脑运动神经系统的建模与辨识研究
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摘要
脑机接口是一种实现大脑直接与外界环境进行交流并进行控制的新技术。随着多通道神经元信号采集技术与计算机控制技术的日益成熟,如何从大脑皮层神经元群体活动中提取运动信息的解码算法是整个脑机接口系统实现脑信号与外界环境联系的关键纽带。本文针对脑运动神经系统的建模与辨识问题,深入研究了从大脑运动皮层神经元脉冲序列信号中提取关于生物具体运动行为信息的解码算法,以及从时间编码的角度分析神经元信号的方法。
本文首先研究了建立大脑运动皮层神经元信号与肢体运动方向关系模型的问题。提出了一种基于二叉树的多类支持向量机(SVM)分类方法,建立用群体神经元的放电频率模式预测手臂运动方向的模型。通过与常用的线性群体向量法(PVA)以及学习矢量量化(LVQ)方法比较,表明支持向量机方法具有较强的学习能力和推广能力,适用于样本数量较少的神经元信号分析。另外,还采用简化了计算复杂性的最小二乘支持向量机方法建模,性能与标准的支持向量机相似,并且运算时间较短,更适用于神经元信号的在线分析,有利于实现性能更高的用于神经康复的脑机接口系统。
然后,针对较为复杂的运动轨迹的建模问题,提出采用基于最小二乘支持向量机的非线性NARX模型,用群体神经元的放电频率模式预测三维空间中手臂运动轨迹的位置坐标。并且与线性的ARX模型以及基于ANN的NARX模型比较。表明非线性NARX模型比线性ARX模型能够更好地描述脑运动神经系统,而用LS-SVM算法建立的模型比ANN建立模型的预测精度更高,泛化能力更强。另外,还对实验记录的群体中的神经元进行了选择,使用相对较少的神经元信号实现了更精确的运动轨迹预测,并且有利于减少脑机接口系统的运算负荷。
为了能够直接分析神经元发放脉冲的时间信息,本文系统研究了Spiking神经网络(SNN)的神经元模型、网络结构、计算机仿真方法以及网络学习算法。在类似ANN中BP算法的SpikeProp网络学习算法的基础上,提出了两种改进方法:一种是用学习速率自适应调整和加动量项的方法来提高SNN的收敛速度和改善动态性能;另一种采用更接近生物神经元的SRM模型,更全面地考虑了神经元在发放脉冲后的状态变化,并采用BP算法在线调整神经元的不应期,使多脉冲发放的SNN传递信息的效率更高。
在研究Spiking神经网络的实现基础上,本文提出采用SNN方法,直接从大脑运动皮层神经元脉冲序列的时间模式中提取有关手运动方向以及手抓握角度的信息。通过单层和二层前向SNN分析运动皮层神经元活动的结果表明,SNN算法用于提取神经元脉冲序列中的时间信息是可行的,并且多层网络具有更高的计算能力。另一方面,与采用ANN分析神经元放电频率的结果比较表明,时间编码方法比频率编码方法更接近于实际生物神经系统处理信息的方式,利用SNN方法可以从被频率编码所忽视的脉冲的精确时间信息中找出包含在神经元脉冲序列中的与生物具体运动行为有关的更多信息。
最后,对全文进行了总结,并指出了在今后工作中需要进一步深入探讨的问题。
Brain-computer interface (BCI) is a new technology to realize the brain communication and controlling of the external environment. With the development of the multi-channel neural signal recoding and computer control technology, the decoding algorithms which extract movement information from the brain cortical neural ensemble activity is a key component to relate the brain signal with external environment. The aim of this dissertation is to identify and model the brain motor neural system. For this purpose, the decoding algorithms that extract movement information from the motor cortical neural spike trains and the methods to analyze the neural signals from the view of temporal coding are researched.
Firstly, the methods to model the relation between motor cortical neural signals and arm movement directions are researched. A multi-class support vector machines (SVM) algorithm of a binary tree recognition strategy is used to predict the movement direction with the firing rate patterns of neural ensemble. The performance of the SVM based neural activity recognition is compared with that of the linear population vector algorithm (PVA) and the learning vector quantization (LVQ) approach. The results show that the SVM method has better learning and generalization performance, which demonstrates that the SVM algorithm is a suitable approach for neural signals analyses. In addition, the least squares support vector machines (LS-SVM) method is also used to model the brain motor neural system. The LS-SVM algorithm not only has good performance similar to SVM, but also costs less computational time than SVM. That demonstrates the LS-SVM method is more suitable for online analyses of neural signals, and it holds hope for a possibly more accurate BCI for neural prosthesis.
