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h-h equation     
相关语句
  h-h方程
     PHYSICS MEANINGS OF THE SOLUTION OF H-H EQUATION
     神经细胞物理学中H-H方程解意义的讨论
短句来源
     Finally, based on H-H model of neuron, we simulated the SR to help neuron increasing tactile sensitivity when the stimulating signal is under the threshold via an algorithm to solve nonlinear the H-H equation.
     论文最后以神经元H-H方程为基础,采用一种求解H-H非线性方程的算法,初步探索利用随机共振提高触觉神经元阈下触觉信号敏感能力的仿真研究。
短句来源
     Second, according to the characteristic of the instantaneous change of action potential accompanied by the nerve impulse, we use H-H equation to describe such change, and conduct simulation combined with SR theory.
     然后根据产生神经冲动时动作电位全或无式瞬时快速变化的特点,采用H-H方程作为描述这种变化的神经元模型,结合随机共振理论进行了仿真研究。
短句来源
     1. Since the H-H equation consists of four nonlinear differential equations, and random Gaussian white noise should be added to the equation to study SR, we adopts a new algorithm to solve the random differential equations, which is easy to handle. By the simulation results the algorithm is proved to be correct and feasible.
     1.由于H-H方程是由四个非线性的微分方程组成,运算工作量大,并且为了研究随机共振现象,要在方程中加入随机高斯白噪声,因此本文采用了一种新的求解随机微分方程的算法,该算法简单易行,通过仿真结果表明该算法正确可行。
短句来源
  中h-h方程
     PHYSICS MEANINGS OF THE SOLUTION OF H-H EQUATION
     神经细胞物理学中H-H方程解意义的讨论
短句来源
  h-h方程
     These features are obtained by long-term physiological experiments, or by H-H equation with computer simuiation which we carried out The neuron models, which have these nine neuron properties, are not convenient for mathematical analysis.
     这些特性是从长期的生理实验中获得的,也可以由H-H方程经计算机模拟获得[1],我们作了这些模拟。 由这九个特性构成的神经元模型不便于作统一的数学分析。
短句来源
  “h-h equation”译为未确定词的双语例句
     The solution of the H - H equation corresponds to nerve impulses.
     通过对Hodhkin-Huxley方程解的分析,可知它与神经脉冲对应。
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In order to manifest the richness and variety of artificial neural networks (ANN), their intelligent behavior, and to develop advanced applications of ANN. it is necessary to build theoretical frame for the ANN Here presented is the first part of the fiame, which includes the following three parts: neuron model, plasticity of synapses and their leaening algorithms, and the architecture of the neural network. The neuron model is composed of nine features based on neurophysiology, including: ( 1 ) spatial summation...

In order to manifest the richness and variety of artificial neural networks (ANN), their intelligent behavior, and to develop advanced applications of ANN. it is necessary to build theoretical frame for the ANN Here presented is the first part of the fiame, which includes the following three parts: neuron model, plasticity of synapses and their leaening algorithms, and the architecture of the neural network. The neuron model is composed of nine features based on neurophysiology, including: ( 1 ) spatial summation effect (2) tempofal summation effect (3) the characteristics of the threshold; (4) non-respond duration; (5) adaptability; (6) excitation and characteristics; (7) delay: (8) characteristics of the output. (9) characteristics of conductivity. These features are obtained by long-term physiological experiments, or by H-H equation with computer simuiation which we carried out The neuron models, which have these nine neuron properties, are not convenient for mathematical analysis. In some cases. some characters of them are not important,such as conductivity, adaptability, etc. while some other characters (spatal summation effect, output character, etc.) are more important. If we review the popular ANN. models with the nine features, we can find that all of them are within these features. We used these features in practice and simulation research, satisfactory results were obtained, such as associated memory and data compression in ECG.

由于人工神经网络(ANN)的种类繁多、行为各异,因此有必要建立一个统一的建立在生物基础上的理论框架。以便进一步发展ANN的各种高级应用。这个框架由神经元模型、突触的可塑性以及它的学习算法、神经网络的结构三部分组成。本文是它的第一部分,神经元模型是由建筑在神经生理基础之上的九个特性组成,可以定量描述包括:(1)空间总和效应;(2)时间总和效应;(3)阈值特性;(4)不应期;(5)适应性;(6)兴奋与抑制特性;(7)延时;(8)输出特性;(9)传导特性。这些特性是从长期的生理实验中获得的,也可以由H-H方程经计算机模拟获得[1],我们作了这些模拟。由这九个特性构成的神经元模型不便于作统一的数学分析。其中某些特性在某些情况下是不重要的;如传导特性,适应性等。而另一些特性如空间总和效应,输出特性是较为重要的。衡量目前流行的各种神经网络模型,发现都没有超出九个特性[2]。应用这些特性于实践及各种仿真研究中,都获得良好结果如联想记忆及ECG的数据压缩等。

The solution of the H - H equation corresponds to nerve impulses. There are two speeds of impulse. The fast one is asymptotically stable, but the later is not. A threshold charge can be fined when stimulating current is high enough. Pulse interactions, pulse collision, pulse interaction on parallel fibers, dendric branch point interaction, delay at varicosities, and conduction throuth axonal branching are discussed. The multiplex neuron Is also briefly discussed.

通过对Hodhkin-Huxley方程解的分析,可知它与神经脉冲对应。不同神经脉冲速度具有其特殊的意义。快速脉冲具有渐进稳定性,慢速脉冲则没有。强度达到一定程度的神经脉冲有一个对应的临界电荷。脉冲的碰撞、在分枝处的相互作用等的讨论,多重神经细胞的简析。

Hopf bifurcation of the H-H equations was computed by numerical methods and bursting of neurons were studied by simulation. It is shown that slow wave can induce bursting. The results prompt that bursting or ultraexciting of neurons can be induced by slow time-course EPSC/EPSP when it is big enough. And it is maybe just the seizure reason of some diseases (such as epilepsy).

通过对H -H方程Hopf分叉的数值计算以及神经元放电的仿真研究 ,从理论和仿真实验两方面都证明了慢变刺激可以引起神经元的阵发放电。结果提示 ,足够大的突触慢反应可以引起神经元的阵发放电和/或超常兴奋 ,这或许正是某些疾病 (如癫痫 )突发的原因

 
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