Not only useful, but I think this method elegantly enforces a hypothesis in identifying neuronal coordinates that I think a lot of theorists implicitly believe: at most a small number of neural modes can condense at a given moment, even if a large number is observed to condense across many moments.

Joshua Glaser@joshuaiglaser.bsky.social · 3w ago

Excited to share this work, led by Andrew Zimnik and Sherry An, from @mchurchland.bsky.social and my labs! www.cell.com/neuron/fullt... 1/2

this looks extremely awesome!

David G. Clark@david-g-clark.bsky.social · 5mo ago

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...

Our paper is out in @natneuro.nature.com! www.nature.com/articles/s41... We develop a geometric theory of how neural populations support generalization across many tasks. @zuckermanbrain.bsky.social @flatironinstitute.org @kempnerinstitute.bsky.social 1/14

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

Can confirm this was a fun project! My favorite takeaway is that the (low-but-extensive) rank of a network can be used as a knob for controlling dimensionality while leaving single-neuron properties unchanged.

David G. Clark@david-g-clark.bsky.social · 9mo ago

Now in PRX: Theory linking connectivity structure to collective activity in nonlinear RNNs! For neuro fans: conn. structure can be invisible in single neurons but shape pop. activity For low-rank RNN fans: a theory of rank=O(N) For physics fans: fluctuations around DMFT saddle⇒dimension of activity

Wanted to share a new version (much cleaner!) of a preprint on how connectivity structure shapes collective dynamics in nonlinear RNNs. Neural circuits have highly non-iid connectivity (e.g., rapidly decaying singular values, structured singular-vector overlaps), unlike classical random RNN models.

Connectivity structure and dynamics of nonlinear recurrent neural networks

Studies of the dynamics of nonlinear recurrent neural networks often assume independent and identically distributed couplings, but large-scale connectomics data indicate that biological neural circuit...

arxiv.org