I have seen a few articles about neural nets. Mostly they went along these lines: we tried these architectures, these meta parameters, we trained it for $x$ hours on $y$ CPUs, and it gave us these results that are 0.1% better than state of the art.
What I am interested in is whether there exists (at least as a work in progress) a framework that gives some explanation why is some architecture better than other, what makes one activation function more suitable for image recognition than another, etc.
Do you have some tips about where to start? I would prefer something more systematic than a Google search (a book, a list of key articles is ideal).