Unsupervised pre-training is fundamentally different than boosting.

Boosting is a clever way of modelling a conditional distribution. The insight behind the success of pre-training is that, for many perceptual tasks, having a good model of the input (rather than the input->output mapping) is key.

I have no delusion that the algorithms that work for training deep networks are anything like what the brain actually does, but I don't care. There are many tasks where deep neural nets are state of the art.