FitzHugh-Nagumo神经元网络的同步振荡与联想记忆

本文研究由FitzHugh—Nagumo神经元所组成的脉动神经元网络的同步与联想记忆恢复。基于神经元微观生理结构，本文给出具有空间随机分布延时的神经元间耦合，而这种随机分布延时描述了脉动信号从突触前神经元到突触后神经元在轴突上传播所需要的时间。记忆由空时发放的神经元集群表达，在噪声涨落的作用下，系统取得了对不完整输入的记忆恢复。     

Effects of information transmission delay and channel blocking on synchronization in scale-free Hodgkin-Huxley neuronal networks

In this paper,we investigate the evolution of spatiotemporal patterns and synchronization transitions in dependence on the information transmission delay and ion channel blocking in scale-free neuronal networks.As the underlying model of neuronal dynamics,we use the Hodgkin-Huxley equations incorporating channel blocking and intrinsic noise.It is shown that delays play a significant yet subtle role in shaping the dynamics of neuronal networks.In particular,regions of irregular and regular propagating excitatory fronts related to the synchronization transitions appear intermittently as the delay increases.Moreover,the fraction of working sodium and potassium ion channels can also have a significant impact on the spatiotemporal dynamics of neuronal networks.As the fraction of blocked sodium channels increases,the frequency of excitatory events decreases,which in turn manifests as an increase in the neuronal synchrony that,however,is dysfunctional due to the virtual absence of large-amplitude excitations.Expectedly,we also show that larger coupling strengths improve synchronization irrespective of the information transmission delay and channel blocking.The presented results are also robust against the variation of the network size,thus providing insights that could facilitate understanding of the joint impact of ion channel blocking and information transmission delay on the spatiotemporal dynamics of neuronal networks.     

Dynamics of firing patterns, synchronization and resonances in neuronal electrical activities： experiments and analysis

Recent advances in the experimental and theoretical study of dynamics of neuronal electrical firing activities are reviewed. Firstly, some experimental phenomena of neuronal irregular firing patterns, especially chaotic and stochastic firing patterns, are presented, and practical nonlinear time analysis methods are introduced to distinguish deterministic and stochastic mechanism in time series. Secondly, the dynamics of electrical firing activities in a single neuron is concerned, namely, fast-slow dynamics analysis for classification and mechanism of various bursting patterns, one- or two-parameter bifurcation analysis for transitions of firing patterns, and stochastic dynamics of firing activities （stochastic and coherence resonances, integer multiple and other firing patterns induced by noise, etc.）. Thirdly, different types of synchronization of coupled neurons with electrical and chemical synapses are discussed. As noise and time delay are inevitable in nervous systems, it is found that noise and time delay may induce or enhance synchronization and change firing patterns of coupled neurons. Noise-induced resonance and spatiotemporal patterns in coupled neuronal networks are also demonstrated. Finally, some prospects are presented for future research. In consequence, the idea and methods of nonlinear dynamics are of great significance in exploration of dynamic processes and physiological functions of nervous systems.     

Multiple synchronization transitions due to periodic coupling strength in delayed Newman–Watts networks of chaotic bursting neurons

In this paper, we numerically study the effect of time-periodic coupling strength on the synchronization of firing activity in delayed Newman–Watts networks of chaotic bursting neurons. We first examine how the firing synchronization transitions induced by time delay under fixed coupling strength changes in the presence of time-periodic coupling strength, and then focus on how time-periodic coupling strength induces synchronization transitions in the networks. It is found that time delay can induce more synchronization transitions in the presence of time-periodic coupling strength compared to fixed coupling strength. As the frequency of time-periodic coupling strength is varied, the firing exhibits multiple synchronization transitions between spiking antiphase synchronization and in-phase synchronization of various firing behaviors including bursting, spiking, and both bursting and spiking, depending on the values of time delay. These results show that time-periodic coupling strength can increase the synchronization transitions by time delay and can induce multiple synchronization transitions of various firing behaviors in the neuronal networks. This means that time-periodic coupling strength plays an important role in the information processing and transmission in neural systems.     

