Delay-aided stochastic multiresonances on scale-free FitzHugh-Nagumo neuronal networks

The stochastic resonance in paced time-delayed scale-free FitzHugh--Nagumo （FHN） neuronal networks is investigated. We show that an intermediate intensity of additive noise is able to optimally assist the pacemaker in imposing its rhythm on the whole ensemble. Furthermore, we reveal that appropriately tuned delays can induce stochastic multiresonances, appearing at every integer multiple of the pacemaker＇s oscillation period. We conclude that fine-tuned delay lengths and locally acting pacemakers are vital for ensuring optimal conditions for stochastic resonance on complex neuronal networks.

Spiral Wave in Small-World Networks of Hodgkin-Huxley Neurons

The effect of small-world connection and noise on of Hodgkin-Huxley neurons are investigated in detail. Some the formation and transition of spiral wave in the networks interesting results are found in our numerical studies, i） The quiescent neurons are activated to propagate electric signal to others by generating and developing spiral wave from spiral seed in small area. ii） A statistical factor is defined to describe the collective properties and phase transition induced by the topology of networks and noise, iii） Stable rotating spiral wave can be generated and keeps robust when the rewiring probability is below certain threshold, otherwise, spiral wave can not be developed from the spiral seed and spiral wave breakup occurs for a stable rotating spiral wave. iv） Gaussian white noise is introduced on the membrane of neurons to study the noise-induced phase transition on spiral wave in small-world networks of neurons. It is confirmed that Ganssian white noise plays active role in supporting and developing spiral wave in the networks of neurons, and appearance of smaller factor of synchronization indicates high possibility to induce spiral wave.

Delay-induced coherence bi-resonance-like behavior in stochastic Hodgkin-Huxley neuron networks

In this paper,we study how information transmission delays affect the spiking behavior of electrically coupled stochastic Hodgkin-Huxley (HH) neurons on Newman-Watts networks.It is found that the spiking behavior becomes the most regular at an optimal time delay,indicating the occurrence of delay-induced coherence resonance-like (CR-like) behavior.Interestingly,there are different CR-like types,depending on the membrane patch size of the neuron.For a smaller patch size,only single CR-like behavior occurs;while for a larger patch size,coherence bi-resonance-like (CBR) behavior appears.These findings show that the delay-induced CR-like behavior is closely related to the channel noise strength,and the coupled neurons may exhibit different spiking behaviors under the interplay of the channel noise and time delay.Therefore,the channel noise should be taken into account in the study of time delay-related spiking activity in stochastic HH neurons.This work provides new insight into the role of channel noise and information transmission delays in realistic neural systems.

Emergence of target waves in neuronal networks due to diverse forcing currents

The electric activities of neurons could be changed when ion channel block occurs in the neurons.External forcing currents with diversity are imposed on the regular network of Hodgkin-Huxley(HH) neuron,and target waves are induced to occupy the network.The forcing current I1 is imposed on neurons in a local region with m 0 ×m 0 nodes in the network,neurons in other nodes are imposed with another forcing current I2.Target wave could be developed to occupy the network when the gradient forcing current(I1-I2) exceeds certain threshold,and the formation of target wave is independent of the selection of boundary condition.It is also found that the developed target wave can decrease the negative effect of ion channel block and suppress the spiral wave,and thus channel noise is also considered.The potential mechanism of formation of target wave could be that the gradient forcing current(I1-I2) generates quasi-periodical signal in local area,and the propagation of quasi-periodical signal induces target-like wave due to mutual coupling among neurons in the network.

Effects of potassium channel blockage on inverse stochastic resonance in Hodgkin-Huxley neural systems

Inverse stochastic resonance(ISR)is a phenomenon in which the firing activity of a neuron is inhibited at a certain noise level.In this paper,the effects of potassium channel blockage on ISR in single Hodgkin-Huxley neurons and in small-world networks were investigated.For the single neuron,the ion channel noise-induced ISR phenomenon can occur only in a certain small range of potassium channel blockage ratio.Bifurcation analysis showed that this small range is the bistable region regulated by the external bias current.For small-world networks,the effect of non-homogeneous network blockage on ISR was investigated.The network blockage ratio was used to represent the proportion of potassium-channel-blocked neurons to total network neurons.It is found that an increase in network blockage ratio at small coupling strengths results in shorter ISR duration.When the coupling strength is increased,the ISR is more significant in the case of a large network blockage ratio.The ISR phenomenon is determined by the network blockage ratio,the coupling strength,and the ion channel noise.Our results will provide new perspectives on the observation of ISR in neuroscience experiments.

