I've looked over the paper now, unless I'm misunderstanding this seems very similar to the general trend of hebbian learning/STDP/local predictive coding/teache

I've looked over the paper now, unless I'm misunderstanding this seems very similar to the general trend of hebbian learning/STDP/local predictive coding/teacher forcing (for those unfamiliar, these are all distinct but the basic idea is always to have a signal adjust based on the difference with some local target. Hebbian learning is the basic "fire together wire together" principle, STDP is a specific instantiation that works with specific types of memristors, teacher forcing comes from RNN training and imposes the ground truth input on intermediate step, local predictive coding I can't recall the precise thing but but basically diffuses a local output error through a network similar to what is done on a single layer here [which can actually approximate backpropagation! It's very cool]). How would you differentiate yourself against this/what would you say is the core benefit of this approach?