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Ann Huang

@annhuang42.bsky.social
174 followers 144 following 19 posts

Comp Neuro, ML, Dynamical Systems 🧠🤖PhD student at Harvard & Kempner Institute. Prev at McGill, Mila, EPFL. 💻: ann-huang-0.github.io

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Reposted by Ann Huang
Mitchell Ostrow @neurostrow.bsky.social · 21/09/2026
Very excited to announce our most recent paper! TL;DR we made our prior work on comparing dynamics (DSA) much faster and generalizable to diverse data domains and problem settings (1/)
biorxiv.org
A metric for comparing complex systems by their dynamics
Comparisons are fundamental to science: experiment against model, one organism against another, a system against itself across time. Because many systems, from brains to climate, are characterized by ...
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Ann Huang @annhuang42.bsky.social · 23/04/2026
[ #ICLR2026 ] How do we know if two systems are performing the same computation when they are constantly driven by different external inputs? 🧠🤖 I’ll be presenting our novel method InputDSA tomorrow April 23 (2:15pm-4:45pm EDT in Pavilion 3 P3-#1614)📍 Come swing by our poster! I’d love to chat!
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Reposted by Ann Huang
Matt Perich @mattperich.bsky.social · 10/03/2026
New paper hot off the (pre-)press! We dig into the evolutionary origins of neural computations for behavioral control across mice, monkeys, and humans: www.biorxiv.org/content/10.6.... As our lab's first foray into comparative analysis of neural dynamics, I’m super excited about this work! 1/18
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Kempner Institute at Harvard University @kempnerinstitute.bsky.social · 09/03/2026
NEW from the #KempnerInstitute: InputDSA, a tool to separate intrinsic dynamics from input-driven effects—enabling accurate, efficient comparisons of complex systems with external inputs. Read the #DeeperLearning blog post by @annhuang42.bsky.social & @kanakarajanphd.bsky.social: bit.ly/4bkrvy9
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InputDSA: Demixing then comparing recurrent and externally driven dynamics in complex systems - Kempner Institute
We explored how to measure the similarity between two complex systems when they are driven by external inputs, like biological neural circuits or reinforcement learning agents. Our novel method, calle...
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Reposted by Ann Huang
Satpreet (Sat) Singh @satpreetsingh.bsky.social · 05/03/2026
📣 Excited to announce the 2nd edition of our workshop “Agent-Based Models in Neuroscience: Theory, Autonomy, Embodiment & Environment” at @cosynemeeting.bsky.social #CoSyNe2026!! 🧠🤖🌍🪰🐟🐭💪🧘🏃 🗓️ March 17, 2026 📍 Cascais, Portugal 🔗 Speaker lineup and schedule: neuro-agent-models.github.io
neuro-agent-models.github.io
🤖 Agent-Based Models in Neuroscience
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Reposted by Ann Huang
David G. Clark @david-g-clark.bsky.social · 04/03/2026
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...
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Reposted by Ann Huang
Kempner Institute at Harvard University @kempnerinstitute.bsky.social · 04/03/2026
NEW: #Kempner researchers develop a mean-field theory of task-trained RNNs that bridges random and learned connectivity—and find macaque motor cortex is best captured by an intermediate, task-specific recurrent structure. Read the blog post 👇 🔗 bit.ly/47f3Ldl
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Reposted by Ann Huang
Kempner Institute at Harvard University @kempnerinstitute.bsky.social · 03/02/2026
🤖📊 NEW in the Deeper Learning blog: @annhuang42.bsky.social & @kanakarajanphd.bsky.social break down their recent work examining how #RNNs solve the same task in different ways, and why that matters. Joint work with @satpreetsingh.bsky.social & @flavioh.bsky.social bit.ly/4kj4fVd #NeuroAI
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Measuring and Controlling Solution Degeneracy Across Task-Trained Recurrent Neural Networks - Kempner Institute
Despite reaching equal performance success when trained on the same task, artificial neural networks can develop dramatically different internal solutions, much like different students solving the sam...
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Reposted by Ann Huang
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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Reposted by Ann Huang
Naomi Saphra @nsaphra.bsky.social · 12/12/2025
So @lchoshen.bsky.social posted a thread on X about how different training runs tend to converge, and I just had to argue with him. Training variation is fascinating, and I think we've kinda cracked it!
x.com
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Ann Huang @annhuang42.bsky.social · 05/12/2025
🧠🧵Presenting TODAY (4:30–7:30), poster #2001! Come by and say hi!
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Ann Huang @annhuang42.bsky.social · 24/11/2025
📍Excited to share that our paper was selected as a Spotlight at #NeurIPS2025! arxiv.org/pdf/2410.03972 It started from a question I kept running into: When do RNNs trained on the same task converge/diverge in their solutions? 🧵⬇️
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Reposted by Ann Huang
Mitchell Ostrow @neurostrow.bsky.social · 10/11/2025
Our next paper on comparing dynamical systems (with special interest to artificial and biological neural networks) is out!! Joint work with @annhuang42.bsky.social , as well as @satpreetsingh.bsky.social , @leokoz8.bsky.social , Ila Fiete, and @kanakarajanphd.bsky.social : arxiv.org/pdf/2510.25943
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