Why Deep Learning Works – Artificial Understanding

I like to refer to the input layer as being "on the bottom" rather than at the far left as in this image. When viewing it my way, the low-to-high dimension we use in my rotated version of the above can be mentally mapped to a low-to-high stack of abstraction levels; I'm not the only one using this dimension this way. I hope this rotation isn't too confusing. We can see that there is an obvious data Reduction and an obvious complexity Reduction. Can we determine whether the system is also performing what I'd like to call "Epistemic Reduction": Is it reducing away that which is unimportant, and if so, how does it accomplish this? How does an operator in a Deep Learning stack know what makes something important (Salient)? A pure data "reduction" of sorts could be accomplished by compression schemes or even random deletion.