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Analysing correlations between streams of events is an important problem. Itarises for example in Neurosciences, when the connectivity of neurons should beinferred from spike trains that record neurons' individual spiking activity.While recently some approaches for inferring delayed synaptic connections havebeen proposed, they are limited in the types of connectivities and delays theyare able to handle, or require computation-intensive procedures. This paperproposes a faster and more flexible approach for analysing such delayedcorrelated activity: a statistical approach for the Analysis of Connectivity inspiking Events (ACE), based on the idea of hypothesis testing. It firstcomputes for any pair of a source and a target neuron the inter-spike delaysbetween subsequent source- and target-spikes. Then, it derives a null model forthe distribution of inter-spike delays for \emph{uncorrelated}~neurons.Finally, it compares the observed distribution of inter-spike delays to thisnull model and infers pairwise connectivity based on the Pearson's Chi-squaredtest statistic. Thus, ACE is capable to detect connections with a prioriunknown, non-discrete (and potentially large) inter-spike delays, which mightvary between pairs of neurons. Since ACE works incrementally, it has potentialfor being used in online processing. In our experiments, we visualise theadvantages of ACE in varying experimental scenarios (except for one specialcase) and in a state-of-the-art dataset which has been generated forneuro-scientific research under most realistic conditions.

ACE: A Novel Approach for the Statistical Analysis of Pairwise Connectivity