视皮层神经元活动的振荡和同步振荡现象

视皮层神经元电活动的振荡和同步振荡现象是神经生理学实验的最新发现之一.这一研究成果在认知科学界引起广泛关注.学术界对它在认知心理学上的解释、作用及意义有不同的评论.     

神经耦合振荡的量子孤波分析

为了分析神经耦合振荡产生频率同步的原因,在神经信息波动方程孤立子解的基础上讨论了神经耦合振荡方程组的稳定解。分别从同地和异地耦合孤波的同相和反相四种情况给出了孤波之间距离随时间的变化规律。阐述了大脑视觉皮层神经元集群之间的耦合振荡在神经孤波模式下呈现出的波粒二象性,体现了知觉具有量子化的特点。因而神经振荡必将引发集体突发,形成脑波频率同步。     

动态神经元网络模型的复杂性问题

在动态神经元网络模型中,当神经元总数仅为３时就观察到了非周期振荡。运用Ｌｅｍｐｅｌ和Ｚｉｖ提出的复杂性度量对这种现象进行了分析,结果表明对于其中一个神经元所发出的脉冲序列来说,至少直到１０００个脉冲为止还不能发现任何的周期性,并且其复杂性可以和由ｌｏｇｉｓｔｉｃ映射所产生的时间序列当其参数落在混沌区中时所具有的复杂性相比拟．这些结果也表明这种方法是所观察的时间范围内区分长周期振荡和非周期活动的好方法。结果还提示神经生理实验记录中所谓的噪声,其中有些可能是来源于生物神经元本身的非线性性质。     

与突触可塑性相关的神经振荡和信息流研究

作为一种有节律的神经活动,神经振荡现象发生在所有的神经系统中,例如大脑皮层、海马、皮层下神经核团以及感觉器官.本综述首先给出了已有的研究结果,即基于theta和gamma频段的同步神经振荡揭示了认知过程的起源与本质,如学习与记忆.然后介绍了关于神经振荡分析的新技术和算法,如表征神经元突触可塑性的神经信息流方向指数,并例...     

动态神经网络中的同步振荡

目前有一种假设认为同一视觉对象是由一群神经元的同步振荡活动来表征的。这一神经元发放活动的时间特性,是解决视觉信息处理中“结合问题（Ｂｉｎｄｉｎｇｐｒｏｂｌｅｍ）”的可能机制。本文用我们所提出的一种简化现实性神经网络模型［１］所构造的时滞非线性振子网络［２］,模拟生物神经网络的同步振荡活动。并考虑了振子各参数的设置与振荡活动的关系,以及网络振子间耦联对同步活动的影响．     

综述: 知觉相关的神经振荡-外界节律同步化现象

具有特定频率的节律性刺激能同步大脑内相应频率的神经振荡,使神经活动与外界刺激发生相位锁定,称之为神经振荡-外界节律同步化(neural entrainment).这种同步化的现象伴随着大脑内神经元集群兴奋水平的周期性波动,并与节律信息加工、知觉及注意等认知过程存在关联.得益于其非侵入、易操作以及能有效调控神经活动的特性,神经振荡-外界节律同步化成为了研究神经振荡与知觉和认知功能关系的有力手段,也为认知障碍诊断及干预提供了新的思路和方法.     

自涌动态神经元集群-脑的时空编码新概念

概述了关于脑的时空编码概念的演化,涉及到下面一些问题：１）皮层神经元是以放电速率还是以放电定时编码信息;２）Ｂａｒｌｏｗ的“祖母细胞”和Ｈｅｂｂ经典细胞集群面临的挑战;３）Ｍａｌｓｂｕｒｇ的脑功能的相关理论和神经活动的同步振荡;４）Ｈｏｐｆｉｅｌｄ的神经编码原理和自涌动态神经元集群新概念;５）Ｔｈｅｔａ节律,ｇａｍｍａ振荡和２００Ｈｚ锁相振荡与脑的“时间编码空间”;最后,给出了一些讨论。     

知觉相关的神经振荡-外界节律同步化现象

具有特定频率的节律性刺激能同步大脑内相应频率的神经振荡,使神经活动与外界刺激发生相位锁定,称之为神经振荡-外界节律同步化(neural entrainment).这种同步化的现象伴随着大脑内神经元集群兴奋水平的周期性波动,并与节律信息加工、知觉及注意等认知过程存在关联.得益于其非侵入、易操作以及能有效调控神经活动的特性,神经振荡-外界节律同步化成为了研究神经振荡与知觉和认知功能关系的有力手段,也为认知障碍诊断及干预提供了新的思路和方法.     

