基于泊松点过程的猕猴PMd与M1脑区脉冲神经信号关系建模

发布时间：2018-01-26 15:28

本文关键词： 神经元建模泊松点过程通用线性模型数值梯度下降　出处：《浙江大学》2017年硕士论文　论文类型：学位论文

【摘要】：神经元脉冲信号的建模与预测是神经科学领域的重要研究问题。通过神经元建模来分析脉冲信号的发放特点,有助于研究学者们更加深刻地理解大脑在执行高级认知任务中的工作方式以及神经信息在不同脑区之间的传递方式,从而对大脑的生理特性有一个更好的认识,乃至建立脑机融合的神经假体。本文通过对猕猴PMd脑区与M1脑区的神经元脉冲信号构建数理统计模型,来定性、定量地分析二者之间的功能联系。PMd脑区与M1脑区在猕猴的高级认知活动中具有重要作用,对这两个脑区的神经元进行建模,有助于研究学者们深入了解两个脑区协同工作的方式细节。脉冲信号建模存在诸多挑战。例如,神经元脉冲信号本身包含非常丰富的信号发放特性,需要模型具备足够强的表达能力来表征脉冲信号的多样性;除此之外,神经元所传送的信息包含在脉冲信号点过程序列之中,需要模型能够针对脉冲信号的点过程特性充分挖掘特征。本文以泊松通用线性模型为基础,针对这几个问题提出了若干改进,全文的贡献点归纳如下:1.本文借鉴集成学习中混合模型的思想,训练若干个弱表征能力的子模型,并对其进行混合构成完整模型,从而增强模型整体的表达能力;2.本文通过将泊松通用模型对应的目标函数由最大化似然函数转化为优化Discrete Time Rescaling Kolmogorov Smirnov统计量,借此增强模型对神经元脉冲信号点过程特性的考量;3.本文通过实验从不同角度验证所提出的模型的预测能力,实验结果表明本文模型在拟合优度角度能够保持一个比较突出的结果,同时模型本身维持着一个较好的生物解释性。
[Abstract]:The modeling and prediction of neuron pulse signal is an important research problem in the field of neuroscience. It is helpful for researchers to understand more deeply how the brain works in performing advanced cognitive tasks and how neural information is transmitted between different brain regions, so as to have a better understanding of the physiological characteristics of the brain. In this paper, a mathematical statistical model of neural pulse signals in the PMd and M1 brain regions of rhesus monkeys was constructed to determine the nature of the neural prosthesis. Quantitative analysis of the functional relationship between the two areas. PMd and M1 brain regions play an important role in the advanced cognitive activities of rhesus monkeys. The neurons in these two brain regions are modeled. It is helpful for researchers to understand the details of how the two brain regions work together. There are many challenges in the modeling of pulse signal. For example, the neuron pulse signal itself contains very rich signaling characteristics. It is necessary for the model to be strong enough to represent the diversity of pulse signals. In addition, the information transmitted by the neuron is contained in the pulse signal point process sequence, which requires that the model can fully mine the characteristics of the point process characteristics of the pulse signal. This paper is based on Poisson's general linear model. The contributions of this paper are summarized as follows: 1. This paper uses the idea of hybrid model in integrated learning to train several sub-models with weak representation ability. A complete model is constructed by mixing it to enhance the expression ability of the model as a whole. 2. In this paper, the objective function corresponding to Poisson's general model is transformed from maximum likelihood function to optimized Discrete Time Rescaling Kolmogorov. Smirnov statistics. The model is used to evaluate the process characteristics of neuron pulse signal points. 3. The prediction ability of the proposed model is verified by experiments from different angles. The experimental results show that the proposed model can maintain a relatively outstanding result in the goodness of fit angle. At the same time, the model itself maintains a better biological interpretation.
【学位授予单位】：浙江大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：Q42;TN911.6

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