Separately, do I understand the idea correctly:

- when learning on the real examples, just propagate forward

- when learning on the fake examples, first generate them by propagating white noise backward

That's a technique that was tried in machine learning and worked (although obviously not optimally or we'd all be using it by now) and then we hypothesised that animal brains use it because it would explain sleep.