神经递质在睡醒转换中作用的数学模型仿真研究

根据已有的生理解剖知识［１ ］ 提出了关于入睡机制的三细胞环路模型及其网络模型［２ ］ 。在文献［２］ 模型的基础上,根据神经递质在睡醒转换中的重要作用及其作用方式主要是通过影响丘脑中的两种离子流－ 钾的漏电流（ＩＫＬ） 和超极化激活的阳离子流（Ｉｈ） ,以及丘脑皮质系统中突触传递强度的变化而起作用的生理知识,修改了［２］ 中三细胞环路模型及其网络模型。模型仿真结果显示,在适当调节模型参数的条件下,确能使细胞环路产生入睡纺锤节律与觉醒快速放电之间的双向转换,其网络模型也能通过同步振荡在皮层处出现人脑电中规定入睡标志的特征纺锤波与规定觉醒期的低幅快波之间的双向转换。此结果又一次启示了脑信息处理中如何通过同步振荡机制将表示微观特性的神经元群放电特征整合为脑的宏观功能状态的过程。     

睡眠2期脑电信号产生的生理机制模型仿真研究

目前慢波睡眠生理机制研究已有的理论及动物实验结果显示,慢波睡眠中,皮层-丘脑系统神经元存在三种不同节律的振荡:慢振荡(<1 HZ)、δ振荡(1-4Hz)和纺锤振荡(7-14Hz)。这些神经元电活动在皮层水平广泛同步化,产生慢波睡眠脑电。提出了能分别产生这三种节律的三种神经元环路模型,并将之组合简化成一个七细胞环路模型。由这样的大量环路组成的网络模型在合适的突触连接参数范围内,能在皮层处产生人类慢波睡眠脑电2期的完整波形。这一结果说明了如何将动物实验观察到的睡眠生理机制的结果与人自然睡眠活动的脑电结果联系起来,并提示脑信息处理中由微观神经元群放电特征整合为脑的宏观功能状态的主要环节。     

入睡K-综合波产生的生理机制模型仿真研究

在确定人类睡眠脑电客观分期的国际标准中,有两类脑电特征波可以用来确定入睡状态（睡眠第二期）,即纺锤波和K－综合波。在前文中已提出了产生纺锤波的生理机制模型。按照1998年后对K－综合波形成的生理机制的看法,建立了微观神经元环路模型,其放电节律与实验中入睡时神经元放电的振荡节律相一致。而由大量这种相同环路组成的网络模型则在皮层处可产生符合K－综合波的波形。这一结果再次启示了脑信息处理中如何由微观神经元群放电特征整合为脑的宏观功能状态的过程。     

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。     

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．     

研究报告: 大鼠感觉运动系统静息态脑网络研究

为了理解啮齿类动物的脑功能连接,本文利用9.4T fMRI获得轻度麻醉状态下大鼠静息状态及刺激激活的数据,通过互相关分析构建节点之间的相关系数矩阵并计算相应的网络参数．结果发现：给予前爪电刺激时,刺激对侧初级感觉皮层(S1)、丘脑(Tha)有较强的正激活,双侧尾状壳核(CPu)有较强的负激活．静息状态时大鼠感觉/运动皮层内部、丘脑内部的连接性较强,而感觉/运动皮层与丘脑之间的连接较弱,双侧感觉运动系统之间存在较强的同步低频振荡,感觉运动系统在静息态时的脑网络具有小世界属性．结果提示,啮齿类动物在大脑信息处理中的功能分离和整合可能与人类存在某些相似性,支持哺乳动物中枢神经系统的基本功能存在遗传保守性的观点．     

