  Spiking neural networks have been referred to as the third generation of
artificial neural networks where the information is coded as time of the
spikes. There are a number of different spiking neuron models available and
they are categorized based on their level of abstraction. In addition, there
are two known learning methods, unsupervised and supervised learning. This
thesis focuses on supervised learning where a new algorithm is proposed, based
on genetic algorithms. The proposed algorithm is able to train both synaptic
weights and delays and also allow each neuron to emit multiple spikes thus
taking full advantage of the spatial-temporal coding power of the spiking
neurons. In addition, limited synaptic precision is applied; only six bits are
used to describe and train a synapse, three bits for the weights and three bits
for the delays. Two limited precision schemes are investigated. The proposed
algorithm is tested on the XOR classification problem where it produces better
results for even smaller network architectures than the proposed ones.
Furthermore, the algorithm is benchmarked on the Fisher iris classification
problem where it produces higher classification accuracies compared to
SpikeProp, QuickProp and Rprop. Finally, a hardware implementation on a
microcontroller is done for the XOR problem as a proof of concept. Keywords:
Spiking neural networks, supervised learning, limited synaptic precision,
genetic algorithms, hardware implementation.
