Title: Objective Procedure for Reconstructing Couplings in Complex Systems,
Abstract: Inferring directional connectivity from point process data of multiple
elements is desired in various scientific fields such as neuroscience,
geography, economics, etc. Here, we propose an inference procedure for this
goal based on the kinetic Ising model. The procedure is composed of two steps:
(1) determination of the time-bin size for transforming the point-process data
to discrete time binary data and (2) screening of relevant couplings from the
estimated networks. For these, we develop simple methods based on information
theory and computational statistics. Applications to data from artificial and
\textit{in vitro} neuronal networks show that the proposed procedure performs
fairly well when identifying relevant couplings, including the discrimination
of their signs, with low computational cost. These results highlight the
potential utility of the kinetic Ising model to analyze real interacting
systems with event occurrences.