1/X Excited to present this preprint on multi-tasking, with @david-g-clark.bsky.social and Ashok Litwin-Kumar! Timely too, as “low-D manifold” has been trending again. (If you read thru the end, we escape Flatland and return to the glorious high-D world we deserve.) www.biorxiv.org/content/10.6...

1/6 Why does the brain maintain such precise excitatory-inhibitory balance? Our new preprint explores a provocative idea: Small, targeted deviations from this balance may serve a purpose: to encode local error signals for learning. www.biorxiv.org/content/10.1... led by @jrbch.bsky.social

How to find all fixed points in piece-wise linear recurrent neural networks (RNNs)? A short thread 🧵 
In RNNs with N units with ReLU(x-b) activations the phase space is partioned in 2^N regions by hyperplanes at x=b 1/7

(1/5) Fun fact: Several classic results in the stat. mech. of learning can be derived in a couple lines of simple algebra! In this paper with Haim Sompolinsky, we simplify and unify derivations for high-dimensional convex learning problems using a bipartite cavity method. arxiv.org/abs/2412.01110

This list likely reflects mainly my interests and circle, and I’m sure I’ve missed many people, but I gave it a try: (I’ll be slowly editing it until it reaches 150/150) go.bsky.app/7VFUkdn (also, I tried but couldn't remove my profile...)

i enjoyed reading the geometry of plasticity paper and felt that something important was coming, this is it: