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Weinan Sun

@sunw37.bsky.social
259 followers 64 following 25 posts

Neuroscience, Artificial Intelligence, and Beyond. Assistant professor at Cornell Dept. of Neurobiology & Behavior

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Weinan Sun @sunw37.bsky.social · 12/02/2025
9/12 What do our findings reveal about hippocampal computation? We tested several models—but only Clone-Structured Causal Graph (CSCG) @dileeplearning.bsky.social matched the orthogonalized states and learning trajectory, highlighting hidden-state inference as a key learning principle.
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Weinan Sun @sunw37.bsky.social · 12/02/2025
8/12 At the single-cell level, cells in expert mice showed a continuum: some cells were “place-like,” while others became “splitters” firing selectively for Near vs. Far trials. Single-cell responses evolve across sessions, driving overall decorrelation. Explore our data: cognitivemap.janelia.org
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Weinan Sun @sunw37.bsky.social · 12/02/2025
6/12 Using UMAP on our data, the CA1 manifold evolved over learning. It started as an unstructured cluster, then formed a hub-and-spoke, and finally a ring that split into branches by trial type—refining similar inputs into distinct states that eventually capture the underlying task structure.
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Weinan Sun @sunw37.bsky.social · 12/02/2025
5/12 During learning, hippocampal activity became more distinct. Early on, neurons in ambiguous zones responded similarly; over time, responses separated and tuned to specific task states.
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Weinan Sun @sunw37.bsky.social · 12/02/2025
4/12 During training, mice showed 4 behavioral strategies: 1. Random licking, 2. Licking at both potential rewards, 3. Stopping after collecting a reward, 4. Only licking at the correct reward location.
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Weinan Sun @sunw37.bsky.social · 12/02/2025
3/12 Task: Mice ran along two similar VR corridors—one with a Near reward and one with a Far reward. An indicator cue at the start signaled the active corridor. Expert mice learned to lick only at the correct reward zone.
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Weinan Sun @sunw37.bsky.social · 12/02/2025
2/12 We used a custom 2P-RAM mesoscope (designed by @sofroniewn.bsky.social, Dan Flickinger, & Karel Svoboda. @hhmijanelia.bsky.social) to track thousands of CA1 cells across sessions. It let us see changes in single-cell & population activity throughout learning.
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