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A population density framework that captures interneuronal correlations

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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.

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Correspondence to Chin-Yueh Liu.

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Open Access This article is published under license to BioMed Central Ltd. This is an Open Access article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Liu, C., Nykamp, D.Q. A population density framework that captures interneuronal correlations. BMC Neurosci 8, P25 (2007) doi:10.1186/1471-2202-8-S2-P25

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Keywords

  • Animal Model
  • Population Density
  • Probability Density
  • Evolution Equation
  • Efficient Method