Delorme, A., Gautrais, J., VanRullen, R., & Thorpe, S.J. (1999). SpikeNET: A simulator for modeling large networks of integrate and fire neurons. Neurocomputing, 26-27, 989-996.
Abstract
SpikeNET is a simulator for modeling large networks of asynchronously spiking neurons. It uses simple integrate-and-fire neurons which undergo step-like changes in membrane potential when synaptic inputs arrive. If a threshold is exceeded, the potential is reset and the neuron added to a list to be propagated on the next time step. Using such spike lists greatly reduces the computations associated with large networks, and simplifies implementations using parallel hardware since inter-processor communication can be limited to sending lists of the neurons which just fired. We have used it to model complex multi-layer architectures based on the primate visual system that involve millions of neurons and billions of synaptic connections. Such models are not only biological but also efficient, robust and very fast, qualities which they share with the human visual system.
