Simulating artificial neural networks on parallel architectures

Parallelization is necessary to cope with the high computational and communication demands of neuroapplications, but general purpose parallel machines soon reach performance limitations. The article explores two approaches: parallel simulation on general purpose computers, and simulation/emulation on neurohardware. Different parallelization methods are discussed, and the most popular techniques are explained. While the software approach looks for an optimal programming model for neural processing, the hardware approach tries to imitate the neuroparadigm using the best of silicon technology.