🧠 New preprint! How does the brain build specialized, efficient representations as we grow up? We used manifold learning to track the "intrinsic dimensionality" (ID) of brain activity in ~800 participants (aged 3mo–53yrs), as they performed naturalistic tasks and rested/slept.

bioRxiv Neuroscience@biorxiv-neursci.bsky.social · 2w ago

Developmental tuning of functional manifold dimensionality across the human brain https://www.biorxiv.org/content/10.64898/2026.07.24.740635v1

Excited to share that our paper is now out in #JNeurosci! We propose a multidimensional framework of high-level visual cortex that reconciles a longstanding debate. Thanks to @kathadobs.bsky.social, @martinhebart.bsky.social, and everyone else for the great discussions along the way. More to come 🧠🌈

Multidimensional feature tuning in category-selective areas of human visual cortex

Two prominent accounts describe the functional organization of human high-level visual cortex. A categorical view emphasizes category-selective areas, while a dimensional view highlights continuous fe...

doi.org

SfN Journals@sfnjournals.bsky.social · 3w ago

#JNeurosci: Using a data-driven analysis of fMRI responses to natural images, @levandyck.bsky.social @martinhebart.bsky.social & @kathadobs.bsky.social identified interpretable dimensions that explain activity in face-, body-, & scene-selective areas https://doi.org/10.1523/JNEUROSCI.0038-26.2026

How can manifold theory help us understand why representations learned by AI models🤖 are aligned to the brain🧠? We expanded our UniReps Best Short Paper on this topic into a full paper at @icmlconf! Now extended to ECoG and with new brain-tuning results! Check it out👇

Emily Cheng@emcheng.bsky.social · 3mo ago

Presenting this at #ICML with @rjantonello.bsky.social and Aditya Vaidya✨ Why do 𝙢𝙞𝙙𝙙𝙡𝙚 layers in LLMs and speech-audio models best predict brain responses to language? We show a peak in the dimensionality of 🤖 activations (left) to track high 🧠 predictivity (right) 🧵(cross-posted from X)

And a nice summary by Richard Antonello can be found here: elifesciences.org/articles/111...

Language Models: Does the brain really know what word is coming next?

Apparent neural encoding of future words may arise from the statistical structure of language itself, rather than from predictive computations in the brain.

elifesciences.org

ines-schoenmann.bsky.social@ines-schoenmann.bsky.social · 3mo ago

New peer-reviewed paper w/ @mheilbron.bsky.social, @predictivebrain.bsky.social & Jakub Szewczyk! Pre-onset brain encoding has been taken as evidence that brains–like LLMs–predict upcoming words. We show that the same signatures arise in systems that cannot predict. (elifesciences.org) (1/8)

Excited to share our work on mechanisms of naturalistic audiovisual processing in the human brain 🧠🎬!! www.biorxiv.org/content/10.1...

Introducing CorText: a framework that fuses brain data directly into a large language model, allowing for interactive neural readout using natural language. tl;dr: you can now chat with a brain scan 🧠💬 1/n

🧠 New at #NeurIPS2025! 🎵 We're far from the shallow now🎵 TL;DR: We introduce the first "reasoning embedding" and uncover its unique spatio-temporal pattern in the brain. 🔗 arxiv.org/abs/2510.228...

As our lab started to build encoding 🧠 models, we were trying to figure out best practices in the field. So @neurotaha.bsky.social built a library to easily compare design choices & model features across datasets! We hope it will be useful to the community & plan to keep expanding it! 1/

neurotaha@neurotaha.bsky.social · 10mo ago

🚨 Paper alert: To appear in the DBM Neurips Workshop LITcoder: A General-Purpose Library for Building and Comparing Encoding Models 📄 arxiv: arxiv.org/abs/2509.091... 🔗 project: litcoder-brain.github.io

In our new paper, we explore how we can build encoding models that are both powerful and understandable. Our model uses an LLM to answer 35 questions about a sentence's content. The answers linearly contribute to our prediction of how the brain will respond to that sentence. 1/6

New paper with @mujianing.bsky.social & @prestonlab.bsky.social! We propose a simple model for human memory of narratives: we uniformly sample incoming information at a constant rate. This explains behavioral data much better than variable-rate sampling triggered by event segmentation or surprisal.

bioRxiv Neuroscience@biorxiv-neursci.bsky.social · last yr.

Efficient uniform sampling explains non-uniform memory of narrative stories https://www.biorxiv.org/content/10.1101/2025.07.31.667952v1

🚨Paper alert!🚨 TL;DR first: We used a pre-trained deep neural network to model fMRI data and to generate images predicted to elicit a large response for each many different parts of the brain. We aggregate these into an awesome interactive brain viewer: piecesofmind.psyc.unr.edu/activation_m...

What are the organizing dimensions of language processing? We show that voxel responses during comprehension are organized along 2 main axes: processing difficulty & meaning abstractness—revealing an interpretable, topographic representational basis for language processing shared across individuals

Stimulus dependencies---rather than next-word prediction---can explain pre-onset brain encoding during natural listening https://www.biorxiv.org/content/10.1101/2025.03.08.642140v1

I’m hiring a full-time lab tech for two years starting May/June. Strong coding skills required, ML a plus. Our research on the human brain uses fMRI, ANNs, intracranial recording, and behavior. A great stepping stone to grad school. Apply here: careers.peopleclick.com/careerscp/cl... ......

Technical Associate I, Kanwisher Lab

MIT - Technical Associate I, Kanwisher Lab - Cambridge MA 02139

careers.peopleclick.com

🚨 New Preprint!! LLMs trained on next-word prediction (NWP) show high alignment with brain recordings. But what drives this alignment—linguistic structure or world knowledge? And how does this alignment evolve during training? Our new paper explores these questions. 👇🧵

Just in time for the holidays! Some cool new evidence from @eghbal_hosseini for the idea of universal representations shared by high-performing ANNs and brains in two domains: language and vision! Go Eghbal!

Eghbal Hosseini@eghbal-hosseini.bsky.social · 2y ago

Why do diverse ANNs resemble brain representations? Check out our new paper with Colton Casto, @nogazs.bsky.social , Colin Conwell, Mark Richardson, & @evfedorenko.bsky.social on “Universality of representation in biological and artificial neural networks.” 🧠🤖 tinyurl.com/yckndmjt

Really excited to be at NeurIPS this week presenting our new encoding model scaling laws work! Be sure to check out our poster (#402) on Tuesday afternoon and our new code and model release, and feel free to DM me to chat!

Alexander Huth@alexanderhuth.bsky.social · 3y ago

At NeurIPS this week, @rjantonello.bsky.social is presenting his work on scaling fMRI language encoding models. We're also sharing code, extracted features, and estimated model weights for some of our best models: github.com/HuthLab/enco... Paper: arxiv.org/abs/2305.11863