No, non-parametric learning is an unrelated topic, which seeks to estimate probability distributions without using "parametric" (= having known structure) models. The canonical example of a non-parametric approach is using histograms, or their generalization, called Parzen Windows.

http://en.wikipedia.org/wiki/Parzen_Windows

That's an orthogonal distinction. The methods thus far have typically been parametric, in that there's a fixed network topology and the learning algorithm adjusts the (fixed set of) weights on the edges. There's no reason, though, why you couldn't have a nonparametric version that adaptively chose the number of hidden nodes in the networks and the connectivity structure.