> See Hebbian learning

The one that is not used, because it's inherently unstable?

Learning using locally accessible information is an interesting approach, but it needs to be more complex than "fire together, wire together". And then you might have propagation of information that allows to approximate gradients locally.

Is that what they're teaching now? Originally it was not used because it was believed it couldn't learn XOR (it can [just not as perceptrons were defined]).

Is there anyone in particular whose work focuses on this that you know of?

Oja's rule dates back to 1982?

It’s Hebbian and solves all stability problems.

https://en.wikipedia.org/wiki/Oja's_rule