Joséphine Raugel

@JRaugel

PhD student @ Meta & ENS, on Large models and Brains processing language and vision together. jraugel.github.io

Paris, France

Joined October 2021

Replies Reposts Media

Joséphine Raugel
@JRaugel
Jun 29
Brain2Qwerty V2 is out 💫
Jarod Levy
@JarodLevy
Jun 29
🧠⌨️ Decode language from brain activity without surgery. 🧠⌨️ Brain2Qwerty V1 is officially published in Nature Neuroscience. Today, we're releasing Brain2Qwerty V2. We achieve unprecedented performance for a non-invasive MEG setup. Details below 🧵👇
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Joséphine Raugel
@JRaugel
May 28
1/ We’re so glad to share this new study 💫 Does the brain learn like a Deep Net? 🧠⚙️ - 📄Misalignment Between Backpropagation and the Hierarchy of Brain Responses to Images - 🔗arxiv.org/abs/2605.28693 Thread below 🧵
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Joséphine Raugel
@JRaugel
May 7
It's time to flex your brain foundation model: NeuralBench is out. 36 EEG tasks, 94 datasets, 9,500 subjects. Congratulations @hubertjbanville for leading this tremendous work
Hubert Banville
@hubertjbanville
May 6
🧠 Introducing NeuralBench: a unified, open-source framework to benchmark NeuroAI models. v1.0: 36 EEG tasks, 94 datasets, task-specific + foundation models. MEG/fMRI ready. MIT-licensed, FAIR's Brain & AI @AIatMeta. Code: github.com/facebookresear… Paper: ai.meta.com/research/publi…
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Joséphine Raugel
@JRaugel
Apr 24
We’re thrilled to share DinoBrain at @iclr_conf: Can AI help us understand how the brain learns to see? 🧠 📍Poster P3 #1610 | 3:15pm - DINOv3’s training mirrors some striking aspects of brain development, especially when trained on human-centric data. - Reproduced on 8 models.
Jean-Rémi King
@JeanRemiKing
Sep 1, 2025
Can AI help understand how the brain learns to see the world? Our latest study, led by @JRaugel from FAIR at @AIatMeta and @ENS_ULM, is now out! 📄 arxiv.org/pdf/2508.18226 🧵 A thread:
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Joséphine Raugel
@JRaugel
Apr 23
Come have a chat with @stephanedascoli and I about TribeV2, at ICLR, poster P3 #1636 💫
AI at Meta
@AIatMeta
Mar 26
Today we're introducing TRIBE v2 (Trimodal Brain Encoder), a foundation model trained to predict how the human brain responds to almost any sight or sound. Building on our Algonauts 2025 award-winning architecture, TRIBE v2 draws on 500+ hours of fMRI recordings from 700+ people
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