Elliot Callender

First post in a sequence about cognition enhancement for AIS research acceleration.[1] No one is training a BCI deep learning model to speak neuralese[2] back to the brain. We should make something which reads and writes native neural representations. Current models, at best, encode visual stimuli for retinal implants. The field grew up around prosthetics, but I'm very surprised that even transhumanist engineers haven't built algorithms whose activations are treated by the brain like additional cortical columns. It seems low-hanging. I'd love to hear why I'm wrong. Why exocortex? Higher cortical surface area gives you much more parallel processing. But I'm more excited about the inferential depth and interpretability which are possible in-silico. Humans are bottlenecked by the number of serial operations we can do in "one forward [corticothalamic] pass". I'll go into more depth in a future post, but our abstractive depth is roughly capped by developmental times and neuroanatomy. Machine matmuls are not quite so capped, even on the relevant 20ms timescales. Given sufficient compute, we're bounded by neural I/O more than even gradient descent iterations. Accessibility of activations is also a massive benefit. Assuming writeback, you can easily (natively, non-mnemonically) expand working memory by an OOM for whichever representations are on-chip; we can already decode WM from macaques with less than 0.2%[3] of their functional cortical sheet, and RAM is much more persistent than neuron activation sequences. Writing back signals such that memory items are accessible is harder. I will talk about approaches which mitigate this in a future post on optogenetic organoids and synthetic capillaries[4]. Integration We're sticking a non-native module on top of an adult brain and hoping it works. Native neuroarchitecture takes at least 12 years from instantiation to frontier, and that figure assumes you're Terence Tao. Doesn't development take too long for the augmentee