  Simultaneous recordings from many neurons hide important information and the
connections characterizing the network remain generally undiscovered despite
the progresses of statistical and machine learning techniques. Discerning the
presence of direct links between neuron from data is still a not completely
solved problem. To enlarge the number of tools for detecting the underlying
network structure, we propose here the use of copulas, pursuing on a research
direction we started in [1]. Here, we adapt their use to distinguish different
types of connections on a very simple network. Our proposal consists in
choosing suitable random intervals in pairs of spike trains determining the
shapes of their copulas. We show that this approach allows to detect different
types of dependencies. We illustrate the features of the proposed method on
synthetic data from suitably connected networks of two or three formal neurons
directly connected or influenced by the surrounding network. We show how a
smart choice of pairs of random times together with the use of empirical
copulas allows to discern between direct and un-direct interactions.
