Modeling spike-count dependence structures with multivariate Poisson distributions
© Onken and Obermayer; licensee BioMed Central Ltd. 2008
- Published: 11 July 2008
- Maximum Likelihood Estimation
- Likelihood Estimation
- Cumulative Distribution
- Cumulative Distribution Function
- Short Time Interval
Previous studies use multivariate Gaussian distributions as models of correlated spike-counts . However, this approximation is not appropriate for short time intervals and fails to model realistic dependence structures. To eradicate these shortcomings, we propose alternative joint distributions that are marginally Poisson distributed and contain a broad range of dependence structures for count variables.
We apply two methods to generate multivariate Poisson distributions of dependent spike-counts. The first approach employs sums of hidden variables to introduce dependencies between the Poisson distributed counts . The second approach introduces dependencies by means of copulas of several classes. Copulas are functions that couple marginal cumulative distribution functions to form a joint distribution function with the same margins .
This work was supported by BMBF grant 01GQ0410.
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