Event modeling of message interchange in stochastic neural ensembles

We propose a modeling framework based on the event-driven paradigm for populations of neurons which interchange messages. Unlike other strategies our approach is focused on the dynamics at the mesoscopic level (spike production and reception) and does not determine the microstates of the neurons. We apply the technique on a discrete model of stochastic ensembles and on extensions of this model to the continuous time domain. Due to the event-driven nature of the method efficient large-scale simulations can be performed without precision errors. The approach uses spike predictions as evidences and a one-step update of the predictions is performed every time an event occurs, resulting in a more efficient solution than the existing strategies.