Comp Neuro, ML, Dynamical Systems 🧠🤖PhD student at Harvard & Kempner Institute. Prev at McGill, Mila, EPFL.
💻: https://ann-huang-0.github.io
Ann Huang
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[ #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!
🧠🧵Presenting TODAY (4:30–7:30), poster #2001!
Come by and say hi!
Ann Huang
📣 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
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!
Ann Huang
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
Satpreet (Sat) Singh
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
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/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...
🤖📊 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
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
Naomi Saphra
Matt Perich
Kempner Institute at Harvard University
Kempner Institute at Harvard University
Kempner Institute at Harvard University
David G. Clark
Owen Marschall
📍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?
🧵⬇️
✍️ In the @kempnerinstitute.bsky.social blog: our new tool built to compare the dynamics of complex systems when both internal circuitry and external inputs shape their behavior.
Catch @annhuang42.bsky.social presenting this work at #ICLR! kempnerinstitute.harvard.edu/research/dee...
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...
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...
bit.ly
bit.ly
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...
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...
