Congratulations to #FlatironCCN director @eerosim.bsky.social#FlatironCCNon his election to the National Academy of Sciences! https://bit.ly/42AEUhl #science #neuroscience

Cosyne invited me to give a long tutorial (4 hours!) on methods to quantify differences high-d neural recordings across animals, brain regions, deep neural nets, etc. The recording is up on youtube. I hope it inspires more research on this fundamental topic! www.youtube.com/watch?v=n44x...

I am totally pumped about this new work . "Task-trained RNNs" are a powerful and influential framework in neuroscience, but have lacked a firm theoretical footing. This work provides one, and makes direct contact with the classical theory of random RNNs: www.biorxiv.org/content/10.6...

1/7 How should feedback signals influence a network during learning? Should they first adjust synaptic weights, which then indirectly change neural activity (as in backprop.)? Or should they first adjust neural activity to guide synaptic updates (e.g., target prop.)? openreview.net/forum?id=xVI...

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: