Before revision:  Applications to data from artificial andin 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 modelto analyze real interacting systems with event occurrences .
After revision:  Applications to data from artificial andin vitro neuronal networks show that the proposed procedure performs fairly well when identifying relevant couplings, including the discrimination of their signs, for the synthetic and experimental data. In particular, the results support that we can infer the physical connections of underlying systems in favorable situations, even when using the simple statistical model .