Event modeling of message interchange in stochastic neural ensembles

Abstract

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 produc-tion 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.