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This article is part of the supplement: Sixteenth Annual Computational Neuroscience Meeting: CNS*2007

Open Access Open Badges Poster presentation

A population density framework that captures interneuronal correlations

Chin-Yueh Liu* and Duane Q Nykamp

Author Affiliations

Department of Mathematics, University of Minnesota, Minneapolis, MN, 55455, USA

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BMC Neuroscience 2007, 8(Suppl 2):P25  doi:10.1186/1471-2202-8-S2-P25

The electronic version of this article is the complete one and can be found online at:

Published:6 July 2007

© 2007 Liu and Nykamp; licensee BioMed Central Ltd.

Poster presentation

We have developed a population density framework that captures correlations between any pair of neurons in the population. We model each population of integrate-and-fire neurons as receiving input in the form of correlated Poisson processes. The evolution equation for the probability density of any pair of neurons within the population is a multivariate integro-differential equation which we solve numerically. We demonstrate the numerical method and compare the numerical solutions with Monte-Carlo simulations. Traditional population density approaches assume all neurons within a population are independent. However, correlations that are missed by these approaches can significantly alter network dynamics. Hence, the correlated population density method developed here could provide a framework to analyze how correlations propagate through networks and could be a computationally efficient method to accurately simulate large scale networks.