  A new supervised learning algorithm, SNN/LP, is proposed for Spiking Neural
Networks. This novel algorithm uses limited precision for both synaptic weights
and synaptic delays; 3 bits in each case. Also a genetic algorithm is used for
the supervised training. The results are comparable or better than previously
published work. The results are applicable to the realization of large scale
hardware neural networks. One of the trained networks is implemented in
programmable hardware.
