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A kinetic theory network model to capture first and second order statistics of population activity in large-scale neuronal networks

2009
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BMC Neuroscience
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We present a kinetic theory neuronal network model that captures how first and second order statistics of population activity depend on certain statistical patterns of connectivity. To capture pairwise correlations among neurons in each population, we model the activity of each population by a joint population density ρ(x 1 , x 2 , t), where (x 1 , x 2 ) represents the state of any pair of neurons in the population. We derive the evolution equations of ρ(x 1 , x 2 , t) using the

doi:10.1186/1471-2202-10-s1-p154
fatcat:bvxrbcugpvf6vkwa2pd6xzngfa