丘脑底核—苍白球神经元群网络模型的动力学分析

丘脑底核、内苍白球以及外苍白球是基底核的重要组成核团,本文建立了三者相互作用的神经元群网络模型,并研究其动力学行为.在外部输入作用下,系统可以在低频和高频状态之间转换；增大纹状体对苍白球的抑制性输入导致系统产生低频同步振荡.结果表明系统的这些动力学行为与基底核的功能紧密相关.     

基于基底神经节机理的行为决策模型

基底神经节是大脑深部一系列神经功能核团的总称,主要与周围的大脑皮层、丘脑、小脑和脑干相连.基底神经节的主要功能是参与运动调节和随意运动控制,特别是行为的决策确定和动作选择.本文主要介绍基于基底神经节机理的行为决策确定模型,重点综述决策确定和动作选择的理论模型进展.首先介绍了基底神经节的解剖结构,即输入核、输出核、多巴胺系统和中继核,进而描述了基底神经节的功能连接(直接通路、间接通路和超直接通路),然后基于基底神经节的多个皮质-BG回路之间的相互作用,概述了决策模型在运动和行为选择等方面的理论模型,主要有Gurney的选择-控制模型、Humphries的神经元群模型、Frank的认知决策模型、Wang的选择决策模型和Rabinovich相空间动力学选择模型等.最后讨论了这些理论决策模型和大脑疾病(帕金森病)的相互关系等.     

基于基底神经节机理的行为决策模型

基底神经节是大脑深部一系列的神经功能核团的总称，主要与周围的大脑皮层，丘脑，小脑和脑干相连.基底神经节的主要功能是参与运动调节和随意运动控制，特别是行为的决策确定和动作选择.本文主要介绍基于基底神经节机理的行为决策确定模型，重点综述决策确定和动作选择的理论模型进展.这里首先介绍了基底神经节的解剖结构，即输入核、输出核、多巴胺系统和中继核，进而描述了基底神经节的功能连接(直接通路、间接通路和超直接通路)，然后基于基底神经节的多个皮质-BG回路之间的相互作用，概述了决策模型在运动和行为选择等方面的理论模型，主要有Gurney的选择-控制模型、Humphries的神经元群模型、Frank的认知决策模型、Wang X J的选择决策模型和Rabinovich相空间动力学选择模型等.最后讨论了这些理论决策模型和大脑疾病(帕金森病)的相互关系等.     

微电极立体定向手术脑组织实时识别技术研究

针对目前帕金森病损毁手术中,神经外科医生无法准确实时识别和判断脑组织内部边界以及微电极所在神经核团的位置,对临床微电极记录帕金森病患者的神经放电信号进行研究;利用数学分形理论中的盒子维数理论进行分析,提出了客观定量的识别组织边界的方法,提高了组织定位精度;经过临床神经外科专家论证和大鼠实验结果表明,该方法可有效地识别出沿微电极针道上脑组织核团的变化,判定组织边界,该方法可用于临床神经外科手术中辅助定位导向。     

基于小脑-基底神经节机理的行为认知计算模型

针对智能体的行为认知问题,提出一种小脑与基底神经节相互协调的行为认知计算模型。该模型核心为操作条件学习算法,包括评价机制、行为选择机制、取向机制及小脑与基底神经节的协调机制。初期的学习信号来自于下橄榄体和黑质两部分,在熵的意义上说明该算法是收敛的。采用该学习方法为自平衡两轮机器人建立运动神经认知系统,利用RBF网络逼近行为和评价网络。仿真实验表明该方法改善仅有基底神经节作用的行为-评价算法学习速度慢和失败次数多的问题,学习后期通过温度的不断降低,加快学习速度,震荡逐渐消失,改善学习效果。     

龙虾心脏神经节模型的动力学分析

通过对龙虾心脏神经节模型的研究,从非线性动力学角度对模型所产生的簇发放做了详细的分析,讨论了不同电生理参数条件下,模型簇发放中所蕴含着的丰富的动力学性质,如：峰峰间距(InterSpike Intervals,ISIs)的加周期分岔和倍周期分岔等.通过模型分析结果可进一步理解龙虾心脏神经节动作电位簇发放中所蕴含的丰富的发放模式和节律编码.     

