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Owen Marschall

@omarschall.bsky.social
173 followers 196 following 49 posts

Postdoc in the Litwin-Kumar lab at the Center for Theoretical Neuroscience at Columbia University. I'm interested in multi-tasking and dimensionality.

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Owen Marschall @omarschall.bsky.social · 14/07/2026
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.
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Owen Marschall @omarschall.bsky.social · 04/03/2026
this looks extremely awesome!
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Reposted by Owen Marschall
SueYeon Chung @sueyeonchung.bsky.social · 10/02/2026
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
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Owen Marschall @omarschall.bsky.social · 15/12/2025
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...
biorxiv.org
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...
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Owen Marschall @omarschall.bsky.social · 04/11/2025
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.
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Reposted by Owen Marschall
David G. Clark @david-g-clark.bsky.social · 19/08/2025
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.
arxiv.org
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...
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