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Reposted by Jean-Rémi King

BrainHack Donostia @bhdonostia.bsky.social · 7d

Jean-Rémi King - The Emergence of Language in the Human Brain

We’re excited to announce that @jeanremiking.bsky.social , CRNS researcher at the ENS Paris & leader of Meta’s Brain & AI team, will be sharing groundbreaking insights into how language emerges in the human brain at BHD2025!

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Reposted by Jean-Rémi King

Basque Center on Cognition, Brain and Language (BCBL) @bcbl.bsky.social · 27d

[2/2] 📊 His research uses encoding and decoding approaches to show how modern speech and language models account for brain responses to natural speech, measured with EEG, MEG, iEEG, and fMRI, even in children aged 2 to 12.

📆 November 19–21, 2025.

+info 👇

brainhack-donostia.github.io

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Jean-Rémi King @jeanremiking.bsky.social · Sep 4

Congrats Mariya!

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Jean-Rémi King @jeanremiking.bsky.social · Sep 3

Thanks to all the great researchers who contributed to this project: Joséphine Raugel, the DINOv3 team, @valentinwyart.bsky.social, FAIR and ENS as well as the open source and open data #NeuroAI communities for making this possible! 🙏

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Jean-Rémi King @jeanremiking.bsky.social · Sep 3

Overall, the training of DINOv3 mirror some striking aspects of brain development: late-acquired representations map onto the cortical areas with e.g. greater expansion and slower timescales, suggesting that DINOv3 spontaneously captures some of the neuro-developmental trajectory

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Jean-Rémi King @jeanremiking.bsky.social · Sep 3

→ Second factor: data type: Even models trained only on satellite or cellular images significantly capture brain signals — but the same model trained on standard images encodes higher all brain regions.

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Jean-Rémi King @jeanremiking.bsky.social · Sep 3

So what are the factors that lead DINOv3 to become brain-like?
→ 1st factor: Model size: bigger models become brain-like faster during training, reach higher brain-scores, especially in high-level brain regions.

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Jean-Rémi King @jeanremiking.bsky.social · Sep 3

Third, the representations of the visual cortex are typically acquired early on in the training of DINOv3.
By contrast, it requires much more training to learn representations similar to those of the prefrontal cortex.

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Jean-Rémi King @jeanremiking.bsky.social · Sep 3

Surprisingly, these encoding, spatial and temporal scores all emerge across training, but at different speeds.

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Jean-Rémi King @jeanremiking.bsky.social · Sep 3

Second, DINOv3 learns a representational hierarchy which corresponds to the spatial and temporal hierarchies in the brain.

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Jean-Rémi King @jeanremiking.bsky.social · Sep 3

First, we observe that, with training, DINOV3 learns representations that progressively align with those of the human brain.

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Jean-Rémi King @jeanremiking.bsky.social · Sep 3

To evaluate how data type, data quantity and model size each leads DINOv3 to more-or-less brain-like activation, we trained and tested several variants:

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Jean-Rémi King @jeanremiking.bsky.social · Sep 3

We compare the activation of DINOv3 (ai.meta.com/dinov3/), a SOTA self-supervised computer vision model trained on natural images,
to the activations of the human brain in response to the same images using both fMRI (naturalscenesdataset.org) and MEG (openneuro.org/datasets/ds0...)

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Jean-Rémi King @jeanremiking.bsky.social · Sep 3

Can self supervised learning help understand how the brain learns to see the world?

Our latest study, led by Josephine Raugel (FAIR, ENS), is now out:

📄 arxiv.org/pdf/2508.18226
🧵 thread below

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Reposted by Jean-Rémi King

CNSP Workshop @cnspworkshop.bsky.social · Aug 22

🚨 Just over a week left to register for the #CNSP2025 Online Workshop (details in post below)! 🚨

Link to the workshop registration form: docs.google.com/forms/d/e/1F...

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Reposted by Jean-Rémi King

CNSP Workshop @cnspworkshop.bsky.social · Aug 22

Our first Keynote Speaker this year will be Jean-Rémi King
@jeanremiking.bsky.social (CNRS) who leads the Brain & AI team @metaai.bsky.social. He will be giving an exciting talk on the "Emergence of Language in the Human Brain".

