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GP CaKe: Effective brain connectivity with causal kernels
[article]
2017
arXiv
pre-print
A fundamental goal in network neuroscience is to understand how activity in one region drives activity elsewhere, a process referred to as effective connectivity. Here we propose to model this causal interaction using integro-differential equations and causal kernels that allow for a rich analysis of effective connectivity. The approach combines the tractability and flexibility of autoregressive modeling with the biophysical interpretability of dynamic causal modeling. The causal kernels are
arXiv:1705.05603v1
fatcat:6k77dlbecfhghackq4uqe4ucru