Brette, R. (2006). Exact simulation of integrate-and-fire models with synaptic conductances. Neural Comput 18(8): 2004-2027.
Abstract. Computational neuroscience relies heavily on the simulation of large networks of neuron models. There are essentially two simulation strategies: 1) using an approximation method (e.g. Runge-Kutta) with spike times binned to the time step; 2) calculating spike times exactly in an event-driven fashion. In large networks, the computation time of the best algorithm for either strategy scales linearly with the number of synapses, but each strategy has its own assets and constraints: approximation methods can be applied to any model but are inexact; exact simulation avoids numerical artefacts but is limited to simple models. Previous work has focused on improving the accuracy of approximation methods. In this paper we extend the range of models that can be simulated exactly to a more realistic model, namely an integrate-and-fire model with exponential synaptic conductances.
- Functions for exact simulation: IFSC.h, IFSC.c (to include in your favourite event-driven simulator)
- Scilab implementation of functions for exact simulation and an example script for a random network: ExpCondIF(Brette).zip. The archive includes a generic event-driven simulator written in Scilab for networks without delays (this is for pedagogical purposes and is not intended to be efficient at all). N.B.: Scilab is a free scientific software (resembling Matlab).