Closer, but still no :) Geoff Hinton proposed contrastive divergence training for Restricted Boltzmann Machines in his 2006 science paper. CD does not apply outside of RBMs though, and most of these nets in the article here are not in fact RBMs. The paper did spark a lot of interest in the field though.

These are all neural nets (with some bells and whistles in some cases like tied weights, pooling units, etc) trained exactly as they were trained before using stochastic gradient descent or LBFGS. We did come up with a lot of tricks for making SGD work though, like momentum terms, clamping of weights during learning, dropout, unsupervised pretraining, etc., but in large part it's just a lot more compute power. These networks just turned out to work very well when you have a LOT of (fairly homogeneous) data and can afford to scale them up computationally. And that's pretty awesome, looks like we have a powerful hammer and there are plenty of nails lying around :)