不同耦合类型的三维Hybrid神经元同步动力学

神经元网络的信息传递与多个神经元间的耦合同步密切相关.大量研究表明,神经元耦合系统的同步化问题是研究大脑处理信息的关键.本文基于三维混合神经元模型研究了神经元的复杂放电,该模型由Wilson模型的快子系统和H-R模型的慢子系统组成.该模型能够重现大脑皮层神经元一系列包括规则峰放电、快速峰放电和簇放电等神经动力学行为.本文基于三维混合神经元模型,探讨了三维混合神经元在电突触耦合、化学突触耦合和磁通耦合条件下的同步放电行为,模拟膜电位序列图、相位差图等探讨恒同及非恒同下耦合强度对神经元同步放电的影响.本研究将为人们进一步了解神经系统疾病的发病机制提供指导和帮助,并为神经科学领域提供可能的研究思路.     

HH神经元的自抗扰同步研究

因外部电磁环境变化导致生物神经元放电节律不同步是生物控制中需要解决的一个难点问题。HH神经元模型是描述神经元动力学特性的第一个数学模型,以HH神经元为研究对象,利用各种现代控制方法获得HH神经元的同步,方法的不足在于对外部干扰的敏感性以及控制律复杂不易实现。为增强神经元同步的鲁棒性和可行性,利用自抗扰控制算法,在三种不同情况下研究HH神经元的同步,并且依次加入不同干扰验证其同步效果。仿真结果表明,自抗扰控制能够对神经元系统的总扰动进行实时估计和补偿,消除扰动对神经元系统同步的影响,从而获得良好的同步效果。     

应用Washout filter控制耦合神经元同步活动

以FitzHugh-Nagumo神经元为例,使用Simulink对耦合神经元的同步活动进行模拟研究,然后,通过引入Washout filter反馈控制,比较控制前后其放电行为,探究反馈控制作用的动力学机制,以及寻求其去同步的最优参数。Washout filter辅助反馈控制是非线性动力学研究的重要问题之一,它在理论上已经得到较多的研究。Washout filter反馈控制通过一个经过设计的控制输入处理同步化并改善耦合神经元的动力学特性参数。     

含时滞和Ih流的神经元的同步放电行为

本文研究了时滞和超极化激活的阳离子流Ih对抑制耦合的水蛭神经元的同步放电行为的调控.通过数值仿真揭示了时滞、耦合强度和Ih流都能诱发丰富的同步转迁行为,如从同步的周期-6簇放电到同步的周期-1簇放电.借助ISI分岔和快慢变量分离方法获得了Ih流诱导同步转迁行为的动力学原因.研究结果表明,时滞和Ih流都是影响水蛭神经元同步行为的重要因素.     

脑皮层神经网络的线性时滞反馈控制

神经元的病态同步放电会破坏大脑的正常功能, 导致癫痫和帕金森等生理疾病. 本文采用神经元二维映射模型构建一个脑皮层神经网络, 当神经元之间的耦合强度超过某一阈值时, 网络中所有神经元同步放电. 通过施加线性时滞反馈控制, 可以有效的消除这种同步状态, 且不改变神经元本身的放电特性. 仿真结果表明线性时滞反馈 可以实现对脑皮层神经网络的去同步化控制, 且对刺激参数的变化具有鲁棒性.     

关于正弦起搏器控制耦合神经元的去同步探索

以全同HR神经元的耦合系统为例,探索了通过加入正弦起搏器实现其去同步的问题.发现正弦起搏器不但可以减弱系统的同步性,还可以改变神经元的放电模式.对系统去同步情况进行了分类,用度量指标刻画系统的去同步程度,并进行了相应的数值模拟.结果表明:在一定参数范围内,系统的去同步效果对于神经元间耦合强度的敏感性远大于对起搏器的控制强度的敏感性.     

电磁刺激对FHN神经元系统的调控作用

本文系统地研究了外加电磁刺激对FitzHugh-Nagum。(FHN)神经元系统动力学行为的调控作用。首先,在强非线性电磁感应的作用下,FHN神经元对外加电磁刺激的响应呈现显著的非线性变化特点,不仅能够产生混沌的放电现象,而且还出现了不同放电模式之间的转迁。其次,在电磁感应的作用下,周期振g的电磁刺激对Newman-Watts小世界的神经元网络的脉冲放电频率和同步性都能够进行有效地调控,不仅提高了神经元网络对局部弱激励信号的探测和响应能力,而且能有效地控制网络时空斑图从相位同步到完全同步的演化。本文的研究揭示了电磁刺激对单个神经元和神经元网络系统动力学行为的显著调控能力,有待为生理上应用电磁刺激辅助治疗精神疾病提供理论指导。     

耦合Pre-Botzinger复合体中神经元的反相簇放电模式及同步研究

本文以pre-B(o)tzinger复合体中两个耦合兴奋性神经元为研究对象,分别研究了耦合神经元反相簇放电类型及其同步转迁.当钠电导参数在一定范围内变化时,耦合神经元分别表现为“sup-Hopf/fold cycle”型和“fold/fold cycle”型反相簇放电.通过计算耦合神经元的簇放电内的峰相位差,进而判定反相簇放电中的峰同步行为,并且研究了反相放电的类型与同步的关系.     

