rtdl-revisiting-models VS lava-dl

JFYI: recently, we have split our codebase into separate projects: - https://github.com/Yura52/rtdl - https://github.com/Yura52/tabular-dl-revisiting-models - (the new one) https://github.com/Yura52/tabular-dl-num-embeddings

Surrogate gradient learning w/ backpropagation: for short, you can use backpropagation with SNNs (by a little trick during the backward pass). Super easy to implement, super efficient. You have a deep SNN trained via backprop with any type of input you want. Personally, that is completely my jam. Maybe you can use such paradigm to easily train an SNN in your biomed image dataset. Good repos: SnnTorch comes with the best tutorials to explain SNNs and surrogate gradient learning. This is the fastest way to understand the field and begin to implement you solution. Nevertheless, spikingjelly remains a better option when it comes to implement your ideas (better memory efficiency, etc). Good mention to lava-dl, with which you can train a neural network and directly transfer it into neuromorphic hardware (Intel Loihi) if you have access to this kind of chip.