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Come see my poster on the information theory behind brain-to-text decoding, today at #SfN24 from 1 to 5PM at Poster O15. And also be sure to check out our work on explaining and driving the activity of individual cortical voxels by using LLMs, at poster O16, right next door!

🚨 Thrilled to announce that our paper "Language models and brains align due to more than next-word prediction and word-level information" has been accepted at #EMNLP2024! 🧠🤖 Work done in collaboration with Mariya Toneva. Curious about why brain and GPT-2 models align? Read on👇

🧠 Stop by poster #z11 and learn more about work by PhD student Suna Guo in collaboration with Franco Pestilli and Alexander Huth Huth Lab UT Neuroscience #whitematter #sematics #dwi #sfn2024

My 2nd to last #neuroscience paper will appear UniReps !! 🧠🧠 Maximizing Neural Regression Scores May Not Identify Good Models of the Brain 🧠🧠 w/ @KhonaMikail Mitchell Ostrow Brando Miranda Sanmi Koyejo Answering a puzzle 2 years in the making openreview.net/forum?id=vbtj0… 1/12

I designed a webapp to tinker with the hyperparameters of UMAP as I will participate in a podcast on it today. It is super simple. Maybe someone finds it useful for intuition building: koerding.github.io/umap-web-app/

1/6 I usually don’t comment on these things, but Rylan Schaeffer et al.'s paper contains enough misconceptions that I thought it might be useful to address them. In short, effective dimensionality is not the whole story for model-brain linear regression, for several reasons:

This nicely summarizes some misconceptions in the recent position paper about measures of brain-model comparison. One additional point: While I agree with the idea in the original paper that we shouldn't *just* focus on regression, many other measures have been proposed! 1/4

1/n🤖🧠 New paper alert!📢 In "Assessing Episodic Memory in LLMs with Sequence Order Recall Tasks" (arxiv.org/abs/2410.08133) we introduce SORT as the first method to evaluate episodic memory in large language models. Read on to find out what we discovered!🧵

If you're over 60, don't shovel snow

Brain activity is useful for improving language models! We show that even small amounts of brain activity recorded while people listen to engaging stories (less than 0.7% of pretraining data) substantially improve speech LM understanding, when incorporated as a training signal.

In this TINS review we (w/cvnlab, eli merriam, & Eline Kupers) argue that *intensive* (many hours of data) fMRI of single individuals for single-voxel model fitting is a paradigm shift for cognitive/computational neuroimaging. tinyurl.com/intensivefmri 1/2

arxiv.org/abs/2406.04328

Yikes! From Jo, Yang, et al. arxiv.org/abs/2405.06459

TFW the precision scan goes tooooo long #HappyHalloween

I'm not at neurips this year, but Chandan Singh Richard Antonello & co are presenting work on how you can just ask LLMs questions about pieces of text, and then use the answers as embeddings. This works well and is very interpretable! Poster #3801 from 11-2 today x.com/csinva/status/…