感觉系统检测弱信号的随机共振现象

目的通过信号检测理论和心理物理学实验来研究人体感觉系统中噪声增强感觉的现象。方法感觉系统可用一个由接收器部分和分类器部分组成的阈值检测器来模拟。其中 ,接收器部分不再被看作是线性单元或是单个非线性单元 ,而是被看作是由多个MacCulloch Pitts神经元构成的加性网络。使用百分比正确率和信号可检测度这 2个评价指标来模拟新模型的性能。采用心理物理学实验方法中的单择一的强迫选择法和二择一的强迫选择法来验证模拟结果。结果模拟结果显示 2个评价指标都表现出随机共振现象 ,实验在定性上说明了理论模型的正确性。结论感觉系统是通过随机共振现象来利用噪声增强感觉的 。

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。

脉冲发放神经网络建模

生物神经系统是一种脉冲发放神经网络系统。当前流行的人工神经网络模型与生物神经系统相比仍存在着较大差异。本文根据电生理学结果建立了适合于工程应用的脉冲发放神经网络模型。给出了输入－输出关系、突触联接关系、动态方程和学习功力学以及各种统计量的均值和方差。

兴奋性神经群体的非周期信息传输研究

研究了FitzHugh-Nagumo(FHN)可兴奋性神经元的小世界网络模型中非周期信息传输的问题。此小世界网络模型的每个节点都是一个FHN神经元,每个神经元的输入为具有一定相关时间的高斯非周期信号,而内部噪声为强度相同但相互独立的高斯白噪声。数值结果表明,随着噪声强度的增加,输入输出信号的相关系数出现了非周期随机共振现象。连接度对于非周期信号传输性能影响有限。对于两个不同小世界网络的互相关进行了分析以期提高信息传递率。这些研究结果对于复杂系统信号处理理论具有重要意义。

部分时滞诱发Watts-Strogatz小世界神经元网络产生随机多共振

已有研究显示时滞可诱发神经元网络产生随机多共振,但它们主要讨论了神经元间的耦合都存在时滞的情形.然而实际中,有些神经元间的信息传递是瞬时的或时滞很小可以忽略的,即神经元网络中只有部分神经元间的耦合具有时滞,简称部分时滞(若神经元网络内共有l条耦合边,其中有l1条耦合边是具有时滞的,而剩余的耦合边的时滞为零,则我们称这类时滞为部分时滞).本文以Watts-Strogatz小世界神经元网络为研究对象,主要讨论部分时滞对该神经元网络系统响应强度的影响.研究结果指出,系统响应强度随部分时滞的增加呈现多峰的变化态势,即部分时滞可诱发随机多共振现象;而且使系统响应强度达到最优水平的部分时滞的取值区间随随机时滞边概率的增加渐渐变窄,当随机时滞边概率足够大时,系统响应强度只有在时滞位于外界信号周期的整数倍附近才会达到最优.此外,我们还分析了随机连边概率和神经元网络中边的总数对部分时滞诱发的随机多共振现象的影响.结果显示,部分时滞诱发的随机多共振现象对随机连边概率具有一定的鲁棒性,而神经元网络中边的总数对部分时滞诱发的随机多共振的影响则较大.

Detection of ordered wave in the networks of neurons with changeable connection

The probability of long-range connection among neurons could be changeable in biological neuronal networks. In this paper, the probability of long-range connection between neurons is not fixed at a constant but varies in a numerical region (≤p0 ), and then the collective behaviors of neurons are detected. A statistical factor in the two-dimensional space is used to detect the phase transition and robustness of spiral wave in the active network of neurons. It is found that the development of spatiotem-poral pattern depends on the numerical region (≤p0 ) for the probability of long-range connection. Coherence resonance-like behavior is observed due to the fluctuation in the long-range probability. Spiral waves emerge to occupy the network of neurons under an optimized probability of long-range connection, and it shows certain robustness in weak channel noise.

突触神经随机汇池网络的信息传递研究

针对突触神经网络的信息传输问题,本文研究了饱和性突触神经随机汇池网络的信息传递特性,分析了网络内部噪声源对于互信息和刺激特定信息的影响,给出了互信息和刺激特定信息的数值计算方法,并对刺激特定信息分布进行实验研究。实验结果表明,在兴奋性和抑制性突触神经共同组成的异质类随机汇池网络中,存在适量噪声能够增强网络信息传递的随机共振现象,远离饱和区的信号幅值对神经元群体信息传递效率的贡献较大。本文的研究结果对进一步理解突触神经信号处理机制具有重要指导意义。

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.