分段线性脉冲神经元模型的簇放电分析

应用一种二维分段线性脉冲神经元模型的简单计算特性,对其簇放电特性进行了详细研究。发现该模型表现出强迫和内禀两类簇放电模式,并分析了内禀簇放电的产生机理及分岔现象。在实验中,应用该模型对一类具有簇放电特性的皮层神经元进行了模拟。     

耦合Pre-B?tzinger复合体中神经元的反相簇放电模式及同步研究

本文以pre-B?tzinger复合体中两个耦合兴奋性神经元为研究对象,分别研究了耦合神经元反相簇放电类型及其同步转迁.当钠电导参数在一定范围内变化时,耦合神经元分别表现为"sup-Hopf/fold cycle"型和"fold/fold cycle"型反相簇放电.通过计算耦合神经元的簇放电内的峰相位差,进而判定反相簇放电中的峰同步行为,并且研究了反相放电的类型与同步的关系.     

Auto-associative memory with two-stage dynamics of nonmonotonicneurons

Dynamical properties of a neural auto-associative memory with two-stage neurons are investigated theoretically. The two-stage neuron is a model whose output is determined by a two-stage nonlinear function of the internal field of the neuron (internal field is a weighted sum of outputs of the other neurons). The model is general, including nonmonotonic neurons as well as monotonic ones. Recent studies on associative memory revealed superiority of nonmonotonic neurons to monotonic ones. The present paper supplies theoretical verification on the high performance of nonmonotonic neurons and proves that the capacity of the auto-associative memory with two-stage neurons is O(n/ radicallog n), in contrast to O(n/log n) of simple threshold neurons. There is also a discussion of recall processes, where the radius of basin of attraction of memorized patterns is clarified. An intuitive explanation on why the performance is improved by nonmonotonic neurons is also provided by showing the correspondence of the recall processes of the two-stage-neuron net and orthogonal learning.     

Activation of neuronal ensembles via controlled synchronization

In this contribution we present the activation of neuronal ensembles of Hindmarsh-Rose neurons by controlled synchronization. The main problem consists in to impose a particular spiking-bursting behavior in all the neurons of the network. We consider a network where the neurons are in its resting state, it is desired that the neurons change their resting state to a particular behavior of activation, dictated by a neuron called the reference neuron. The goal is reached by controlling some neurons in the network controlling only the membrane potential (electrical synapse). The key feature of the present contribution is that by controlling a small number of neurons in the network a desired behavior is induced in all the neurons in the network despite its network topology. The important parameters are the control gain and the coupling strength, thus the activation of the network lays down on a compromise between the control gain and the coupling strength.     

Faithful representation of stimuli with a population of integrate-and-fire neurons

We consider a formal model of stimulus encoding with a circuit consisting of a bank of filters and an ensemble of integrate-and-fire neurons. Such models arise in olfactory systems, vision, and hearing. We demonstrate that bandlimited stimuli can be faithfully represented with spike trains generated by the ensemble of neurons. We provide a stimulus reconstruction scheme based on the spike times of the ensemble of neurons and derive conditions for perfect recovery. The key result calls for the spike density of the neural population to be above the Nyquist rate. We also show that recovery is perfect if the number of neurons in the population is larger than a threshold value. Increasing the number of neurons to achieve a faithful representation of the sensory world is consistent with basic neurobiological thought. Finally we demonstrate that in general, the problem of faithful recovery of stimuli from the spike train of single neurons is ill posed. The stimulus can be recovered, however, from the information contained in the spike train of a population of neurons.     

