  We investigate several distribution free dependence detection procedures,
mainly based on bootstrap principles and their approximation properties. Thanks
to this study, we introduce a new distribution free Unitary Events (UE) method,
named Permutation UE, which consists in a multiple testing procedure based on
permutation and delayed coincidence count. Each involved single test of this
procedure achieves the prescribed level, so that the corresponding multiple
testing procedure controls the False Discovery Rate (FDR), and this with as few
assumptions as possible on the underneath distribution. Some simulations show
that this method outperforms the trial-shuffling and the MTGAUE method in terms
of single levels and FDR, for a comparable amount of false negatives.
Application on real data is also provided.