Improving spatio-temporal focusing and source reconstruction through deconvolution

In this study, a technique is demonstrated to improve the ability of time reversal to both spatially and temporally focus, or compress, elastic wave energy, or to improve the quality of the reconstruction of the source signal. This method utilizes the deconvolution, or inverse filter, in single channel time reversal experiments in solids. Special attention is given to the necessary procedure for improving source signal reconstruction in real experimental conditions. It is also demonstrated theoretically and numerically that good temporal focusing implies that the radius in the spherically symmetric part of the spatial focus is small.     

用于冲击波温度测量的宽动态线性光学高温计

采用直线聚焦型光电倍增管（PMT）,研制了用于冲击波温度测量的8通道辐射高温计。改进后的PMT分压器电路使系统各通道脉冲输出线性均大于8 mA（-800 V高压）,饱和光强域值达到约100 W量级,响应时间（上升沿）小于3 ns。该系统对光谱辐亮度值的相对测量不确定度为约3%,已成功用于测量50 kK以内的高温辐射历史和冲击波温度。     

Prediction of thermal gradient in an air channel with presence of electrostatic field using artificial neural network

Free convection from cubical air channel equipped with copper plate was taken into consideration in the presence of electrostatic field as the channel position was varied. The paper examines the artificial neural network capability in modeling and prediction of five output parameters for plate temperatures as affected with four input parameters of heat flux, applied voltage, the inclination angle of channel, and inlet ambient temperature. The proposed network’s performance was measured with increasing number of neurons in hidden layer. The best network structure was found 4-20-5 with Levenberg–Marquardt training algorithm and mean squared error of 0.06366. The mean relative error for all output cases were <2.9 %. The best coefficient of determination was resulted at 30 cm from channel entrance section to the amount of 0.9807. The discrepancies of the results are chiefly attributed to 90° channel inclination angle. The network was able to predict accurately temperature trend of airflow and plate with voltage, heat flux, and channel positions.     

脉动型神经元网络的联想记忆与分割

以广泛讨论的Hodgk in-Hux ley神经元节点组成脉动神经元网络,从神经系统空时模式编码理论研究网络的记忆(或模式)存储与时间分割问题。给定一个输入模式,它是几种模式的叠加,网络能够以一部分神经元同步发放的形式一个接一个地在时间域分割出每一种模式。如果输入的模式是缺损的,系统能够把它们恢复成完好的原型,即神经网络的联想记忆功能。     

电磁场下神经元模型中混合簇的分岔机制

Pre-B?tzinger复合体是新生哺乳动物呼吸节律起源的关键部位, 是呼吸节律产生的中枢. 忆阻器的功能类似于神经元突触的可塑性, 可用其模拟磁通量.本文在Butera动力学模型的基础上引入刺激电流和磁通控制忆阻器, 分别研究这两个因素对单个pre-B?tzinger复合体神经元中混合簇放电模式的影响.通过无量纲化的方法对变量进行时间尺度分析, 结果表明, 模型包含3个不同的时间尺度.通过快慢分解和分岔分析研究了神经元混合簇放电产生和转迁的动力学机制.电流和磁通量都可以影响混合簇中胞体簇的个数, 减小电流和磁通量的值, 混合簇中胞体簇的个数也会相应减少, 并使簇的类型由"fold/homoclinic"型簇放电转迁为经由"fold/homoclinic"滞后环的"Hopf/Hopf"型簇放电.双参数分岔分析表明, 随着钙离子浓度的逐渐增加, 全系统轨线在鞍结分岔曲线和同宿轨分岔曲线之间来回跃迁, 是混合簇的产生分岔机制.全系统轨线在鞍结分岔曲线和同宿轨分岔曲线之间跃迁的次数, 与混合簇中胞体簇的个数相对应.      

Dynamical response,information transition and energy dependence in a neuron model driven by autapse

Autapses are a class of special synapses of neurons. In those neurons, their axons are not connected to the dendrites of other neurons but are attached to their own cell bodies. The output signal of a neuron feeds back to itself, thereby allowing the neuronal firing behavior to be self-tuned. Autapses can adjust the firing accuracy of a neuron and regulate the synchronization of a neuronal system. In this paper, we investigated the information capacity and energy efficiency of a Hodgkin–Huxley neuron in the noisy signal transmission process regulated by delayed inhibitory chemical autapse for different feedback strengths and delay times. We found that the information transmission, coding efficiency, and energy efficiency are maximized when the delay time is half of the input signal period. With the increase in the inhibitory strength of autapse, this maximization is increasingly obvious. Therefore, we propose that the inhibitory autaptic structure can serve as a mechanism and enable neural information processing to be energy efficient.

