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Kelsey Han

@chihyehan.bsky.social
70 followers 73 following 9 posts

Cog Comp Neuro PhD at Johns Hopkins 🔗 kelseyhan-jhu.github.io

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Reposted by Kelsey Han
Erica Busch @elbusch.bsky.social · 11/06/2026
Our new paper is out this week in Nature Neuroscience! www.nature.com/articles/s41... We built a BCI that works with the brain's natural geometry — and we found that people could learn to play a video game with their brains in <1 hr of training. This efficiency is groundbreaking & here's why:
nature.com
Human learning of noninvasive brain–computer interfaces via manifold geometry - Nature Neuroscience
Busch et al. use nonlinear neural manifolds to help humans gain rapid control over a noninvasive brain–computer interface, allowing them to learn how to play a video game with real-time fMRI neurofeed...
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Erica Busch @elbusch.bsky.social · 29/05/2026
🎉🎉🎉🎉 I'm thrilled and humbled to share some major updates! (A 🧵 but TLDR: graduated from Yale, joining Sungkyunkwan University in South Korea as an IBS Young Scientist Fellow this summer, and starting as an assistant professor in Vanderbilt's College of Connected Computing in Fall 2027!)
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Mariam Aly @mariamaly.bsky.social · 24/03/2026
How do the brain’s event representations change as we gain familiarity with an experience? Brain regions’ representations can become coarser or finer as events become familiar. Slow-timescale structure predicts memory. Excited to share this work w/ Narjes Al-Zahli & @chrisbaldassano.bsky.social!
jneurosci.org
Repeated Viewing of a Film Clip Changes Event Timescales in The Brain
Many everyday experiences share a recurring structure: routines, familiar routes, rewatched films, and replayed songs. How do repeated encounters with such structure alter the brain’s representations ...
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Reposted by Kelsey Han
Hayoung Song @hayoungsong.bsky.social · 23/03/2026
Attention fluctuates over time and across contexts—how is this reflected in the brain?🧠 Fitting a dynamical systems model to fMRI data, we show that the geometry of neural dynamics along the attractor landscape reflects changes in attention. Out in @natcomms.nature.com www.nature.com/articles/s41...
nature.com
Geometry of neural dynamics along the cortical attractor landscape reflects changes in attention - Nature Communications
Attention fluctuates over time and across contexts—how is this reflected in the brain? Fitting a dynamical systems model to fMRI data, Song and colleagues show that the geometry of neural dynamics alo...
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Reposted by Kelsey Han
Alex Williams @itsneuronal.bsky.social · 19/03/2026
Cosyne invited me to give a long tutorial (4 hours!) on methods to quantify differences high-d neural recordings across animals, brain regions, deep neural nets, etc. The recording is up on youtube. I hope it inspires more research on this fundamental topic! www.youtube.com/watch?v=n44x...
youtube.com
Cosyne 2026 - Cosyne Tutorial: Comparative Analysis of Neural Population Codes
YouTube video by Cosyne Talks
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Reposted by Kelsey Han
Andrew Lampinen @lampinen.bsky.social · 17/03/2026
Pleased to share that our paper "Representation Biases: Variance is Not Always a Good Proxy for Importance" is now out as Theory/New Concepts paper in eNeuro! www.eneuro.org/content/13/3... 1/
eneuro.org
Representation Biases: Variance Is Not Always a Good Proxy for Importance
A central approach in neuroscience is to analyze neural representations as a means to understand a system's function, through the use of methods like principal component analysis, regression, and repr...
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Kelsey Han @chihyehan.bsky.social · 30/01/2026
Thanks Erica!
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Kelsey Han @chihyehan.bsky.social · 30/01/2026
So yes—high-dimensional neural codes do shape behavior. Not only do stimulus representations scale unboundedly, individual differences span the full dimensional capacity of cortical codes. We're only beginning to understand the rich structure that makes each brain unique.
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Kelsey Han @chihyehan.bsky.social · 30/01/2026
The upshot: your subjective experience isn't encoded in a low-dimensional subspace of cortical activity. It emerges from the full high-dimensional geometry of cortical population responses—most of which we've been missing with conventional approaches.
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Kelsey Han @chihyehan.bsky.social · 30/01/2026
We also found that neural dimensionality is related to the concreteness of each subject’s recollection. Subjects who focus on concrete details, as opposed to abstract aspects of the movies, tend to share more dimensions with others.
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Kelsey Han @chihyehan.bsky.social · 30/01/2026
These neural differences matter! Fine-grained structure in higher dimensions of cortical activity predicts behavioral differences during recall—even after accounting for coarse-scale effects captured by standard methods.
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Kelsey Han @chihyehan.bsky.social · 30/01/2026
In our new work, we find that the ways individual brains differ are *not* constrained to a few dominant patterns. We find distinct patterns in how individual brains process natural movies along many latent dimensions, and these differences are reliable across different movies.
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Kelsey Han @chihyehan.bsky.social · 30/01/2026
Recent work from our lab revealed the scale-free structure of cortical image representations in large-scale studies of humans and monkeys. Stimulus-related information is distributed across thousands of dimensions, extending far beyond the few dominant components typically studied.
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Kelsey Han @chihyehan.bsky.social · 30/01/2026
Our findings show that individual neural patterns during movie viewing span orders of magnitude of dimensions—and these high-dimensional codes predict how people describe their experiences.
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Kelsey Han @chihyehan.bsky.social · 30/01/2026
Human visual cortex representations may be much higher-dimensional than earlier work suggested, but are these higher dimensions of cortical activity actually relevant to behavior? Our new paper tackles this by studying how different people experience the same movies. 🧵 www.cell.com/current-biol...
cell.com
High-dimensional structure underlying individual differences in naturalistic visual experience
Han and Bonner reveal that individual visual experience arises from high-dimensional neural geometry distributed across multiple representational scales. By characterizing the full dimensional spectru...
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Reposted by Kelsey Han
Mick Bonner @mickbonner.bsky.social · 11/12/2025
Dimensionality reduction may be the wrong approach to understanding neural representations. Our new paper shows that across human visual cortex, dimensionality is unbounded and scales with dataset size—we show this across nearly four orders of magnitude. journals.plos.org/ploscompbiol...
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UniReps @unireps.bsky.social · 12/12/2025
📢The UniReps x @ellis.eu speaker series is back! Come join us in our next appointment 18th December 4 pm CET with @meenakshikhosla.bsky.social and Raj Magesh Gauthaman🔵🔴
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Reposted by Kelsey Han
Mick Bonner @mickbonner.bsky.social · 12/12/2025
Hopkins Cog Sci is hiring! We have two open faculty positions: one in vision, and one language. Please repost!
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