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Jean-Rémi King @jeanremiking.bsky.social · Aug 25

Had a blast chatting with @neildegrassetyson.com on @startalkradio.bsky.social ’s podcast about how AI and neuroscience come together
startalkmedia.com/show/mindrea...

Mindreading with Jean Rémi-King - StarTalk Special Edition

What would it take to read someone’s mind? Neil deGrasse Tyson, Chuck Nice, & Gary O’Reilly explore the science of decoding our thoughts w/ Jean-Rémi King.

startalkmedia.com

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Jean-Rémi King @jeanremiking.bsky.social · Aug 16

Very nice , congrats !

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Reposted by Jean-Rémi King

BrainHack Donostia @bhdonostia.bsky.social · Aug 12

We are honoured to welcome @jeanremiking.bsky.social to BrainHack Donostia 2025!

Dr. King is a CNRS researcher based at the École Normale Supérieure in Paris, currently leading the Brain & AI team at Meta AI, combining expertise in cognitive neuroscience and machine learning.

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Lucy (Mingfang) Zhang @lucyzmf.bsky.social · Aug 11

Excited to be with my team at #ccn2025 this week! I’ll be presenting part of the workshop on Thursday. Come say hi!

Jean-Rémi King @jeanremiking.bsky.social · Aug 11

We’re very happy to share 3 highlights of our Brain and AI team for #CCN2025 's week:

1. 🏆1st place for the Algonauts competition: paper, thtread and code below

2.🗣Keynote: Language in the Brain: 2025.ccneuro.org/k-and-t-lang...

3. 🚀Tutorial: Scale your decoding pipeline in the notebook

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Jean-Rémi King @jeanremiking.bsky.social · Aug 11

Algonauts winning model:
thread: x.com/stephanedasc...
Paper: www.arxiv.org/abs/2507.22229
Code: github.com/facebookrese...

Stéphane d'Ascoli on X: "🏆 Thrilled to announce we reached 1st position in the Algonauts 2025 Competition with our 1B model of the brain watching movies! 📄Paper: https://t.co/TxVNNzQyeF 🧑‍💻Code: https://t.co/m7km1HkbAJ 💿Data: https://t.co/Hsq9u0vIZG ⚔️Challenge: https://t.co/8ms3AOQNMC 👇Thread: https://t.co/jVpoP7TJHl" / X

🏆 Thrilled to announce we reached 1st position in the Algonauts 2025 Competition with our 1B model of the brain watching movies! 📄Paper: https://t.co/TxVNNzQyeF 🧑‍💻Code: https://t.co/m7km1HkbAJ 💿Data: https://t.co/Hsq9u0vIZG ⚔️Challenge: https://t.co/8ms3AOQNMC 👇Thread: https://t.co/jVpoP7TJHl

x.com

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Jean-Rémi King @jeanremiking.bsky.social · Aug 11

We’re very happy to share 3 highlights of our Brain and AI team for #CCN2025 's week:

1. 🏆1st place for the Algonauts competition: paper, thtread and code below

2.🗣Keynote: Language in the Brain: 2025.ccneuro.org/k-and-t-lang...

3. 🚀Tutorial: Scale your decoding pipeline in the notebook

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Reposted by Jean-Rémi King

Cogan Lab @coganlab.bsky.social · Jun 13

Last week,
@danielsexton16.bsky.social
(7th year Neurosurgery Resident at Duke University) presented Linnea Evanson and colleagues' new paper on comparing the development of language from childhood to adulthood and how it relates to LLMs. This 🧵 explores our thoughts (🤍 & ❔)

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Jean-Rémi King @jeanremiking.bsky.social · Jun 18

Linearly readable information

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Jean-Rémi King @jeanremiking.bsky.social · Jun 17

Academia is often a long dry road, but every now and then you get the most amazing comment 🤗

Cogan Lab @coganlab.bsky.social · Jun 13

🤍1️⃣: They collected one of the most fascinating datasets we have ever seen. The number of patients in the dataset across all ranges of neurodevelopment is truly a feat to behold.

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