神经元尖峰模型的Fokker-Planck解

Derek J. Daniel
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引用次数: 0

摘要

神经科学中神经元尖峰模型的随机动力学,当被视为一个大型模拟网络时,已知可以简化为解决福克-普朗克方程的经典问题,或概概论中等效的Kolmogorov微分方程,用于对随机注入离子电流的神经元的统计特性进行数值评估。然而,这里的问题是,福克-普朗克方程的初始条件是狄拉克函数,因此,在计算神经科学中,实际实现可以获得数值稳定性的函数会成为问题。因此,在这个简短的交流中,提出了实现这样一个初始条件的计算方法,它本身就导致了这个问题的精确解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fokker-Planck Solution for a Neuronal Spiking Model
The stochastic dynamics of a neuronal spiking model in neuroscience, when viewed as a large simulated network, are known to reduce to the classic problem of solving the Fokker-Planck equation, or the equivalent Kolmogorov differential equation in probability theory, for the numerical evaluation of the statistical properties of neurons as a random injection of ion currents. The problem here, however, is that the initial condition for the Fokker-Planck equation is a Dirac delta function so the actual implementation of Delta functions that at the same time can attain numerical stability can become problematic in computational neuroscience. Therefore, in this brief communication, a computational method for implementing such an initial condition is suggested, which itself has led to an exact solution for this problem.
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来源期刊
Transport Theory and Statistical Physics
Transport Theory and Statistical Physics 物理-物理:数学物理
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