[jmchambers]

Totally agree! The "fire together, wire together" approach to training weights is super easy to parallelize, and you can design custom silicon to make it ridiculously efficient. Back when I was a Computational Neuroscience (CN) researcher, I worked with a team in Manchester that was exploring exactly that—not sure if they ever nailed it...

Funny enough, I actually worked with Rafal Bogacz, the last-named author of the paper we’re discussing, during his Basal Ganglia (BG) phase. He’s an incredibly sharp guy and made a pretty compelling argument that the BG implement the multihypothesis sequential probability ratio test (MSPRT) to decide between competing action plans in an optimal way.

Back then, there was another popular theory that the BG used an actor-critic learning model—also quite convincing.

But here’s the rub: in CN, the trend is to take algorithms from computer science and statistics and map them onto biology. What’s far rarer is extracting new ML algorithms from the biology itself.

I got into CN because I thought the only way we’d ever crack AGI was by unlocking the secrets of the best example we’ve got—the mammalian brain. Unfortunately, I ended up frustrated with the biology-led approach. In ten years in the field, I didn’t see anything that really felt like progress toward AGI. CN just moves so much slower than mainstream ML!

Still, I hope Rafal’s onto something with this latest idea. Fingers crossed it gives ML researchers a shiny new algorithm to play with.