中文版 | English
题名

基于隐变量模型的体外神经元群体活动 预测与调控

其他题名
PREDICTING AND CONTROL OF IN VITRO NEURONAL POPULATION ACTIVITY BASED ON LATENT VARIABLE MODELS
姓名
姓名拼音
HOU Runpeng
学号
12132626
学位类型
硕士
学位专业
0809 电子科学与技术
学科门类/专业学位类别
08 工学
导师
刘泉影
导师单位
生物医学工程系
论文答辩日期
2024-05-08
论文提交日期
2024-07-06
学位授予单位
南方科技大学
学位授予地点
深圳
摘要

神经元群体活动的动力学建模与调控在神经科学和神经工程领域具有重要研究意义,为理解大脑活动的基本原理、计算机制和调控响应机制提供了重要的技术支持。神经元群体活动的动力学建模是指通过建立数学或计算模型来描述神经元群体的集体行为和活动模式,目的是揭示神经元群体之间的相互作用、信号传递方式以及神经网络的动态特性。而神经元群体活动调控是指通过外部刺激或内部调节机制来改变或者操控神经元的活动状态和功能的过程,其对于促进神经元群体功能和治疗多种大脑异常功能具有重要的指导意义。
    随着神经信号记录技术的快速发展,研究人员能够直接采集神经元群体的活动并针对高通道的神经元群体活动进行分析,其中计算建模技术可以加速这一进程。近年来,神经计算建模技术推动了包括神经元网络模型、动力学模型、机器学习模型等在神经元群体活动建模中的运用;伴随着深度学习发展,基于数据驱动的机器学习算法在高维数据的端对端建模中也有优异表现,不过存在着缺乏生物信息先验和可解释性不足的局限。本研究在过去研究的基础上,试图探索一种用于理解体外神经元群体活动的模型,并用于神经元群体的精确神经调控。
    
    当前神经元群体活动的建模与调控存在以下关键技术问题:(1)电刺激调控机制尚不清晰,需构建关于神经元群体的刺激输入-神经响应关系的模型;(2)缺乏神经建模引导的精准神经调控范式。由于在体神经元实验刺激调控常受到伦理限制,本研究拟针对体外培养的神经元群体开展相关研究与技术验证,并基于隐变量模型对体外培养的神经元群体活动进行时序动态预测与调控研究,旨在为实现精确神经调控提供新的技术思路与方法。本研究借鉴Dale原理,采取一种生物启发式的循环神经网络模型,用于重构群体神经元网络在外部刺激下的神经响应动力学。模型包括自编码器和兴奋性-抑制性循环神经网络(Excitatory and Inhibitory Recurrent Neural Network, EI-RNN),通过构建的自编码器对多电极阵列记录到的电信号进行编解码,在电生理信号观测空间与虚拟神经元之间构建对应的隐空间映射关系。兴奋性-抑制性循环神经网络引入了兴奋性抑制性神经元连接以及稀疏性先验,在实验中可以准确预测由输入刺激变化导致的网络动态变化。

    进一步的,本研究对训练后的兴奋性-抑制性循环神经网络开展系统性质的分析,并证明了训练后的模型具有边缘稳定性,同时可以准确预测神经群体对电刺激的神经响应。在此基础上,我们构建了基于模型的神经调控框架。并通过遍历有效刺激参数,获得神经元群体活动的可到达范围,指导调控神经元群体状态的设置。同时,本研究进行了开环实验与闭环调控实验,分别实现了将神经元群体活动提高至目标响应的状态的任务以及对于目标神经元群体较长时间的稳定调控。

    综上所述,基于隐变量模型的体外神经元群体活动的预测与调控在神经科学探索和应用领域有着广泛的前景。本文的研究成果借助数学建模思想(隐变量模型、动力学方程和优化控制理论等)和神经科学理论,提升了数据驱动模型对体外神经元群体活动精确建模的生物可解释性。同时,基于构建的隐变量模型能够辅助开展关于系统的稳定性、可控性等数值分析,并指导精确的神经调控。该研究为实现精确的神经调控提供了重要的理论基础和理论框架。

关键词
语种
中文
培养类别
独立培养
入学年份
2021
学位授予年份
2024-06
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