脉冲神经网络的振荡与分割

神经系统中的同步和振荡现象的发现,引起了人们对这些时序现象在生物信息处理中所起的机制的广泛关注。本文讨论了自组织脉冲神经网络的动态特性。在Ｇｅｒｓｔｎｅｒ等工作基础上,我们改进了脉冲响应神经元模型,并推导了多个模式在网络中共存振荡的条件,在这个条件下,网络具有良好的分割外界叠加刺激的能力。计算机模拟结果证实了我们的结论     

微弱电刺激对失眠者睡眠状况及睡眠脑电影响的初步研究

根据睡眠是由脑内亿万神经元同步振荡所刻划的观点［１］,及各种电刺激对动物睡眠影响的实验［２,３］,设计了用特定θ频率的正弦波微弱电流,刺激失眠病人颈部安眠２穴,以观察其对受试者脑电频率的客观影响。其结果是刺激后失眠病人由醒到２期的脑电记录中,θ波逐渐增加,增加了病人的总睡眠时间。这启示我们这种脑部的特殊频率微弱电流刺激,可能有引起脑部神经元群的共振现象,改变了受试者脑电中频率成分的分布特征,从而有助于失眠的治疗。这一现象是值得进一步研究的。     

Firing synchronization and temporal order in noisy neuronal networks

Noise-induced complete synchronization and frequency synchronization in coupled spiking and bursting neurons are studied firstly. The effects of noise and coupling are discussed. It is found that bursting neurons are easier to achieve firing synchronization than spiking ones, which means that bursting activities are more important for information transfer in neuronal networks. Secondly, the effects of noise on firing synchronization in a noisy map neuronal network are presented. Noise-induced synchronization and temporal order are investigated by means of the firing rate function and the order index. Firing synchronization and temporal order of excitatory neurons can be greatly enhanced by subthreshold stimuli with resonance frequency. Finally, it is concluded that random perturbations play an important role in firing activities and temporal order in neuronal networks.     

Local and global synchronization transitions induced by time delays in small-world neuronal networks with chemical synapses

Effects of time delay on the local and global synchronization in small-world neuronal networks with chemical synapses are investigated in this paper. Numerical results show that, for both excitatory and inhibitory coupling types, the information transmission delay can always induce synchronization transitions of spiking neurons in small-world networks. In particular, regions of in-phase and out-of-phase synchronization of connected neurons emerge intermittently as the synaptic delay increases. For excitatory coupling, all transitions to spiking synchronization occur approximately at integer multiples of the firing period of individual neurons; while for inhibitory coupling, these transitions appear at the odd multiples of the half of the firing period of neurons. More importantly, the local synchronization transition is more profound than the global synchronization transition, depending on the type of coupling synapse. For excitatory synapses, the local in-phase synchronization observed for some values of the delay also occur at a global scale; while for inhibitory ones, this synchronization, observed at the local scale, disappears at a global scale. Furthermore, the small-world structure can also affect the phase synchronization of neuronal networks. It is demonstrated that increasing the rewiring probability can always improve the global synchronization of neuronal activity, but has little effect on the local synchronization of neighboring neurons.     

Improved neuronal models for studying neural networks

Previous neuronal models used for the study of neural networks are considered. Equations are developed for a model which includes: 1) a normalized range of firing rates with decreased sensitivity at large excitatory or large inhibitory input levels, 2) a single rate constant for the increase in firing rate following step changes in the input, 3) one or more rate constants, as required to fit experimental data for the adaptation of firing rates to maintained inputs. Computed responses compare well with the types of neuronal responses observed experimentally. Depending on the parameters, overdamped increases and decreases, damped oscillatory or maintained oscillatory changes in firing rate are observed to step changes in the input. The integrodifferential equations describing the neuronal models can be represented by a set of first-order differential equations. Steady-state solutions for these equations can be obtained for constant inputs, as well as the stability of the solutions to small perturbations. The linear frequency response function is derived for sufficiently small time-varying inputs. The linear responses are also compared with the computed solutions for larger non-linear responses.     