利用ADAMS与MATLAB对ABS滑模变结构控制的仿真

为了减少车辆控制系统的开发时间和费用,联合仿真的方法受到越来越多的重视。将多体系统动力学与智能控制理论相结合,对汽车制动防抱死控制系统进行了研究。它结合了不同软件的优点,可以在设计阶段验证控制算法对车辆性能的影响。利用ADAMS/CAR建立了汽车整车多体动力学模型,利用MATLAB/Simulink建立了基于滑移率的滑模变结构防抱死控制系统,利用ADAMS/Controls接口进行模型的集成、系统仿真。结果证明联合仿真方法对ABS系统设计切实可行,该控制算法具有较强的实用性。     

人脑黑质神经核团的精确三维自动分割

随着MRI技术的发展,人脑黑质核团的MRI定量分析在帕金森病诊断中的应用价值越来越高。提出一种解剖先验知识为约束基于动态曲面模型和自适应区域增长的自动3D分割方法,来完成黑质形状结构的精确三维分割和提取。由此获得黑质的位置、形状和体积,以期辅助临床上对早期帕金森病的诊断和评价治疗效果。分割实验表明该方法精确性高,具有较强的自适应性。     

具有时滞-扩散作用的无线传感网络病毒传播模型的振荡动力学研究

研究了具有时滞扩散作用的无线传感网络病毒传播模型的振荡动力学行为。首先,在现有实验证据的基础上,通过引入时滞和扩散作用,建立了一个新的时滞偏微分方程模型,该模型能够很好地刻画无线传感网络病毒传播的现实结构。其次,将潜伏时滞作为分岔参数,通过分析正平衡点的线性化特征方程,推导出此正稳态解的稳定性以及Hopf分岔存在的充分条件。研究结果表明在潜伏时滞和扩散的共同作用下,该时滞扩散模型表现出空间均匀与空间非均匀的振荡形态,揭示了时滞和扩散作用对无线传感网络安全是有害的。最后,给出了数值模拟来验证所给理论结果的有效性。     

基于STDP学习规则的视网膜神经回路的特性

基于视网膜的生理解剖结构,构建了包括视锥细胞、水平细胞、双极细胞、AII无长突细胞、神经节细胞、外侧膝状体核和ON通路与OFF通路的视网膜神经回路模型,并在神经节细胞层和外侧膝状体核层的突触连接中引入STDP(Spike-Timing Dependent Plasticity)学习规则,通过添加单一图形刺激和交替图形刺激,比较神经节细胞和外侧膝状体核的电位发放、发放频率以及两者之间突触权重的变化,研究视网膜神经回路的信息传递特性.结果表明:构建的神经回路模型可有效地将光照强度信息转化为发放时序频率信息,且表现出生物视网膜的信息结构特性;STDP学习规则的引入使得外侧膝状体核层接收了相应的刺激模式并学习记忆了这种模式,且ON通路和OFF通路表现出学习独立性;STDP学习规则可以对交替出现的图形刺激,在突触权重的空间分布上进行叠加,且重叠部分的学习效果更加显著.     

Understanding Parkinsonian handwriting through a computational model of basal ganglia

Handwriting in Parkinson's disease (PD) is typically characterized by micrographia, jagged line contour, and unusual fluctuations in pen tip velocity. Although PD handwriting features have been used for diagnostics, they are not based on a signaling model of basal ganglia (BG). In this letter, we present a computational model of handwriting generation that highlights the role of BG. When PD conditions like reduced dopamine and altered dynamics of the subthalamic nucleus and globus pallidus externa subsystems are simulated, the handwriting produced by the model manifested characteristic PD handwriting distortions like micrographia and velocity fluctuations. Our approach to PD modeling is in tune with the perspective that PD is a dynamic disease.     

A computer model of neuronal pathways in the basal ganglia

Disorders of the basal ganglia and the extrapyramidal motor system exhibit an imbalance of neurotransmitter concentrations in affected neurons. For three synapses with dopamine, acetylcholine, and gamma-amino butyric acid (GABA), mathematical models of synaptic transmission are developed. To describe the kinetics of transmitter substances, compartment analysis is used. Membrane potential behaviour is described by the Hodgkin-Huxley equations with an additional equation accounting for a presynaptic calcium current mediating transmitter release. At the postsynaptic site, activated receptor molecules control the activity of ion channels, eliciting either inhibitory or excitatory postsynaptic potentials. A simple model of the feedback loop connecting the caudate nucleus and the substantia nigra is simulated on a digital computer using the simulation language ACSL. A comparison of the control case with a model of Parkinson's disease shows a shift of eigenvalues towards zero in the diseased state.     