Spatiotemporal spike encoding of a continuous external signal

Interspike intervals of spikes emitted from an integrator neuron model of sensory neurons can encode input information represented as a continuous signal from a deterministic system. If a real brain uses spike timing as a means of information processing, other neurons receiving spatiotemporal spikes from such sensory neurons must also be capable of treating information included in deterministic interspike intervals. In this article, we examine functions of neurons modeling cortical neurons receiving spatiotemporal spikes from many sensory neurons. We show that such neuron models can encode stimulus information passed from the sensory model neurons in the form of interspike intervals. Each sensory neuron connected to the cortical neuron contributes equally to the information collection by the cortical neuron. Although the incident spike train to the cortical neuron is a superimposition of spike trains from many sensory neurons, it need not be decomposed into spike trains according to the input neurons. These results are also preserved for generalizations of sensory neurons such as a small amount of leak, noise, inhomogeneity in firing rates, or biases introduced in the phase distributions.     

Controlling Relations between the Individuality and Collectivity of Neurons and its Application to Self-Organizing Maps

In this paper, we consider a society of neurons where different types of neurons interact with each other. For the first approximation to this society, we suppose two types of neurons, namely, individually and collectively treated neurons. Just as individuality must be in harmony with collectivity in actual societies, individually treated neurons must cooperate with collectively treated neurons as much as possible. We here realize this cooperation by making individually treated neurons as similar to collectively treated neurons as possible. The difference between individually and collectively treated neurons is represented by the Kullback–Leibler divergence. This divergence is minimized using free energy minimization. We applied the method to three problems from the well-known machine learning database, namely wine and protein classification, and the image segmentation problem. In all three problems, we succeeded in producing clearer class structures than those obtainable using the conventional SOM. However, we observed that the fidelity to input patterns deteriorated. For this problem, we found that careful treatment of learning processes were needed to keep fidelity to input patterns at an acceptable level.     

关于视觉注意力选择的神经编码

本文提出了一种改进的注意力选择模型,在这个模型中,周边神经元代表初级视觉皮层的神经元,中心神经元代表更高级视觉皮层中的神经元.生理实验发现方向选择性是初级视觉皮层神经元的重要特性之一,所以模型除了考虑外部刺激的强度,也考虑了初级视觉皮层中的神经元的方向选择性.仿真结果显示改进后的模型能够选择具有不同方向选择性的目标,并且能从一个目标转移到另一个目标.和原模型相比,改进后的模型更符合生理背景.该模型的动力学分析结果,对于理解视觉神经系统的编码有一定的帮助.     

基于模糊聚类方法的神经元形态分类识别

神经元形态分类识别是"人类脑计划"研究首要解决的问题。神经元真实形态复杂多样,利用物理观察和日常经验无法进行分类识别,传统的分类识别算法难以解决形态相似的神经元分类识别的误判现象。针对神经元形态分类误判与类别重叠问题,提出神经元几何形态特征提取方法,设计神经元形态特征自由分类模型,从而为神经元的精确分类、有效识别与新型命名提供方法支持和实践参考。实验结果表明,该分类模型具有较高的运行效率和聚类精度,较好地解决了分类误判和类别重叠问题。     

模糊逻辑神经元研究进展

模糊逻辑神经元作为模糊神经网络的重要组成部分,一直以来备受关注,从模型设计到算法研究,成果颇多。以往的研究多集中于模糊逻辑神经元在某一方面的应用模型、性质和算法,具有突出的针对性和特殊性;而今的研究多立足于构造一种能够包容各种逻辑形态的通用神经元,以体现思维的灵活性和多样性,进而提高神经元的推广和应用价值。针对当前模糊逻辑神经元的研究进展做了综述,分析了前后两类不同模型的典型结构及特点,并明确了未来的发展方向。     

Stochastic synchrony of chaos in a pulse-coupled neural network with both chemical and electrical synapses among inhibitory neurons

The synchronous firing of neurons in a pulse-coupled neural network composed of excitatory and inhibitory neurons is analyzed. The neurons are connected by both chemical synapses and electrical synapses among the inhibitory neurons. When electrical synapses are introduced, periodically synchronized firing as well as chaotically synchronized firing is widely observed. Moreover, we find stochastic synchrony where the ensemble-averaged dynamics shows synchronization in the network but each neuron has a low firing rate and the firing of the neurons seems to be stochastic. Stochastic synchrony of chaos corresponding to a chaotic attractor is also found.     

小脑模型CMAC网络结构及有关参数的确定

从映射的角度分析了CMAC模型各层神经元之间的关系，根据网络输入向量的量化级数、泛化参数、相邻量化级引起的重叠神经元的个数，从理论上给出了虚拟层神经元数目的范围。对于存在实际层神经元的CMAC模型，讨论了压缩映射对网络学习收敛性的影响。最后通过机器手逆运动学问题的仿真实验，进一步比较说明了在从虚拟层神经元到实际层神经元的压缩映射中不同压缩比对网络学习收敛性及系统运行精度的影响。