This September, I will join the @flatironinstitute.org Center for Computational Neuroscience as a Group Leader, where I will launch the High-Dimensional Dynamics & Computation Group. Information for postdocs and grad students is forthcoming. I am grateful to my mentors who made this possible!

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

CCN Director Eero Simoncelli Elected to National Academy of Sciences

Eero Simoncelli, director of the Flatiron Institute’s Center for Computational Neuroscience (CCN), has been elected to the National Academy of Sciences.

bit.ly

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...

Cosyne 2026 - Cosyne Tutorial: Comparative Analysis of Neural Population Codes

YouTube video by Cosyne Talks

youtube.com

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...

A theory of multi-task computation and task selection

Neural activity during the performance of a stereotyped behavioral task is often described as low-dimensional, occupying only a limited region in the space of all firing-rate patterns. This region has...

biorxiv.org

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

Simplified derivations for high-dimensional convex learning problems

Statistical physics provides tools for analyzing high-dimensional problems in machine learning and theoretical neuroscience. These calculations, particularly those using the replica method, often invo...

arxiv.org

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...)

Comp Neuro Starter Pack

Join the conversation

go.bsky.app

Cian O'Donnell@cianodonnell.bsky.social · 2y ago

Is there a computational neuroscience starter pack?

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

Blake Richards@tyrellturing.bsky.social · 2y ago

Check out our new preprint! We show there is a better learning algorithm for #compneuro than gradient descent (GD): exponentiated gradients (EG). tl;dr: EG respects Dale's law, produces weight distributions that match biology, and outperforms GD in biologically relevant scenarios. 🧠📈 🧪