Yea I was thinking the same thing. I teach some of this stuff at the graduate level but it’s tough for newcomers to get used to even in that setting.

No neuromorphic chip. Main reason is interpretability.

Yea the surrogate gradient stuff works ok, others that are decent 1) STDP variants, especially dopamine modulated STDP (emulates RL-like reinforcement) 2) for networks < 10M params, evolution strategies and similar zero-order solvers can work well operating directly on the weights 3) variational solvers can work if you structure the net + activations appropriately

Unfortunately, beginner literature on this stuff is virtually nonexistent. Your best bet is to read papers and experiment.

I use a custom SNN variant in production on real use cases, and the way we train those is very similar to the FF proposal. Most people just assume SNNs are impossible to train because SGD isn’t immediately available, when in reality there are dozens of ways to train SNNs to achieve solid performance.