The term I've heard for this sort of thing is "Physical Neural Networks" or "PNN"s. My impression is that one of the big things holding them back is that because we can't manufacture components to perfect tolerances, you can't train a single model and reuse it like you can with digital logic. Even if you can get close, every single circuit needs some amount of tuning. And we haven't worked out great ways to train them.
There's a lot of research going on in this space though, because yeah, nature can solve certain mathematical problems more efficiently than digital systems.
There's a lot of research going on in this space though, because yeah, nature can solve certain mathematical problems more efficiently than digital systems.
There's a decent review article that came out recently: https://www.nature.com/articles/s41586-025-09384-2 or https://arxiv.org/html/2406.03372v1