  In this work we propose a new supervised learning method for
temporally-encoded multilayer spiking networks to perform classification. The
method employs a reinforcement signal that mimics backpropagation but is far
less computationally intensive. The weight update calculation at each layer
requires only local data apart from this signal. We also employ a rule capable
of producing specific output spike trains; by setting the target spike time
equal to the actual spike time with a slight negative offset for key high-value
neurons the actual spike time becomes as early as possible. In simulated MNIST
handwritten digit classification, two-layer networks trained with this rule
matched the performance of a comparable backpropagation based non-spiking
network.
