  Spike-Timing-Dependent Plasticity (STDP) is an unsupervised learning
mechanism for Spiking Neural Networks (SNNs) that has received significant
attention from the neuromorphic hardware community. However, scaling such local
learning techniques to deeper networks and large-scale tasks has remained
elusive. In this work, we investigate a Deep-STDP framework where a rate-based
convolutional network, that can be deployed in a neuromorphic setting, is
trained in tandem with pseudo-labels generated by the STDP clustering process
on the network outputs. We achieve $24.56\%$ higher accuracy and $3.5\times$
faster convergence speed at iso-accuracy on a 10-class subset of the Tiny
ImageNet dataset in contrast to a $k$-means clustering approach.
