  Analysing correlations between streams of events is an important problem. It
arises for example in Neurosciences, when the connectivity of neurons should be
inferred from spike trains that record neurons' individual spiking activity.
While recently some approaches for inferring delayed synaptic connections have
been proposed, they are limited in the types of connectivities and delays they
are able to handle, or require computation-intensive procedures. This paper
proposes a faster and more flexible approach for analysing such delayed
correlated activity: a statistical approach for the Analysis of Connectivity in
spiking Events (ACE), based on the idea of hypothesis testing. It first
computes for any pair of a source and a target neuron the inter-spike delays
between subsequent source- and target-spikes. Then, it derives a null model for
the distribution of inter-spike delays for \emph{uncorrelated}~neurons.
Finally, it compares the observed distribution of inter-spike delays to this
null model and infers pairwise connectivity based on the Pearson's Chi-squared
test statistic. Thus, ACE is capable to detect connections with a priori
unknown, non-discrete (and potentially large) inter-spike delays, which might
vary between pairs of neurons. Since ACE works incrementally, it has potential
for being used in online processing. In our experiments, we visualise the
advantages of ACE in varying experimental scenarios (except for one special
case) and in a state-of-the-art dataset which has been generated for
neuro-scientific research under most realistic conditions.
