Understanding spontaneous transitions between dynamical modes in a network is of significant
importance. These transitions may separate pathological and normal functions of the
brain. In this paper, we develop a set of measures that, based on spatio-temporal features
of network activity, predict autonomous network transitions from asynchronous to synchronous
dynamics under various conditions. These metrics quantify spike-timing distributions
within a narrow time window as a function of the relative location of the active neurons. We
applied these metrics to investigate the properties of these transitions in excitatory-only and
excitatory-and-inhibitory networks and elucidate how network topology, noise level, and cellular
heterogeneity affect both the reliability and the timeliness of the predictions. The developed
measures can be calculated in real time and therefore potentially applied in clinical
situations.