  We propose a novel local learning rule for spiking neural networks in which
spike propagation times undergo activity-dependent plasticity. Our plasticity
rule aligns pre-synaptic spike times to produce a stronger and more rapid
response. Inputs are encoded by latency coding and outputs decoded by matching
similar patterns of output spiking activity. We demonstrate the use of this
method in a three-layer feedfoward network with inputs from a database of
handwritten digits. Networks consistently improve their classification accuracy
after training, and training with this method also allowed networks to
generalize to an input class unseen during training. Our proposed method takes
advantage of the ability of spiking neurons to support many different
time-locked sequences of spikes, each of which can be activated by different
input activations. The proof-of-concept shown here demonstrates the great
potential for local delay learning to expand the memory capacity and
generalizability of spiking neural networks.