Reinforcement-based decision making in corticostriatal circuits: mutual constraints by neurocomputational and diffusion models

In this letter, we examine the computational mechanisms of reinforce-ment-based decision making. We bridge the gap across multiple levels of analysis, from neural models of corticostriatal circuits-the basal ganglia (BG) model (Frank, 2005 , 2006 ) to simpler but mathematically tractable diffusion models of two-choice decision making. Specifically, we generated simulated data from the BG model and fit the diffusion model (Ratcliff, 1978 ) to it. The standard diffusion model fits underestimated response times under conditions of high response and reinforcement conflict. Follow-up fits showed good fits to the data both by increasing nondecision time and by raising decision thresholds as a function of conflict and by allowing this threshold to collapse with time. This profile captures the role and dynamics of the subthalamic nucleus in BG circuitry, and as such, parametric modulations of projection strengths from this nucleus were associated with parametric increases in decision boundary and its modulation by conflict. We then present data from a human reinforcement learning experiment involving decisions with low- and high-reinforcement conflict. Again, the standard model failed to fit the data, but we found that two variants similar to those that fit the BG model data fit the experimental data, thereby providing a convergence of theoretical accounts of complex interactive decision-making mechanisms consistent with available data. This work also demonstrates how to make modest modifications to diffusion models to summarize core computations of the BG model. The result is a better fit and understanding of reinforcement-based choice data than that which would have occurred with either model alone.     

Silicon implementation of a fuzzy neuron

This paper describes a fuzzy neuron chip which is the modification of an ordinary neuron model by fuzzy logic. The algebraic product of scaler input and connective weights in synapse is replaced by a fuzzy inner product. An excitatory connection is represented by a MIN (minimum) operation and an inhibitory connection by fuzzy logic complement followed by a MIN operation. While an ordinary neuron model is established only by leaning, the fuzzy neuron can be designed and optimized by learning. The fuzzy neuron is implemented in silicon wafer by a standard BiCMOS process. The chip is applied to a handwritten character recognition system and it exhibits very high-speed recognition (less than 500 ns)     

实用神经网络的生物模拟器 待续

本文介绍一种能模拟单个神经元、触突以及系统的模拟器Ｓｉｍｕｌａｔｏｒ。用它模拟在生物界所熟知的一种无脊椎软体类似海参的食肉类动作Ｐｌｅｕｒｏ－ｂｒａｎｃｈｅａ。它的各种形为如吞食食物，遇敌退缩等用Ｆｏｒｔｒａｎ７７版本编程并在Ｓｕｎ３／５０工作站上进行模拟获得了满意结果。     

Modeling basal ganglia for understanding Parkinsonian reaching movements

We present a computational model that highlights the role of basal ganglia (BG) in generating simple reaching movements. The model is cast within the reinforcement learning (RL) framework with correspondence between RL components and neuroanatomy as follows: dopamine signal of substantia nigra pars compacta as the temporal difference error, striatum as the substrate for the critic, and the motor cortex as the actor. A key feature of this neurobiological interpretation is our hypothesis that the indirect pathway is the explorer. Chaotic activity, originating from the indirect pathway part of the model, drives the wandering, exploratory movements of the arm. Thus, the direct pathway subserves exploitation, while the indirect pathway subserves exploration. The motor cortex becomes more and more independent of the corrective influence of BG as training progresses. Reaching trajectories show diminishing variability with training. Reaching movements associated with Parkinson's disease (PD) are simulated by reducing dopamine and degrading the complexity of indirect pathway dynamics by switching it from chaotic to periodic behavior. Under the simulated PD conditions, the arm exhibits PD motor symptoms like tremor, bradykinesia and undershooting. The model echoes the notion that PD is a dynamical disease.     

考虑STDP学习律的树突整合型神经元网络的放电同步

生物神经网络的同步被认为在大脑神经信息的处理过程中发挥了重要作用。本文以树突整合型(DHH)神经元网络为研究对象,在网络中考虑脉冲时间依赖可塑性(STDP)学习律,分析网络连接概率、外部扰动以及STDP学习律对网络同步性的影响。研究结果表明,STDP学习律对网络同步的影响不仅与连接概率有关,也与外部扰动有关。在兴奋性神经元网络中,连接概率增大可以增强网络的同步,外部扰动增大会减弱网络的同步,且在较大的外部扰动下STDP学习律对网络同步特性的增强作用减弱;而在抑制性DHH神经元网络中,连接概率增大,也可以增强网络的同步,且在较大的连接概率下,外部扰动增大,可以增强网络的同步,而STDP学习律对网络同步的增强作用也更加明显。     

Discrete dynamics of two neuron networks

The parameterized, discrete time dynamics of two neuron networks with additive neurons of sigmoidal type is studied with the focus on the dependence on four control parameters. These are the weights and bias terms of the network. Two types of bifurcations are found for these networks, and their bifurcation sets are displayed. For purely excitatory or inhibitory networks bifurcations from a stable fixed point to two stable fixed points coexisting with a stable period-two orbit are characteristic. Excitatory-inhibitory networks exhibit bifurcations from stable fixed points to stable period-four orbits. The relation to dynamics of single units with self-connections is discussed.