  Motivated by a neuroscience question about synchrony detection in spike train
analysis, we deal with the independence testing problem for point processes. We
introduce non-parametric test statistics, which are rescaled general
$U$-statistics, whose corresponding critical values are constructed from
bootstrap and randomization/permutation approaches, making as few assumptions
as possible on the underlying distribution of the point processes. We derive
general consistency results for the bootstrap and for the permutation w.r.t. to
Wasserstein's metric, which induce weak convergence as well as convergence of
second order moments. The obtained bootstrap or permutation independence tests
are thus proved to be asymptotically of the prescribed size, and to be
consistent against any reasonable alternative. A simulation study is performed
to illustrate the derived theoretical results, and to compare the performance
of our new tests with existing ones in the neuroscientific literature.