Secondly, a nonlinear ARX (NARX) model based on LS-SVM is established to identify the relationship between the firing rate patterns of cortical neural ensemble and 3D hand positions. The results show that nonlinear NARX methods prevail against linear ARX method to model the motor cortical neural system. And the LS-SVM algorithm has higher prediction accuracy and better generalization performance than the ANN approach to build the nonlinear model. The best combinations of neurons are also selected from the entire neural ensemble for modeling. The results show that the model built with less neuron can achieve better performance. That can lead to BCI system demanding lower computational power.
To analyze the temporal patterns in neural spike trains, the spiking neural networks (SNN) which propagate information by the timing of individual spikes are studied in this dissertation. The spiking neural model, network architecture, simulation issues, and learning algorithm of SNN are systematically introduced. Two methods are presented to improve the SpikeProp algorithm, an error back-propagation (BP) learning rule suited for SNN. One method is to use the adaptive learning rates with momentum to speed up the convergence and dynamic performance of the SNN. Another is to present a more biologically plausible spiking response model (SRM) to describe the spiking neurons by not neglecting the dependence of the postsynaptic potential upon the firing times of the postsynaptic neuron, and to derive an additional BP learning rule for the coefficient of the refractoriness function. These improvements make the SNN in which neurons can spike multiple times can transfer information more efficiently.
Then the SNN method is proposed to extract movement direction and target orientation from the temporal pattern of the motor cortical neural spike trains directly. A one-layer and a two-layer feedforward SNN are used to analyze the activity of motor cortical neurons. The results show that the SNN algorithm is a feasible to analyze the timing of spike trains, and the multiple-layer network has higher computational power. On the other hand, the comparison results of the temporal pattern recognition by the SNN algorithm and the firing rate analysis by the ANN approach are consistent with the recent development about the neural coding that temporal coding is more biologically plausible than the rate coding. The SNN method is promising to extract more useful movement information from the neural spike trains without temporal information lost.
Finally, the summary of the dissertation and the future work to be investigated are presented.
本文首先研究了建立大脑运动皮层神经元信号与肢体运动方向关系模型的问题。提出了一种基于二叉树的多类支持向量机(SVM)分类方法,建立用群体神经元的放电频率模式预测手臂运动方向的模型。通过与常用的线性群体向量法(PVA)以及学习矢量量化(LVQ)方法比较,表明支持向量机方法具有较强的学习能力和推广能力,适用于样本数量较少的神经元信号分析。另外,还采用简化了计算复杂性的最小二乘支持向量机方法建模,性能与标准的支持向量机相似,并且运算时间较短,更适用于神经元信号的在线分析,有利于实现性能更高的用于神经康复的脑机接口系统。
然后,针对较为复杂的运动轨迹的建模问题,提出采用基于最小二乘支持向量机的非线性NARX模型,用群体神经元的放电频率模式预测三维空间中手臂运动轨迹的位置坐标。并且与线性的ARX模型以及基于ANN的NARX模型比较。表明非线性NARX模型比线性ARX模型能够更好地描述脑运动神经系统,而用LS-SVM算法建立的模型比ANN建立模型的预测精度更高,泛化能力更强。另外,还对实验记录的群体中的神经元进行了选择,使用相对较少的神经元信号实现了更精确的运动轨迹预测,并且有利于减少脑机接口系统的运算负荷。
为了能够直接分析神经元发放脉冲的时间信息,本文系统研究了Spiking神经网络(SNN)的神经元模型、网络结构、计算机仿真方法以及网络学习算法。在类似ANN中BP算法的SpikeProp网络学习算法的基础上,提出了两种改进方法:一种是用学习速率自适应调整和加动量项的方法来提高SNN的收敛速度和改善动态性能;另一种采用更接近生物神经元的SRM模型,更全面地考虑了神经元在发放脉冲后的状态变化,并采用BP算法在线调整神经元的不应期,使多脉冲发放的SNN传递信息的效率更高。
在研究Spiking神经网络的实现基础上,本文提出采用SNN方法,直接从大脑运动皮层神经元脉冲序列的时间模式中提取有关手运动方向以及手抓握角度的信息。通过单层和二层前向SNN分析运动皮层神经元活动的结果表明,SNN算法用于提取神经元脉冲序列中的时间信息是可行的,并且多层网络具有更高的计算能力。另一方面,与采用ANN分析神经元放电频率的结果比较表明,时间编码方法比频率编码方法更接近于实际生物神经系统处理信息的方式,利用SNN方法可以从被频率编码所忽视的脉冲的精确时间信息中找出包含在神经元脉冲序列中的与生物具体运动行为有关的更多信息。
最后,对全文进行了总结,并指出了在今后工作中需要进一步深入探讨的问题。
Brain-computer interface (BCI) is a new technology to realize the brain communication and controlling of the external environment. With the development of the multi-channel neural signal recoding and computer control technology, the decoding algorithms which extract movement information from the brain cortical neural ensemble activity is a key component to relate the brain signal with external environment. The aim of this dissertation is to identify and model the brain motor neural system. For this purpose, the decoding algorithms that extract movement information from the motor cortical neural spike trains and the methods to analyze the neural signals from the view of temporal coding are researched.
