A population density framework that captures interneuronal correlations

Chin-Yueh Liu, Duane Q Nykamp
2007 BMC Neuroscience  
We have developed a population density framework that captures correlations between any pair of neurons in the population. We model each population of integrate-andfire 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
more » ... ions 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.
doi:10.1186/1471-2202-8-s2-p25 fatcat:lytynsrzcng6da5yq2lsm4ubyi