title: A nonparametric approach to extract information from interspike interval data.
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 .
authors.affiliation: Department of Informatics, Sussex University, Brighton BN1 9QH, UK.
authors.forename: Enrico
authors.initials: E
authors.lastname: Rossoni
authors.forename: Jianfeng
authors.initials: J
authors.lastname: Feng
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D006054:Goldfish; D008839:Microelectrodes; D008959:Models, Neurological; D012165:Retinal Ganglion Cells / Q000502:physiology*