  Although representation learning methods developed within the framework of
traditional neural networks are relatively mature, developing a spiking
representation model remains a challenging problem. This paper proposes an
event-based method to train a feedforward spiking neural network (SNN) layer
for extracting visual features. The method introduces a novel
spike-timing-dependent plasticity (STDP) learning rule and a threshold
adjustment rule both derived from a vector quantization-like objective function
subject to a sparsity constraint. The STDP rule is obtained by the gradient of
a vector quantization criterion that is converted to spike-based,
spatio-temporally local update rules in a spiking network of leaky,
integrate-and-fire (LIF) neurons. Independence and sparsity of the model are
achieved by the threshold adjustment rule and by a softmax function
implementing inhibition in the representation layer consisting of
WTA-thresholded spiking neurons. Together, these mechanisms implement a form of
spike-based, competitive learning. Two sets of experiments are performed on the
MNIST and natural image datasets. The results demonstrate a sparse spiking
visual representation model with low reconstruction loss comparable with
state-of-the-art visual coding approaches, yet our rule is local in both time
and space, thus biologically plausible and hardware friendly.