Firstly, the methods to model the relation between motor cortical neural signals and arm movement directions are researched. A multi-class support vector machines (SVM) algorithm of a binary tree recognition strategy is used to predict the movement direction with the firing rate patterns of neural ensemble. The performance of the SVM based neural activity recognition is compared with that of the linear population vector algorithm (PVA) and the learning vector quantization (LVQ) approach. The results show that the SVM method has better learning and generalization performance, which demonstrates that the SVM algorithm is a suitable approach for neural signals analyses. In addition, the least squares support vector machines (LS-SVM) method is also used to model the brain motor neural system. The LS-SVM algorithm not only has good performance similar to SVM, but also costs less computational time than SVM. That demonstrates the LS-SVM method is more suitable for online analyses of neural signals, and it holds hope for a possibly more accurate BCI for neural prosthesis.
Secondly, a nonlinear ARX (NARX) model based on LS-SVM is established to identify the relationship between the firing rate patterns of cortical neural ensemble and 3D hand positions. The results show that nonlinear NARX methods prevail against linear ARX method to model the motor cortical neural system. And the LS-SVM algorithm has higher prediction accuracy and better generalization performance than the ANN approach to build the nonlinear model. The best combinations of neurons are also selected from the entire neural ensemble for modeling. The results show that the model built with less neuron can achieve better performance. That can lead to BCI system demanding lower computational power.
To analyze the temporal patterns in neural spike trains, the spiking neural networks (SNN) which propagate information by the timing of individual spikes are studied in this dissertation. The spiking neural model, network architecture, simulation issues, and learning algorithm of SNN are systematically introduced. Two methods are presented to improve the SpikeProp algorithm, an error back-propagation (BP) learning rule suited for SNN. One method is to use the adaptive learning rates with momentum to speed up the convergence and dynamic performance of the SNN. Another is to present a more biologically plausible spiking response model (SRM) to describe the spiking neurons by not neglecting the dependence of the postsynaptic potential upon the firing times of the postsynaptic neuron, and to derive an additional BP learning rule for the coefficient of the refractoriness function. These improvements make the SNN in which neurons can spike multiple times can transfer information more efficiently.
Then the SNN method is proposed to extract movement direction and target orientation from the temporal pattern of the motor cortical neural spike trains directly. A one-layer and a two-layer feedforward SNN are used to analyze the activity of motor cortical neurons. The results show that the SNN algorithm is a feasible to analyze the timing of spike trains, and the multiple-layer network has higher computational power. On the other hand, the comparison results of the temporal pattern recognition by the SNN algorithm and the firing rate analysis by the ANN approach are consistent with the recent development about the neural coding that temporal coding is more biologically plausible than the rate coding. The SNN method is promising to extract more useful movement information from the neural spike trains without temporal information lost.
Finally, the summary of the dissertation and the future work to be investigated are presented.
引文
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