Volume 9 Supplement 1

Seventeenth Annual Computational Neuroscience Meeting: CNS*2008

Open Access

Capturing correlation structure within a simplified population density framework

BMC Neuroscience20089(Suppl 1):P7

DOI: 10.1186/1471-2202-9-S1-P7

Published: 11 July 2008

We have developed a population density framework that captures correlations between any pair of neurons in the population. Completely representing the correlation structure among neurons would require high-dimensional densities. Hence, we developed a method to simplify the correlation structure by approximating the input to each population of neurons as correlated Poisson processes. The key challenge we address is that of capturing the effect of delayed correlation with such simplified input. We demonstrate the ability of this approach to capture how correlations propagate through networks by comparing our results with Monte-Carlo simulations.

Authors’ Affiliations

(1)
Department of Mathematics, University of Minnesota

Copyright

© Liu and Nykamp; licensee BioMed Central Ltd. 2008

This article is published under license to BioMed Central Ltd.

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