强直电刺激嗅内皮质-海马环路诱发CA1神经元的癫痫同步性膜电位振荡行为

目的和方法 :4 0 0～ 5 0 0 μm大鼠水平脑切片含有封闭的EC 海马环路。强直电刺激 (60Hz ,2s)海马Schaeffer侧支诱发癫痫放电 ,全细胞记录CA1胞体层单个神经元电活动 ,同步记录相应树突区细胞外场电位 ,探讨单个神经元膜电位振荡特性与细胞外癫痫电活动之间的关系。结果 :①强直电刺激诱发CA1神经元膜电位后放性振荡行为呈宽频特征 (3～ 10 0Hz)。以θ节律多见 ,跟随在刺激引起的膜电位去极化或超极化偏移 (paroxysmaldepolarizingorhyperpolaringshift,PDSorPHS)之后 ,振荡波的上升支和下降支分别由膜电位去极化 超极化或超极化 去极化成分构成 ;②逐渐增强的IPSP构成了膜电位振荡的起搏成分 ,继而反弹形成锋电位和阈下振荡 ,与细胞外癫痫样电活动同步 ,并促成癫痫放电由紧张性向阵挛性形式转变 ;③发现了电偶联电位 (spikelets)以及细胞之间的染料偶联现象。结论 :单个神经元作为振荡器可以启动群体神经元超同步化癫痫样电活动 ;缝隙连接可能参与了膜电位振荡的启动与场电位癫痫样电活动的同步作用。     

具有竞争指针的短时记忆神经网络模型

在我们以前提出的短时记忆神经网络模型基础上［３］,我们在新模型中引入突触竞争机制,提出了一个新的短时记忆神经网络模型。模型仍由两个神经网络所组成;其一为与长时记忆共有的信息内容表达网络,另一个为指针神经元环路。由于表达区神经元与指针神经元间的突触权重的竞争,使得模型可以表现出由干扰引起的短时记忆的遗忘。相应于自由回忆序列位置效应和汉字组块两个心理学实验,对模型做了计算机仿真。仿真结果显示模型的行为与两个心理实验定量地符合得很好。由此表明现在的模型更合适于作为短时记忆的模型。     

功能柱结构神经网络模型中的同步振荡现象

功能柱作为大脑皮层一个普遍存在的基本结构, 有着重要的功能意义. 基于皮层功能柱的生理特点, 构建一个模块式的神经网络模型. 当施以恒定密度的脉冲刺激时, 单个功能柱能产生同步振荡. 根据外界输入和网络结构参数的不同, 振荡频率在3~43 Hz之间变化. 由多个功能柱构成的网络之间能在各振荡子之间出现复杂的同步现象, 网络中可以出现部分同步的亚集群.     

短时程突触可塑性的功能意义

短时程的突触可塑性是突触可塑性的一种重要表现形式,对实现神经系统的正常功能起着重要作用.突触的短时程可塑性能够加强突触传递的确定性,调节大脑皮层兴奋和抑制之间的平衡,形成神经活动的时间、空间特性,形成并调节皮层丘脑网络的同步振荡.突触的短时程可塑性可能也参与了注意、启动效应、睡眠节律和学习记忆等神经系统高级功能的实现.     

Structure-preserving model reduction of passive and quasi-active neurons

The spatial component of input signals often carries information crucial to a neuron’s function, but models mapping synaptic inputs to the transmembrane potential can be computationally expensive. Existing reduced models of the neuron either merge compartments, thereby sacrificing the spatial specificity of inputs, or apply model reduction techniques that sacrifice the underlying electrophysiology of the model. We use Krylov subspace projection methods to construct reduced models of passive and quasi-active neurons that preserve both the spatial specificity of inputs and the electrophysiological interpretation as an RC and RLC circuit, respectively. Each reduced model accurately computes the potential at the spike initiation zone (SIZ) given a much smaller dimension and simulation time, as we show numerically and theoretically. The structure is preserved through the similarity in the circuit representations, for which we provide circuit diagrams and mathematical expressions for the circuit elements. Furthermore, the transformation from the full to the reduced system is straightforward and depends on intrinsic properties of the dendrite. As each reduced model is accurate and has a clear electrophysiological interpretation, the reduced models can be used not only to simulate morphologically accurate neurons but also to examine computations performed in dendrites.     