Synchronization and sensitivity enhancement of the Hodgkin-Huxley neurons due to inhibitory inputs

Recent experimental results imply that inhibitory postsynaptic potentials can play a functional role in realizing synchronization of neuronal firing in the brain. In order to examine the relation between inhibition and synchronous firing of neurons theoretically, we analyze possible effects of synchronization and sensitivity enhancement caused by inhibitory inputs to neurons with a biologically realistic model of the Hodgkin-Huxley equations. The result shows that, after an inhibitory spike, the firing probability of a single postsynaptic neuron exposed to random excitatory background activity oscillates with time. The oscillation of the firing probability can be related to synchronous firing of neurons receiving an inhibitory spike simultaneously. Further, we show that when an inhibitory spike input precedes an excitatory spike input, the presence of such preceding inhibition raises the firing probability peak of the neuron after the excitatory input. The result indicates that an inhibitory spike input can enhance the sensitivity of the postsynaptic neuron to the following excitatory spike input. Two neural network models based on these effects on postsynaptic neurons caused by inhibitory inputs are proposed to demonstrate possible mechanisms of detecting particular spatiotemporal spike patterns. Received: 15 April 1999 /Accepted in revised form: 25 November 1999     

Potential Mechanisms for Imperfect Synchronization in Parkinsonian Basal Ganglia

Neural activity in the brain of parkinsonian patients is characterized by the intermittently synchronized oscillatory dynamics. This imperfect synchronization, observed in the beta frequency band, is believed to be related to the hypokinetic motor symptoms of the disorder. Our study explores potential mechanisms behind this intermittent synchrony. We study the response of a bursting pallidal neuron to different patterns of synaptic input from subthalamic nucleus (STN) neuron. We show how external globus pallidus (GPe) neuron is sensitive to the phase of the input from the STN cell and can exhibit intermittent phase-locking with the input in the beta band. The temporal properties of this intermittent phase-locking show similarities to the intermittent synchronization observed in experiments. We also study the synchronization of GPe cells to synaptic input from the STN cell with dependence on the dopamine-modulated parameters. Earlier studies showed how the strengthening of dopamine-modulated coupling may lead to transitions from non-synchronized to partially synchronized dynamics, typical in Parkinson''s disease. However, dopamine also affects the cellular properties of neurons. We show how the changes in firing patterns of STN neuron due to the lack of dopamine may lead to transition from a lower to a higher coherent state, roughly matching the synchrony levels observed in basal ganglia in normal and parkinsonian states. The intermittent nature of the neural beta band synchrony in Parkinson''s disease is achieved in the model due to the interplay of the timing of STN input to pallidum and pallidal neuronal dynamics, resulting in sensitivity of pallidal output to the phase of the arriving STN input. Thus the mechanism considered here (the change in firing pattern of subthalamic neurons through the dopamine-induced change of membrane properties) may be one of the potential mechanisms responsible for the generation of the intermittent synchronization observed in Parkinson''s disease.     

A synchronization-desynchronization code for natural communication signals

Synchronous spiking of neural populations is hypothesized to play important computational roles in forming neural assemblies and solving the binding problem. Although the opposite phenomenon of desynchronization is well known from EEG studies, it is largely neglected on the neuronal level. We here provide an example of in vivo recordings from weakly electric fish demonstrating that, depending on the social context, different types of natural communication signals elicit transient desynchronization as well as synchronization of the electroreceptor population without changing the mean firing rate. We conclude that, in general, both positive and negative changes in the degree of synchrony can be the relevant signals for neural information processing.     

Single unit activity in the guinea-pig cochlear nucleus during sleep and wakefulness.

The effects of waking and sleep on the response properties of auditory units in the ventral cochlear nucleus (CN) were explored by using extracellular recordings in chronic guinea-pigs. Significant increases and decreases in firing rate were detected in two neuronal groups, a) the "sound-responding" and b) the "spontaneous" (units that do not show responses to any acoustic stimuli controlled by the experimenter). The "spontaneous" may be considered as belonging to the auditory system because the corresponding units showed a suppression of their discharge when the receptor was destroyed. The auditory CN units were characterized by their PSTH in response to tones at their characteristic frequency and also by the changes in firing rate and probability of discharge evaluated during periods of waking, slow wave and paradoxical sleep. The CNS performs functions dependent on sensory inputs during wakefulness and sleep phases. By studying the auditory input at the level of the ventral CN with constant sound stimuli, it was shown that, in addition to the firing rate shifts, some units presented changes in the temporal probability of discharge, implying central actions on the corresponding neurons. The mean latency of the responses, however, did not show significant changes throughout the sleep-waking cycle. The auditory efferent pathways are postulated to modulate the auditory input at CN level during different animal states. The probability of firing and the changes in the temporal pattern, as shown by the PSTH, are thus dependent on both the auditory input and the functional brain state related to the sleep-waking cycle.     