     

生物神经网络系统动力学与功能研究

生物神经系统是由数量极其巨大的神经元相互联结的信息网络系统,在生物体的感觉、认知和运动控制中发挥关键性的作用.首先介绍神经元、大脑和一些生物神经网络的生理结构和理论模型,然后分别介绍其放电活动和网络动态特性的一些重要问题,包括神经元的复杂放电模式、耦合神经元网络系统的同步活动和时空动力学、大脑联合皮层神经微回路的网络结构特征,以及工作记忆和抉择过程的动力学机制等. 最后对今后研究给出一些展望.      

A mathematical model for ATP-mediated calcium dynamics in vascular endothelial cells induced by fluid shear stress

In consideration of the mechanism for shear-stress-induced Ca^2＋ influx via ATP（adenosine triphosphate）-gated ion channel P2X4 in vascular endothelial cells, a modified model is proposed to describe the shear-stress-induced Ca^2＋ influx. It is affected both by the Ca^2＋ gradient across the cell membrane and extracellular ATP concentration on the cell surface. Meanwhile, a new static ATP release model is constructed by using published experimental data. Combining the modified intracellular calcium dynamics model with the new ATP release model, we establish a nonlinear Ca^2＋ dynamic system in vascular endothelial cells. The ATP-mediated calcium response in vascular endothelial cells subjected to shear stresses is analyzed by solving the governing equations of the integrated dynamic system. Numerical results show that the shear-stress-induced calcium response predicted by the proposed model is more consistent with the experimental observations than that predicted by existing models.     

A feasible neuron for estimating the magnetic field effect

Biological neurons are capable of encoding a variety of stimuli, and the synaptic plasticity can be enhanced for activating appropriate firing modes in the neural activities. Artificial neural circuits are effective to reproduce the main biophysical properties of neurons when the nonlinear circuits composed of reliable electronic components with distinct physical properties are tamed to generate similar firing patterns as biological neurons. In this paper, a simple neural circuit is proposed to estimate the effect of magnetic field on the neural activities by incorporating two physical electronic components. A magnetic flux-controlled memristor and an ideal Josephson junction in parallel connection are used to percept the induction currents induced by the magnetic field. The circuit equations are obtained according to the Kirchhoff’s theorem and an equivalent neuron model is acquired by applying scale transformation on the physical variables and parameters in the neural circuit. Standard bifurcation analysis is calculated to predict possible mode transition and evolution of firing patterns. The Hamilton energy is also obtained to find its dependence on the mode selection in electronic activities. Furthermore, External magnetic field is applied to estimate the mode transition of neural activities because the phase error and the junction current across the Josephson junction can be adjusted to change the dynamics of the neural circuit. It is found that the biophysical functional neuron can present rapid and sensitive response to external magnetic field. Nonlinear resonance is obtained when stochastic phase error is induced by external time-varying magnetic field. The neural circuit can be suitable for further calculating the collective behaviors of neurons exposed to magnetic field.

     

The race to the nociceptor: mechanical versus temperature effects in thermal pain of dental neurons

The sensing of hot and cold stimuli by dental neurons differs in several fundamental ways. These sensations have been characterized quantitatively through the measured time course of neural discharge signals that result from hot or cold stimuli applied to the teeth of animal models. Although various hypotheses have been proposed to explain the underlying mechanism, the ability to test competing hypotheses against experimental recorded data using biophysical models has been hindered by limitations in our understanding of the specific ion channels involved in nociception of dental neurons. Here we apply recent advances in established biophysical models to test the competing hypotheses. We show that a sharp shooting pain sensa-tion experienced shortly following cold stimulation cannot be attributed to the activation of thermosensitive ion channels, thereby falsifying the so-called neuronal hypothesis,which states that rapidly transduced sensations of coldness are related to thermosensitive ion channels. Our results support a central role of mechanosensitive ion channels and the associated hydrodynamic hypothesis. In addition to the hydrodynamic hypothesis, we also demonstrate that the long time delay of dental neuron responses after hot stimulation could be attributed to the neuronal hypothesis—that a relatively long time is required for the temperature around nociceptors to reach some threshold. The results are useful as a model of how multiphysical phenomena can be combined to provide mechanistic insight into different mechanisms underlying pain sensations.     

