Deep Knowledge and Deep Learning

Deep learning nowadays is the "buzz word": on my first postdoc, I have found out how deep learning stolen the scene in a matter of years, since I last worked directly with machine learning; I left for a while for working with white-box models in mathematical physiology, appetite control. More or less on the same time deep learning was leaving the underworld, we had spiking neural network; I came across that model by professor Kasabov. Deep learning is a set of artificial neural network. Being straight to the point: it is huge number of hidden layers on a multilayered perceptron (MLP). What does make those techniques (i.e., SNN) so different from what we already have and what may set them apart on future applications?