I think this is the main thing I want out of "non-backprop ML" research - figuring out how the whole class of algorithms behaves. And then applying that to figure out how the brain implements its own deep learning.

If we can figure out how the brain's learning dynamics function well enough? We could figure out how to interface with them and extend them.