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👇

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

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/

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.

🚨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... ......

🚨 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!

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!