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On some computational results for single neurons' activity modeling

机译:关于单个神经元活动建模的一些计算结果

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摘要

The classical Ornstein-Uhlenbeck diffusion neuronal model is generalized by inclusion of a time-dependent input whose strength exponentially decreases in time. The behavior of the membrane potential is consequently seen to be modeled by a process whose mean and covariance classify it as Gaussian-Markov. The effect of the input on the neuron's firing characteristics is investigated by comparing the firing probability densities and distributions for such a process with the corresponding ones of the Ornstein-Uhlenbeck model. All numerical results are obtained by implementation of a recently developed computational method. (C) 2000 Elsevier Science Ireland Ltd. All rights reserved. [References: 12]
机译:经典的Ornstein-Uhlenbeck扩散神经元模型通过包含时间依赖性输入来进行泛化,其强度随时间呈指数下降。因此,可以认为膜电位的行为是通过一个过程进行建模的,该过程的均值和协方差将其分类为高斯-马尔可夫。通过将这种过程的放电概率密度和分布与Ornstein-Uhlenbeck模型的相应放电概率密度和分布进行比较,研究了输入对神经元放电特性的影响。所有数值结果均通过实施最新开发的计算方法获得。 (C)2000 Elsevier Science Ireland Ltd.保留所有权利。 [参考:12]

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