A nonparametric approach to extract information from interspike interval data.

Enrico Rossoni; Jianfeng Feng

doi:10.1016/j.jneumeth.2005.05.021

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A nonparametric approach to extract information from interspike interval data.

Enrico Rossoni·Jianfeng Feng

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Abstract

In this work we develop an approach to extracting information from neural spike trains. Using the expectation-maximization (EM) algorithm, interspike interval data from experiments and simulations are fitted by mixtures of distributions, including Gamma, inverse Gaussian, log-normal, and the distribution of the interspike intervals of the leaky integrate-and-fire model. In terms of the Kolmogorov-Smirnov test for goodness-of-fit, our approach is proved successful (P>0.05) in fitting benchmark data for which a classical parametric approach has been shown to fail before. In addition, we present a novel method to fit mixture models to censored data, and discuss two examples of the application of such a method, which correspond to the case of multiple-trial and multielectrode array data. A MATLAB implementation of the algorithm is available for download from .

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Enrico Rossoni, Jianfeng Feng. 2005-08-18. A nonparametric approach to extract information from interspike interval data.. https://doi.org/10.1016/j.jneumeth.2005.05.021

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