A nonparametric approach to extract information from interspike interval data
UNSPECIFIED. (2006) A nonparametric approach to extract information from interspike interval data. JOURNAL OF NEUROSCIENCE METHODS, 150 (1). pp. 30-40. ISSN 0165-0270Full text not available from this repository.
Official URL: http://dx.doi.org/10.1016/j.jneumeth.2005.05.021
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|
|Official Date:||15 January 2006|
|Number of Pages:||11|
|Page Range:||pp. 30-40|
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