随机信号在神经元网络中诱发的双空间相干共振

对确定性行为为静息的神经元网络施加随机信号进行控制,随着信号强度的增加,网络行为由无序到有序的空间行为一螺旋波再到无序,螺旋波的结构由复杂到简单再到复杂到简单的交替,由网络行为的空间结构函数计算出的信噪比会两次达到极大值,即发生了两次空间相干共振,结果不仅展示了该随机信号控制下的网络的动力学行为,还为通过施加控制因素诱导产生空间共振来提高神经系统的信息处理能力提供了可能的方法．     

无标度生物神经元网络的相干共振分析

本文以神经元二维映射模型为节点,构造了一个具有无标度连接特性的生物神经元网络.用高斯白噪声模拟生物神经系统中的环境噪声,通过数字仿真研究了噪声对神经网络动力学行为的影响.噪声可以提高神经元的兴奋性,诱导神经元产生峰电位,当噪声强度为某一适当值时,峰电位序列的有序度可以达到最佳,从而产生相干共振现象.另外,研究表明无标度...     

神经元网络模型的弱信号随机共振检测研究

基于FitzHugh-Nagumo可兴奋细胞耦合后形成的神经元网络模型,对生物神经系统的弱周期信号随机共振检测机制进行研究。以加和网络的双层FHN神经元模型为例,对周期随机共振现象分别进行研究,并应用信噪比、互信息率对比评价方法,结合输出神经元动作电位的发放频率和幅值,从多个角度进行了定量和定性的描述和比较。实验结果表明,双层FHN神经元网络的随机共振响应优于单神经元的FHN模型,且具有更好的稳定性,可以在一定的噪声强度范围内对输入信号进行有效地检测。     

异质性和时滞作用下神经元网络的共振动力学

利用参数互异的Fitzhugh-Nagumo神经元构建了含耦合时滞的无标度神经元网络模型,通过数值模拟的方法,提出研究参数异质性和耦合时滞影响下神经元网络的共振动力学.结果发现,当耦合项中不含时滞时,适中的参数异质性能够使得神经元网络对外界弱周期信号的响应达到最优,即适中的参数异质性能够诱导神经元网络的共振响应,而且异质性诱导共振对耦合强度具有鲁棒性.更重要的是,耦合时滞对参数异质性作用下神经元网络的共振特性也有着显著性影响.当时滞约为信号周期的整数倍时,神经元网络能够周期性地出现共振现象,即适当的耦合时滞能够诱导神经元网络的多重共振,而且这种现象在异质性参数的适当范围内都能明显出现.     

模块神经元网络中耦合时滞诱导的簇同步转迁

利用Courbage-Nekorkin-Vdovin神经元构建含有耦合时滞的模块神经元网络模型,通过数值模拟研究了耦合强度及耦合时滞对模块神经元网络簇同步放电特性的影响.研究结果表明,适当大的耦合强度可以诱导模块神经元网络达到簇同步.同时,研究发现耦合时滞可以诱导模块神经元网络出现簇同步转迁,且当时滞大小约为网络中所有神经元平均振荡周期的整数倍数时,模块神经元网络的簇同步现象能够间歇性出现.此外,研究结果表明时滞诱导的簇同步转迁对子网络内的耦合强度、子网络间的连接概率具有鲁棒性.     

噪声诱使模块化神经元网络产生随机多共振现象

首先,研究了噪声在多模块神经元网络中诱导的随机多共振现象.随机多共振现象是指存在不同的噪声强度,系统在这些噪声强度下对阈下信号的响应达到局部最优.其次,以FitzHugh-Nagumo神经元构成的模块化神经元网络为研究对象,通过数值模拟发现,神经元网络的系统响应随着噪声强度的增加多次达到局部最优,即产生随机多共振现象.同时,通过分析神经元网络平均膜电位的时序图,发现噪声通过诱导神经元网络在一个周期内产生多次发放进而诱导多次共振.最后,我们分析了两个子网络中加入不同强度的噪声时,噪声诱导神经元网络中的随机多共振现象.结果显示,当两个子网络加入不同强度的噪声时,随机多共振现象也会产生.     

神经元模型中混合模式振荡动力学研究进展

混合模式振荡(mixed-mode oscillations以下简称MMOs)是产生于动力系统中的一种复杂的振荡模式,它在自然界中是普遍存在的.混合模式振荡由一系列的小振幅的振荡和大振幅的振荡共同组成,两种模式的振荡交替出现.文章介绍了在神经元系统中混合模式振荡的研究情况和研究方法,主要分析几何奇异摄动理论在动力系统中混合模式振荡的产生机理的作用,并且介绍前包钦格复合体、内嗅皮层的星状细胞和垂体细胞神经元及腺体细胞的混合模式振荡的动力学研究,简单说明其他神经元模型的混合模式振荡的研究情况.为以后的其他领域的混合模式振荡的研究提供了方法.     

论线性系统的反共振问题

本文指出,现有文献对反共振现象的机理解释有若干不妥之处.针对简谐激励下的两自由度系统稳态振动,分析其反共振的机理,阐述了原点反共振、跨点反共振的机理差异,以及反共振与固有振型节点之间的关系.以两个相同弹簧支撑的刚性杆简谐受迫振动为例,详细说明了上述机理和关系.     

混合突触作用下耦合时滞对模块神经元网络簇同步的影响

针对电突触耦合和化学突触耦合混合作用下含有耦合时滞的模块神经元网络,利用非线性动力学理论和数值仿真方法,探讨了耦合强度及耦合时滞对模块神经元网络簇同步特性的影响.结果发现,模块神经元网络中子网络内、子网络间的耦合强度都能促使簇放电神经元取得簇同步,但是时滞却对耦合诱导的簇同步具有显著的抑制作用.进一步的研究证实了本文所得的研究结果不依赖于子网络的数目与子网络的节点个数.需要指出的是,耦合时滞对神经元网络簇同步的抑制作用对治疗簇同步引发的一些神经性疾病(如帕金森病、癫痫等)具有一定的理论指导意义.     