阵发型神经元网络的联想记忆与分割

以Hindmarsh-Rose(HR)神经元组成网络,研究这些网络的记忆(或模式)存储与时间分割问题．给定一个输入模式,它是几种模式的叠加,网络能够以一部分神经元同步发放的形式一个接一个地分割出每一种模式．如果输入的模式是缺损的,系统能够恢复为原型,即网络具有联想记忆功能．模拟也得到据作者所知至今还未报道的一些现象．     

基于多通道时间反转Lamb波的铝板小缺陷检测

超声Lamb波具有传播距离远、衰减小等特点,广泛应用于板结构的无损检测。将Lamb波检测技术与时间反转理论相结合,能提高铝板中小缺陷的检测能力。采用多通道时间反转板中Lamb波聚焦方法对1mm厚的铝板中直径0.8mm通孔缺陷进行检测,检测结果显示多通道时间反转检测信号中形成了两处明显的聚焦,即直达波和缺陷回波聚焦,证明此方法有效提高了检测分辨铝板小缺陷的能力。最后通过直达波、缺陷回波两处聚焦的时间差和Lamb波S_0模态群速度,准确实现了铝板中小缺陷的定位。多通道时间反转Lamb波聚焦方法不仅得到了缺陷检测回波,而且准确实现了缺陷的定位,达到了提高铝板中小缺陷检测能力的目的。     

Assessment of PIV to measure mean velocity and turbulence in open-channel flow

The complementary nature of PIV and LDV, in readily available configurations, is demonstrated along with their strengths and limitations by measurements in the flow over a two-dimensional dune in an open channel. This flow field is well suited to evaluate the relative performance of the two techniques as it contains much of the complexity found in practical hydraulic engineering. Agreement in the data obtained with the two techniques, even in regions of flow reversal and high shear, show that PIV is fast reaching a stage where it can be applied with a level of confidence similar to LDV.     

神经网络时滞系统非共振双Hopf分岔及其广义同步

本文建立了具有自连接和抑制-兴奋型他连接的两个同性神经元模型。其中自连接是由于兴奋型的突触产生,而他连接则分别对应于两神经元兴奋、抑制型的突触。发现如果有兴奋型自连接就会有双Hopf分岔,而没有时滞自连接时双Hopf分岔就会消失,因此自连接引起了双Hopf分岔。作为一个例子,通过变动连接中的时滞和他连接中的比重,1／√2双Hopf分岔得到了详细研究。通过中心流形约化,分岔点邻域内各种不同的动力学行为得到了分类,并以解析形式表出。神经元活动的分岔路径得以表明。从得到的解析近似解可以发现,本文所研究的具有兴奋一抑制型他连接的两相同神经元的节律不能完全同步而只能广义同步。时滞也可以使其节律消失,两神经元变为非活动的。这些结果在控制神经网络关联记忆和设计人工神经网络方面有着潜在的应用。     

Enhancement of synchronized chaotic state in a delay-coupled complex neuronal network

Nonlinear Dynamics - Synchronized firing of neurons is considered as one of the crucial characteristics for signal transmission and neuronal coding. In this article, spatiotemporal synchronization...     

Synchronization of inferior olive neurons via {\mathcal {L}}_1 adaptive feedback

This paper considers the synchronization of inferior olive neurons based on the \({\mathcal {L}}_1\) adaptive control theory. The ION model treated here is the cascade connection of two nonlinear subsystems, termed ZW and UV subsystems. It is assumed that the structure of the nonlinear functions and certain parameters of the IONs are not known, and disturbance inputs are present in the system. First, an \({\mathcal {L}}_1\) adaptive control system is designed to achieve global synchrony of the ZW subsystems using a single control input. This controller can accomplish local synchrony of the UV subsystems if the linearized UV subsystem is exponentially stable. For global synchrony of the UV subsystems, an \({\mathcal {L}}_1\) adaptive control law is designed. Each of these controllers includes a state predictor, an update law, and a control law. In the closed-loop system, global synchrony of the complete models of the IONs (the interconnected ZW and UV subsystems) is accomplished using these two adaptive controllers. Simulations results show that in the closed-loop system, the IONs are synchronized, despite unmodeled nonlinearities, disturbance inputs, and parameter uncertainties in the system.