Simulation scalability of large brain neuronal networks thanks to time asynchrony
We present a new algorithm to efficiently simulate random models of large neural networks satisfying the property of time asynchrony. The model parameters (average firing rate, number of neurons, synaptic connection probability, and postsynaptic duration) are of the order of magnitude of a small mammalian brain, or of human brain areas. Through the use of activity tracking and procedural connectivity (dynamical regeneration of synapses), both computational and memory complexities of this algorithm are proved to be theoretically linear with the number of neurons. These results are experimentally validated by sequential simulations of millions of neurons and billions of synapses running in few minutes using a single thread of an equivalent desktop computer.