A computational study of the effects of serotonin on a molluscan burster neuron

A mathematical model of burster neuron R₁₅ from the abdominal ganglion of Aplysia is presented. This is an improvement over earlier models in that the bursting mechanism is more accurately represented. The improved model allows for simulated application of the neurotransmitter serotonin, which has been reported to have profound effects on the voltage waveform produced by R₁₅. Computational analysis indicates that the serotonin-induced modulation of the waveform can be explained in terms of competition between stationary, bursting, and beating attractors. Analysis also indicates that, as a result of this competition, serotonin increases the sensitivity of the neuron to synaptic perturbations. This may have important consequences with regard to water balance in the Aplysia, particularly during egg laying.     

Izhikevich神经元网络的同步与联想记忆

联想记忆是人脑的一项重要功能。以Izhikevich神经元模型为节点,构建神经网络,神经元之间采用全连结的方式;以神经元群体的时空编码（spatio-temporal coding）理论研究所构建神经网络的联想记忆功能。在加入高斯白噪声的情况下,调节网络中神经元之间的连接强度的大小,当连接强度和噪声强度达到一个阈值时网络中部分神经元同步放电,实现了存储模式的联想记忆与恢复。仿真结果表明,神经元之间的连接强度在联想记忆的过程中发挥了重要的作用,噪声可以促使神经元间的同步放电,有助于神经网络实现存储模式的联想记忆与恢复。     

Analog VLSI implementation of resonate-and-fire neuron

We propose an analog integrated circuit that implements a resonate-and-fire neuron (RFN) model based on the Lotka-Volterra (LV) system. The RFN model is a spiking neuron model that has second-order membrane dynamics, and thus exhibits fast damped subthreshold oscillation, resulting in the coincidence detection, frequency preference, and post-inhibitory rebound. The RFN circuit has been derived from the LV system to mimic such dynamical behavior of the RFN model. Through circuit simulations, we demonstrate that the RFN circuit can act as a coincidence detector and a band-pass filter at circuit level even in the presence of additive white noise and background random activity. These results show that our circuit is expected to be useful for very large-scale integration (VLSI) implementation of functional spiking neural networks.     

现实神经元模型以及其复杂行为表现

在已提出的现实性神经元模型基础上,构造了一个网络,研究其动态反应特性,计算机模拟结果表明在恒定输入下,该网络输出呈脑电波样反应,在周期性刺激下,其输出呈诱发电位样反应,并从ＥＥＧ－ｌｉｋｅ波形的频谱、相空间重构、平面映射及Ｐｏｉｎｃａｒｅ截面,关联维数等方面对网络进行了复杂性分析。     

ISFET-neuron junction: circuit models and extracellular signal simulations

Purpose of this paper is to characterize the Ion-Sensitive Field-Effect Transistors (ISFET)-neuron junction, based on the equivalent electric-circuit approach. As a result, recording of action potentials can be simulated with a general-purpose circuit simulation program such as HSPICE. The neuronal electrical activity, extracellularly recorded by the ISFET, is analyzed as a function of the physical-chemical and geometric ISFET parameters, of the ionic currents in the neuron, and of the neuro-electronic junction parameters such as the sealing resistance, double-layer capacitance, and general adhesion conditions. The models of the neuron, of the coupling circuit, and of the ISFET implemented in HSPICE are first described. These models are then used to simulate the behavior of the junction between a patch of neuronal membrane (described by the compartmental model) and the ISFET.     

Collective behavior of large-scale neural networks with GPU acceleration

In this paper, the collective behaviors of a small-world neuronal network motivated by the anatomy of a mammalian cortex based on both Izhikevich model and Rulkov model are studied. The Izhikevich model can not only reproduce the rich behaviors of biological neurons but also has only two equations and one nonlinear term. Rulkov model is in the form of difference equations that generate a sequence of membrane potential samples in discrete moments of time to improve computational efficiency. These two models are suitable for the construction of large scale neural networks. By varying some key parameters, such as the connection probability and the number of nearest neighbor of each node, the coupled neurons will exhibit types of temporal and spatial characteristics. It is demonstrated that the implementation of GPU can achieve more and more acceleration than CPU with the increasing of neuron number and iterations. These two small-world network models and GPU acceleration give us a new opportunity to reproduce the real biological network containing a large number of neurons.