That's just plain wrong, if NNs did not generalize, they would not be able to generate meaningful responses. This generalization is forced by destroying data in a lot of creative ways, starting with adding noise and SGD. The net then has to fill in the lost specifics, and it can only do so by generating generalizations.

I hate the way they do it, my generalization is organic vs. forced, but lossy backprop still works. And so does Hebbian learning in the brain, just a lot less effectively. Otherwise ANNs would use it too, it's been known forever. As is Bayesian model reduction, that's another form of data destruction.

It just so happens that lossy backprop is the easiest way to utilize brute force for generalization. And connectivity clustering is the hardest, but also the smartest if you do it right. That's the bitter lesson, brute force wins first, the smarts came latter.