Reconstruction of chaotic dynamics via a network of stochastic resonance neurons and its application to speech

Recently, a great deal of attention has been paid tostochastic resonance as a new framework to understand sensory mechanisms of biological systems. Stochastic resonance explains important properties of sensory neurons that accurately detect weak input stimuli by using a small amount of internal noise. In particular, Collins et al. reported that a network of stochastic resonance neurons gives rise to a robust sensory function for detecting a variety of complex input signals. In this study, we investigate effectiveness of such stochastic resonance neural networks to chaotic input signals. Using the Rössler equations, we analyze the network's capability to detect chaotic dynamics. We also apply the stochastic resonance network systems to speech signals, and examine a plausibility of the stochastic resonance neural network as a possible model for the human auditory system.     

Analysis of oscillations in a reciprocally inhibitory network with synaptic depression

We present and analyze a model of a two-cell reciprocally inhibitory network that oscillates. The principal mechanism of oscillation is short-term synaptic depression. Using a simple model of depression and analyzing the system in certain limits, we can derive analytical expressions for various features of the oscillation, including the parameter regime in which stable oscillations occur, as well as the period and amplitude of these oscillations. These expressions are functions of three parameters: the time constant of depression, the synaptic strengths, and the amount of tonic excitation the cells receive. We compare our analytical results with the output of numerical simulations and obtain good agreement between the two. Based on our analysis, we conclude that the oscillations in our network are qualitatively different from those in networks that oscillate due to postinhibitory rebound, spike-frequency adaptation, or other intrinsic (rather than synaptic) adaptational mechanisms. In particular, our network can oscillate only via the synaptic escape mode of Skinner, Kopell, and Marder (1994).     

Hopf bifurcation and oscillations in a communication network with heterogeneous delays

In this paper, we investigate stability, bifurcation and oscillations arising in a single-link communication network model with a large number of heterogeneous users adopting a Transmission Control Protocol (TCP)-like rate control scheme with an Active Queue Management (AQM) router. In the system considered, different user delays are known and fixed but taken from a given distribution. It is shown that for any given distribution of delays, there exists a critical amount of feedback (due to AQM) at which the equilibrium loses stability and a limit cycling solution develops via a Hopf bifurcation. The nature (criticality) of the bifurcation is investigated with the aid of Lyapunov-Schmidt perturbation method. The results of the analysis are numerically verified and provide valuable insights into dynamics of the AQM control system.     

神经元钙振荡的非线性动力学研究

在神经元的生理实验中经常观察到丰富的钙振荡模式,本文详细综述了产生这些现象的钙流交换机理和各类通道调节机理,以及描述这些生理机理的数学表达式.介绍三类典型的研究钙振荡的非线性动力学模型,即电压动力学与钙动力学相耦合的模型,多个钙存储单元之间钙流平衡的模型和考虑信使物质IP3的振荡与钙振荡相互作用的模型;并针对第一个模型简要地讨论其复杂的动力学行为;最后对神经元钙振荡的非线性动力学研究提出了一些展望.     

网络应用程序由IPv4向IPv6迁移策略的研究

本文从应用程序的角度来探讨IPv4网络向IPv6网络过渡的问题,着重论述Pv4网络应用程序向IPv6网络应用程序迁移的三种策略。在研究它们各自优缺点的基础上,得出在过渡时期如何正确使用它们的一些结论。同时,本文还探讨了设计协议无关的网络应用程序的关键的、具有共性的一些原则,对设计与开发协议无关的网络应用程序的具有指导意义。     

Input-driven oscillations in networks with excitatory and inhibitory neurons with dynamic synapses

Previous work has shown that networks of neurons with two coupled layers of excitatory and inhibitory neurons can reveal oscillatory activity. For example, B?rgers and Kopell (2003) have shown that oscillations occur when the excitatory neurons receive a sufficiently large input. A constant drive to the excitatory neurons is sufficient for oscillatory activity. Other studies (Doiron, Chacron, Maler, Longtin, & Bastian, 2003; Doiron, Lindner, Longtin, Maler, & Bastian, 2004) have shown that networks of neurons with two coupled layers of excitatory and inhibitory neurons reveal oscillatory activity only if the excitatory neurons receive correlated input, regardless of the amount of excitatory input. In this study, we show that these apparently contradictory results can be explained by the behavior of a single model operating in different regimes of parameter space. Moreover, we show that adding dynamic synapses in the inhibitory feedback loop provides a robust network behavior over a broad range of stimulus intensities, contrary to that of previous models. A remarkable property of the introduction of dynamic synapses is that the activity of the network reveals synchronized oscillatory components in the case of correlated input, but also reflects the temporal behavior of the input signal to the excitatory neurons. This allows the network to encode both the temporal characteristics of the input and the presence of spatial correlations in the input simultaneously.     

Reinforcement learning to train a cooperative network with both discrete and continuous output neurons

We propose a reinforcement learning algorithm to train a cooperative network with both discrete and continuous output neurons based on the finding that discrete and continuous motorneurons coexist in the gill-withdrawal neural network of the sea mollusk, Aplysia. The network was trained to control an inverted pendulum. Simulation experiments showed that the two output neurons had distinct but cooperative roles: the discrete output neuron was essential for fast learning while the continuous output neuron was necessary for learning fine control. To achieve both fast learning and fine control, the shape of the sigmoid function in the continuous output neuron should be set before learning.