta }S > 0 $的情形 (E 神经元和I 神经元的输入分别为 3.5 μA和 2.5 μA) ; (b) $ \mathrm{\Delta }S < 0 $的情形 (E 神经元和I 神经元的输入分别为2.5 μA和3.5 μA) . 图中黑色线表示该神经元接收到的兴奋性突触电流, 蓝色线表示该神经元接收到的抑制性突触电流, 红色线表示总突触电流, 为前二者之和. 网络平均总突触电流和伽马振荡的峰值功率之间的关系: (c) $ \mathrm{\Delta }S > 0 $的情形 (E 神经元和I 神经元的输入分别为 3.5 μA和 2.5 μA) ; (d) $ \mathrm{\Delta }S < 0 $的情形 (E 神经元和I 神经元的输入分别为2.5 μA和3.5 μA) (Gu et al. 2021a)

    图  7  视觉刺激调控的E/I神经网络的结构, 左侧示意图分别表示E神经元 (下方) 和I神经元 (上方) 的感受野的空间结构. 叠加在光栅刺激 (右上方) 上的感受野显示为两个椭圆区域, 黑色的表示OFF区域, 白色的表示ON区域 (Gu et al. 2019)

    图  8  光栅无灰度对比度的平衡E/I神经网络 (左列图) 、第一种调节方式下光栅具有40%灰度对比度的E/I神经网络 (中列图) 、第二种调节方式下光栅具有60%灰度对比度的E/I神经网络 (右列图) 的仿真结果. (a) (b) (c) 神经元的放电时刻斑图, 标号0 ~ 100的神经元是抑制性神经元, 标号101 ~ 500的神经元是兴奋性神经元; (d) (e) (f) 神经元平均群体活动的功率谱图 (Gu et al. 2019)

    图  9  光栅不同灰度对比度引发Gamma振荡的 (a) 峰值频率和 (b) 峰值功率 (灰度对比度调节方式: 同时增加亮条纹的灰度值$G_1$和减小暗条纹的灰度值$G_2$) (Gu et al. 2019)

    图  10  具有立柱结构的大规模复杂E/I神经网络. (a) 单个立柱中层内和层间神经元之间的连接; (b) V1 区的简化模型 (Kang et al. 2010) , 其中 E 表示兴奋性神经元集群, I 表示抑制性神经元集群, 箭头表示神经信号投射方向, 图中只画出 2 个功能柱; (c) 立柱和立柱之间神经元的连接结构 (Gu et al. 2021b)

    图  11  具有多层立柱结构的大规模复杂E/I神经网络产生的 Gamma 振荡. (a) 当外部输入为 S1 = 0.3、S2 = 0 时每层神经元的放电时刻斑图; (b) 当外部输入为 S1 = 0, S2 = 0.3时每层神经元的放电时刻斑图; 图 (a)(b)中红色点代表抑制性神经元的放电, 蓝色点代表兴奋性神经元的放电; (c) 分别对应于 (a) 和 (b) 的神经元群体活动的功率谱图 (Gu et al. 2021b)

    图  12  具有多层立柱结构的大规模复杂E/I神经网络中Gamma 振荡的振荡频率对输入差异的依赖性. 红色和蓝色曲线分别代表中心刺激对比度增加 (增大S1、保持S2为0) 和周围刺激对比度增加 (增大S2、保持S1为0) 的调节情形 (Gu et al. 2021b)

    图  13  (a) 前馈神经网络结构 (Bear & Connors, 2004) . 标有字母A-G的圆圈表示兴奋性神经元, 小黑点表示抑制性神经元; (b) 侧抑制循环神经网络结构. 标有字母a-d的抑制性神经元收到来自侧向兴奋性神经元 (标有字母A-E) 的反馈, 并侧向抑制相邻的兴奋性神经元 (Han et al. 2018)

    图  14  在兴奋性神经元“B”放电引起抑制性神经元“a”放电的情况下, 兴奋性神经元“A”和“B”的膜电位和接收的抑制性突触电流. (a) 神经元“B” “a”和“A”的放电, 黑点代表相应神经元的放电, 其中“A”未达到阈值电位而没有放电; (b) 神经元“A”的膜电位; (c) 神经元“B”的膜电位; (d) 神经元“A”接收的抑制性突触电流; (e) 神经元“B”接收的抑制性突触电流 (Han et al. 2018)

    图  15  抑制性神经元同步放电的循环E/I神经网络及其放电模式. (a) 由5个兴奋性神经元和5个全连接的同步抑制性神经元 (在虚线椭圆中) 组成的简单E/I网络; (b) 与 (a) 等效的E/I网络; (c) 图 (a) 中E/I神经网络的同步放电模式, 索引为1 ~ 5的神经元表示兴奋性神经元 “A”-“E”, 索引为6 ~ 10的神经元表示抑制性神经元 (Han et al. 2018)

    图  16  全局连接复杂E/I神经网络的结构 (Han et al. 2018)

    图  17  (a) (c) E/I神经网络的同步和部分同步状态, N = 65, 其中神经元1 ~ 50是兴奋性神经元, 神经元51 ~ 65是抑制性神经元; (b) (d) E/I神经网络的放电率对比度, 图中方形线表示纯兴奋性神经元 (断开抑制性连接) 的归一化放电率的标准偏差, 星形线表示全局耦合的E/I神经网络中的兴奋性神经元的归一化放电率的标准偏差, 点线表示E/I神经网络和纯兴奋性神经元网络的放电率对比度增强倍数 (Han et al. 2018)

    图  18  (a) 随机E/I网络结构. 该网络由一个大小为$ {N}_{e} $的兴奋性神经元集群和一个大小为$ {N}_{i} $的抑制性神经元集群组成. 网络中任意神经元连接到其他神经元的概率为$ \rho $. (b) 该网络产生的Gamma同步振荡 (Han et al. 2020)

    图  19  神经元输入无噪声情形下E/I神经网络的放电率对比度增强. (a) 纯兴奋性神经元网络里400个兴奋性神经元的放电率对比度可视化图像; (b) E/I神经网络中400个兴奋性神经元的放电率对比度可视化图像 (Han et al. 2020)

    图  20  信息编码效能指标$ {I}_{\mathrm{C}} $与以下参数的关系: 从抑制性神经元到兴奋性神经元的突触强度$ {g}^{\mathrm{I}\mathrm{E}} $, 从兴奋性神经元到抑制性神经元的突触强度$ {g}^{\mathrm{E}\mathrm{I}} $, 外部刺激的标准差$ {\delta }^{\mathrm{s}} $ (Han et al. 2020)

    表  1  侧循环抑制引起的局部放电率对比度增强 (Han et al. 2018)

    不同的结构放电率/归一化放电率
    ABCDE
    被分离的神经元24/0.81649/1.66712/0.40844/1.49718/0.612
    图13(b) 连接14/0.6342/1.890/041/1.8514/0.63
    下载: 导出CSV

    表  2  抑制性同步引起全局放电率对比度增强 (Han et al. 2018)

    不同的结构放电率/归一化放电率
    ABCDE
    被分离的神经元24/0.81649/1.66712/0.40844/1.49718/0.612
    图15(a) 连接14/0.68644/2.1570/036/1.7658/0.392
    下载: 导出CSV
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  • 收稿日期:  2021-12-13
  • 录用日期:  2022-01-19
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  • 刊出日期:  2022-09-25

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