FHN神经元小世界网络随机共振下的弱信号增强

考虑到生物神经系统显著的弱信号感知能力,提出一种基于概率随机重连的神经元动态连接小世界网络。首先为提高计算神经模型工作效率,本文改变了FHN神经元恢复变量和膜电位特征时间默认相等的思路,可灵活调整膜电位势函数的势垒高度以提高状态的跃迁概率;其次将改进后的FHN神经元作为网络节点,引入基于距离连接权重的动态突触的神经元节点互连模式,通过调整外部干扰和网络内部的噪声强度,使系统进入随机共振状态;最后根据FHN神经元输入输出信号之间的互相关系数,对网络各节点输出进行筛选和融合,从而实现弱信号增强还原。对多种形式的输入弱信号进行了实验,结果表明,本方案可以有效实现弱信号的还原和增强,不仅能保持周期信号频谱的一致性,还能使原始弱信号和系统输出信号的互相关系数保持在0.92以上。因此,基于FHN神经元的小世界网络,能够充分利用随机共振特性,为弱信号的增强复原也提供了一种新的思路。

基于随机放电神经元网络的彩色图像感知研究

本文基于随机共振原理和人脑感知物体色彩的基本生物物理过程,提出了一种低照度彩色图像增强的可解释算法.我们首先研究了电导基积分放电神经元网络中的随机共振现象,揭示了放电阈值、突触权重和集群规模对输出响应信噪比的影响,并识别出放电阈值是影响随机共振效应的关键参数.然后,在结合彩色图像视觉感知的生理过程的基础上,给出了一种基于随机放电神经元网络的彩色图像增强算法,并以峰值信噪比(PSNR)和自然图像质量评估(NIQE)作为提取最优增强图像的度量指标.注意到待增强的图像是非周期信号,因此,为了优化算法的性能,首次提出了一种基于亮度分布的分位数的阈值选取策略.数值实验结果表明,该算法的增强效果良好且性能稳定,并可用于军事探测和医学图像预处理等信号处理领域.

单交互Poisson输入下的随机Hodgkin-Huxely神经元网络

给出了一个单交互Poisson输入下的随机HH神经元网络模型,并讨论了单交互Poisson输入的噪声强度及两两相关性对该网络发放行为的影响.

Synchronization transition of a modular neural network containing subnetworks of different scales

Time delay and coupling strength are important factors that affect the synchronization of neural networks.In this study,a modular neural network containing subnetworks of different scales was constructed using the Hodgkin–Huxley(HH)neural model;i.e.,a small-scale random network was unidirectionally connected to a large-scale small-world network through chemical synapses.Time delays were found to induce multiple synchronization transitions in the network.An increase in coupling strength also promoted synchronization of the network when the time delay was an integer multiple of the firing period of a single neuron.Considering that time delays at different locations in a modular network may have different effects,we explored the influence of time delays within each subnetwork and between two subnetworks on the synchronization of modular networks.We found that when the subnetworks were well synchronized internally,an increase in the time delay within both subnetworks induced multiple synchronization transitions of their own.In addition,the synchronization state of the small-scale network affected the synchronization of the large-scale network.It was surprising to find that an increase in the time delay between the two subnetworks caused the synchronization factor of the modular network to vary periodically,but it had essentially no effect on the synchronization within the receiving subnetwork.By analyzing the phase difference between the two subnetworks,we found that the mechanism of the periodic variation of the synchronization factor of the modular network was the periodic variation of the phase difference.Finally,the generality of the results was demonstrated by investigating modular networks at different scales.

随机边界诱导Izhikevich神经元网络的时空模式转换

在随机边界条件下构建由200×200个Izhikevich神经元组成的方形网络,并利用计算机模拟计算方形网络的时空特性和同步因子,对神经元的放电模式、分岔现象以及方形网络的时空模式和同步性质进行研究。研究结果表明:在相同电流刺激和耦合强度下,由不同放电模式Izhikevich神经元构建的方形网络中,仅当神经元处于Regular Spiking放电模式下才能在网络中观察到螺旋波种子的出现和消失;对于其他放电模式(Fast Spiking,Chattering和Intrinsically Bursting)的Izhikevich神经元构建的方形网络,则无法观察到螺旋波种子的出现。当外界电流刺激恒定时,只有当神经元之间的耦合强度为中等大小时才可在方形网络中观察到螺旋波种子的出现和消亡,相对较小或较大的耦合强度不能诱导神经元网络出现螺旋波种子。对方形神经网络中的同步因子研究发现同步因子随耦合强度的变化存在类似“反共振”的形式。