A parallel simulator to build distributed neural algorithms

The aim of the paper is to present a parallel simulator that allows one to use distributed algorithm to develop artificial neural networks. We developed an efficient parallel simulator to implement artificial neural networks. This simulator makes use of the parallel properties of connectionist models to make an efficient parallel implementation onto general purpose shared memory MIMD computers. Therefore, this simulator naturally leads to build (learning and generalising) neural algorithms that respect the large natural parallel aspects of these models.