Pattern-stimulation and large-scale activity imaging both require methods for synthetically generating and analyzing multiple spike trains, taking into account features like their correlation structure. We have recently introduced a new strategy based on correlation distortions in the Linear-Nonlinear-Poisson (LNP) model for generating multiple spike trains with an exactly controlled correlation structure, and for identifying neural encoding models and directed information flow in networks of neurons.
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- Krumin M, Reutsky I & Shoham S, Correlation-based analysis and generation of multiple spike trains using Hawkes models with an exogenous input, Frontiers in Computational Neuroscience, 4:147 (2010)
- Krumin M & Shoham S, Multivariate auto-regressive modeling and Granger causality analysis of multiple spike trains, Computational Intelligence and Neuroscience (2010)