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A nonparametric approach to extract information from interspike interval data
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UNSPECIFIED. (2006) A nonparametric approach to extract information from interspike interval data. JOURNAL OF NEUROSCIENCE METHODS, 150 (1). pp. 30-40. ISSN 0165-0270
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Official URL: http://dx.doi.org/10.1016/j.jneumeth.2005.05.021
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 http://www.informatics.sussex.ac.uk/users/er28/em/. (c) 2005 Elsevier B.V. All rights reserved.
| Item Type: | Journal Article |
|---|---|
| Subjects: | Q Science > QD Chemistry R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry |
| Journal or Publication Title: | JOURNAL OF NEUROSCIENCE METHODS |
| Publisher: | ELSEVIER SCIENCE BV |
| ISSN: | 0165-0270 |
| Date: | 15 January 2006 |
| Volume: | 150 |
| Number: | 1 |
| Number of Pages: | 11 |
| Page Range: | pp. 30-40 |
| Identification Number: | 10.1016/j.jneumeth.2005.05.021 |
| Publication Status: | Published |
| URI: | http://wrap.warwick.ac.uk/id/eprint/33990 |
Data sourced from Thomson Reuters' Web of Knowledge
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