Simulating Artificial Neural Networks on Parallel Architectures

Parallelism and distribution have been considered the key features of neural processing. The term parallel distributed processing is even used as a synonym for ar-tiicial neural networks. Nevertheless, the actual implementations are still in search of the appropriate model to "naturally represent" neural computing. And the-nal judgement is always given in performance gures { keeping the parallelization issue high on the neurosimu-lation agenda. Two approaches have yielded the best results: parallel simulations on general-purpose computers , and specially developed neurohardware. Programming neural networks on parallel machines requires high-level techniques reeecting both inherent features of neuromodels and characteristics of the underlying computers. On the other hand, emulation of the neuroparadigm requires that the functioning of neural operations be mimicked directly by the hardware. Both approaches are presented, and their advantages and shortcomings are outlined.