Efficient event-driven simulation of excitable hybrid automata

We present an efficient, event-driven simulation framework for large-scale networks of excitable hybrid autontata (EHA), a particular kind of hybrid automata that we use to model excitable cells. A key aspect of EHA is that they possess protected modes of operation in which they are non-responsive to external inputs. In such modes, our approach takes advantage of the analytical solution of the modes' linear differential equations to eliminate all integration steps, and therefore to dramatically reduce the amount of computation required. We first present a simple simulation framework for EHA based on a time-step integration method that follows naturally from our EHA models. We then present our event-driven simulation framework, where each cell has an associated event specifying both the type of processing next required for the cell and a time at which the processing must occur. A priority queue, specifically designed to reduce queueing overhead, maintains the correct ordering among events. This approach allows us to avoid handling certain cells for extended periods of time. Through a mode-by-mode case analysis, we demonstrate that our event-driven simulation procedure is at least as accurate as the time-step one. As experimental validation of the efficacy of the event-driven approach, we demonstrate a five-fold improvement in the simulation time required to produce spiral waves in a 400-x400 cell array.