[16890c]

To your point of on-chip analog neuromorphic training, there is some recent work with one of the same authors [1] on event-based backprop in spiking neural networks. So far they only have simulations, but this is likely an important step toward fully integrated, scalable training of SNNs on neuromorphic hardware.

[1] https://www.nature.com/articles/s41598-021-91786-z

[igorkraw]

This is part of the research I find more exciting, but the challenge is to actually make this work for things which aren't MNIST. I might be wrong on this, but I haven't seen any novel learning rules deal with Fashion MNIST or CIFAR so far. MNIST can be solved based only on image statistics and is a bad check in this regard - almost everything can learn MNIST