Synchronization study in ring-like and grid-like neuronal networks

In this paper, we study the synchronization status of both two gap-junction coupled neurons and neuronal network with two different network connectivity patterns. One of the network connectivity patterns is a ring-like neuronal network, which only considers nearest-neighbor neurons. The other is a grid-like neuronal network, with all nearest neighbor couplings. We show that by varying some key parameters, such as the coupling strength and the external current injection, the neuronal network will exhibit various patterns of firing synchronization. Different types of firing synchronization are diagnosed by means of a mean field potential, a bifurcation diagram, a correlation coefficient and the ISI-distance method. Numerical simulations demonstrate that the synchronization status of multiple neurons is much dependent on the network patters, when the number of neurons is the same. It is also demonstrated that the synchronization status of two coupled neurons is similar with the grid-like neuronal network, but differs radically from that of the ring-like neuronal network. These results may be instructive in understanding synchronization transitions in neuronal systems.     

Cortical network modeling: analytical methods for firing rates and some properties of networks of LIF neurons.

The circuitry of cortical networks involves interacting populations of excitatory (E) and inhibitory (I) neurons whose relationships are now known to a large extent. Inputs to E- and I-cells may have their origins in remote or local cortical areas. We consider a rudimentary model involving E- and I-cells. One of our goals is to test an analytic approach to finding firing rates in neural networks without using a diffusion approximation and to this end we consider in detail networks of excitatory neurons with leaky integrate-and-fire (LIF) dynamics. A simple measure of synchronization, denoted by S(q), where q is between 0 and 100 is introduced. Fully connected E-networks have a large tendency to become dominated by synchronously firing groups of cells, except when inputs are relatively weak. We observed random or asynchronous firing in such networks with diverse sets of parameter values. When such firing patterns were found, the analytical approach was often able to accurately predict average neuronal firing rates. We also considered several properties of E-E networks, distinguishing several kinds of firing pattern. Included were those with silences before or after periods of intense activity or with periodic synchronization. We investigated the occurrence of synchronized firing with respect to changes in the internal excitatory postsynaptic potential (EPSP) magnitude in a network of 100 neurons with fixed values of the remaining parameters. When the internal EPSP size was less than a certain value, synchronization was absent. The amount of synchronization then increased slowly as the EPSP amplitude increased until at a particular EPSP size the amount of synchronization abruptly increased, with S(5) attaining the maximum value of 100%. We also found network frequency transfer characteristics for various network sizes and found a linear dependence of firing frequency over wide ranges of the external afferent frequency, with non-linear effects at lower input frequencies. The theory may also be applied to sparsely connected networks, whose firing behaviour was found to change abruptly as the probability of a connection passed through a critical value. The analytical method was also found to be useful for a feed-forward excitatory network and a network of excitatory and inhibitory neurons.     

Modulation of the dynamics of cerebellar Purkinje cells through the interaction of excitatory and inhibitory feedforward pathways

The dynamics of cerebellar neuronal networks is controlled by the underlying building blocks of neurons and synapses between them. For which, the computation of Purkinje cells (PCs), the only output cells of the cerebellar cortex, is implemented through various types of neural pathways interactively routing excitation and inhibition converged to PCs. Such tuning of excitation and inhibition, coming from the gating of specific pathways as well as short-term plasticity (STP) of the synapses, plays a dominant role in controlling the PC dynamics in terms of firing rate and spike timing. PCs receive cascade feedforward inputs from two major neural pathways: the first one is the feedforward excitatory pathway from granule cells (GCs) to PCs; the second one is the feedforward inhibition pathway from GCs, via molecular layer interneurons (MLIs), to PCs. The GC-PC pathway, together with short-term dynamics of excitatory synapses, has been a focus over past decades, whereas recent experimental evidence shows that MLIs also greatly contribute to controlling PC activity. Therefore, it is expected that the diversity of excitation gated by STP of GC-PC synapses, modulated by strong inhibition from MLI-PC synapses, can promote the computation performed by PCs. However, it remains unclear how these two neural pathways are interacted to modulate PC dynamics. Here using a computational model of PC network installed with these two neural pathways, we addressed this question to investigate the change of PC firing dynamics at the level of single cell and network. We show that the nonlinear characteristics of excitatory STP dynamics can significantly modulate PC spiking dynamics mediated by inhibition. The changes in PC firing rate, firing phase, and temporal spike pattern, are strongly modulated by these two factors in different ways. MLIs mainly contribute to variable delays in the postsynaptic action potentials of PCs while modulated by excitation STP. Notably, the diversity of synchronization and pause response in the PC network is governed not only by the balance of excitation and inhibition, but also by the synaptic STP, depending on input burst patterns. Especially, the pause response shown in the PC network can only emerge with the interaction of both pathways. Together with other recent findings, our results show that the interaction of feedforward pathways of excitation and inhibition, incorporated with synaptic short-term dynamics, can dramatically regulate the PC activities that consequently change the network dynamics of the cerebellar circuit.