BMC Neuroscience
Volume 9
Suppl 1 |
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 Poster presentationCapturing correlation structure within a simplified population density frameworkChin-Yueh Liu  and Duane Q Nykamp Department of Mathematics, University of Minnesota, Minneapolis, MN, 55455, USA  author email corresponding author email
from Seventeenth Annual Computational Neuroscience Meeting: CNS*2008
Portland, OR, USA. 19–24 July 2008 BMC Neuroscience 2008,
9(Suppl 1):P7doi:10.1186/1471-2202-9-S1-P7 First paragraph (this article has no abstract)
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. | 
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