  This paper presents a basic enhancement to the DeSTIN deep learning
architecture by replacing the explicitly calculated transition tables that are
used to capture temporal features with a simpler, more scalable mechanism. This
mechanism uses feedback of state information to cluster over a space comprised
of both the spatial input and the current state. The resulting architecture
achieves state-of-the-art results on the MNIST classification benchmark.
