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改进的泄漏积分点火神经元首次通过时间的积分方程解。

Improved integral equation solution for the first passage time of leaky integrate-and-fire neurons.

机构信息

Zanvyl Krieger Mind/Brain Institute and Solomon Snyder Department of Neuroscience, Johns Hopkins University, Baltimore, MD 21218, USA.

出版信息

Neural Comput. 2011 Feb;23(2):421-34. doi: 10.1162/NECO_a_00078. Epub 2010 Nov 24.

Abstract

An accurate calculation of the first passage time probability density (FPTPD) is essential for computing the likelihood of solutions of the stochastic leaky integrate-and-fire model. The previously proposed numerical calculation of the FPTPD based on the integral equation method discretizes the probability current of the voltage crossing the threshold. While the method is accurate for high noise levels, we show that it results in large numerical errors for small noise. The problem is solved by analytically computing, in each time bin, the mean probability current. Efficiency is further improved by identifying and ignoring time bins with negligible mean probability current.

摘要

准确计算首次穿越时间概率密度(FPTPD)对于计算随机漏电积分和点火模型解的可能性至关重要。先前基于积分方程方法提出的 FPTPD 的数值计算方法离散化了跨越阈值的电压的概率电流。虽然该方法对于高噪声水平是准确的,但我们表明,对于小噪声水平,它会导致较大的数值误差。通过在每个时间箱中分析计算平均概率电流来解决该问题。通过识别和忽略具有可忽略的平均概率电流的时间箱,进一步提高了效率。

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