  The problem of training spiking neural networks (SNNs) is a necessary
precondition to understanding computations within the brain, a field still in
its infancy. Previous work has shown that supervised learning in multi-layer
SNNs enables bio-inspired networks to recognize patterns of stimuli through
hierarchical feature acquisition. Although gradient descent has shown
impressive performance in multi-layer (and deep) SNNs, it is generally not
considered biologically plausible and is also computationally expensive. This
paper proposes a novel supervised learning approach based on an event-based
spike-timing-dependent plasticity (STDP) rule embedded in a network of
integrate-and-fire (IF) neurons. The proposed temporally local learning rule
follows the backpropagation weight change updates applied at each time step.
This approach enjoys benefits of both accurate gradient descent and temporally
local, efficient STDP. Thus, this method is able to address some open questions
regarding accurate and efficient computations that occur in the brain. The
experimental results on the XOR problem, the Iris data, and the MNIST dataset
demonstrate that the proposed SNN performs as successfully as the traditional
NNs. Our approach also compares favorably with the state-of-the-art multi-layer
SNNs.
