I am a CNRS researcher at École Normale Supérieure currently detached to Meta AI, where I lead the Brain & AI team. This team aims to identify the brain and computational bases of human intelligence, with a focus on language. For this, we develop deep learning algorithms to decode and model brain activity recorded with MEG, EEG, electrophysiology and fMRI.
Some of our latest studies:
Visual summaries of some of our latest studies:
Dynadiff: Single-stage Decoding of Images from Continuously Evolving fMRI, arXiv 2025 👇
By Careil, Benchetrit and King,
Our latest brain-to-image decoding model is now available on HuggingFace: "Dynadiff: Single-stage Decoding of Images from Continuously Evolving fMRI", led by Marlène Careil and Yohann Benchetrit: - Paper: arxiv.org/pdf/2505.14556 - Github: github.com/facebookrese... - Thread: 👇
— Jean-Rémi King (@jeanremiking.bsky.social) May 22, 2025 at 3:55 PM
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Emergence of Language in the Developing Brain, pdf 2025 👇
By Evanson et al,
I'm very pleased to share our latest study: ‘Emergence of Language in the Developing Brain’, by L Evanson, P Bourdillon et al: - Paper: ai.meta.com/research/pub... - Blog: ai.meta.com/blog/meta-fa... - Thread below 👇
— Jean-Rémi King (@jeanremiking.bsky.social) May 15, 2025 at 6:00 PM
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Brain-to-Text Decoding: A Non-invasive Approach via Typing, arXiv 2025 👇
By Lévy et al,
Our Brain-to-Text study is now on arXiv, check out the thread! 👇
— Jean-Rémi King (@jeanremiking.bsky.social) Feb 26, 2025 at 3:50 PM
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From Thought to Action: How a Hierarchy of Neural Dynamics Supports Language Production, arXiv 2025 👇
By Zhang et al,
The thread from our latest study:
— Jean-Rémi King (@jeanremiking.bsky.social) Feb 18, 2025 at 3:48 PM
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Exca - Execute and cache seamlessly in python, Github 2025 👇
Do you use hydra for your ML pipeline? 😡 Wished it was [pure python] / [handling cache] / [simpler to use] ? 😊 Try exca: `pip install exca` ! github.com/facebookrese...
— Jean-Rémi King (@jeanremiking.bsky.social) Dec 17, 2024 at 10:14 AM
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A polar coordinate system represents syntax in large language models, arXiv 2024 👇
By Diego et al,
🚨We're very excited to share our latest study, by Pablo Diego and team: "A polar coordinate system represents syntax in large language models", 📄: Paper arxiv.org/abs/2412.05571 🪧: Poster tomorrow: neurips.cc/virtual/2024... 🧵: Thread 👇
— Jean-Rémi King (@jeanremiking.bsky.social) Dec 12, 2024 at 3:25 PM
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Hierarchical dynamic coding coordinates speech comprehension in the human brain, bioRxiv 2025 👇
By Gwilliams et al,
Go checkout @lauragwilliams.bsky.social 's latest paper on decoding the hierarchy of language from brain signals!
— Jean-Rémi King (@jeanremiking.bsky.social) May 3, 2024 at 9:46 AM
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Brain decoding: toward real-time reconstruction of visual perception, ICLR 2024 👇
By Benchetrit, Banville and King,
We’re very happy to share our latest study:
— Jean-Rémi King (@JeanRemiKing) October 18, 2023
‘Brain decoding: toward real-time reconstruction of visual perception’
led by @BenchetritYoha1 & @HubertBanville
- paper: https://t.co/HaqClxPiLt
- blog: https://t.co/py2AjqDlmQ
- summary: ⬇️ pic.twitter.com/Btg5CfmKaW
Negation mitigates rather than inverts the neural representations of adjectives, Plos Biology 2024 👇
By Arianna Zuanazzi and team,
How the brain compose minimal phrases.
— Jean-Rémi King (@JeanRemiKing) June 4, 2024
Go check out @AriannaZuanazzi 's study https://t.co/NkH4RzLUEZ pic.twitter.com/UVm4QOkHRT
Hierarchical dynamic coding coordinates speech comprehension in the brain, bioRxiv 2024 👇
By Laura Gwilliams and team
Delighted to share our new preprint! with @AlecMarantz, @DavidPoeppel and @JeanRemiKing:https://t.co/ZHzAX7ksLV
— Laura Gwilliams (@GwilliamsL) April 20, 2024
"Hierarchical dynamic coding coordinates speech comprehension in the brain"
Summary below 👇
1/8 pic.twitter.com/uyVpUdewiY
Tracking the neural codes for words and phrases during semantic composition, working memory storage and retrieval, Cell Reports 2024 👇
By Desbordes, Dehaene and & King
New paper from the lab on compositionality, just out in Cell Reports :
— Stanislas Dehaene (@StanDehaene) March 12, 2024
“Tracking the neural codes for words and phrases during semantic composition, working memory storage and retrieval”
Available in open access: https://t.co/qnRI3tquNj
A thread (1/n) pic.twitter.com/aeshmGX2EV
Evidence of a predictive coding hierarchy in the human brain listening to speech, Nature Human Behaviour 2023 👇
By Caucheteux, Gramfort & King
Our paper is out in Nature Human Behaviour🔥🔥
— Charlotte Caucheteux (@c_caucheteux) March 6, 2023
‘Evidence of a predictive coding hierarchy in the human brain listening to speech’
📄https://t.co/bkZ3AYMqDi
💡Unlike language models, our brain makes distant & hierarchical predictions
with @agramfort and @JeanRemiKing
Thread👇 pic.twitter.com/Jbs68EOaur
Toward a realistic model of speech processing in the brain with self-supervised learning, Neurips 2022 👇
By Millet*, Caucheteux*, Orhan, Boubenec, Gramfort, Dunbar, Pallier & King
🔥Preprint out:
— Jean-Rémi King (@JeanRemiKing) June 6, 2022
`Toward a realistic model of speech processing in the brain with self-supervised learning’:https://t.co/rJH6t6H6sm
by J. [Millet](https://jamju.github.io/)*, @c_caucheteux* and our wonderful team:
The 3 main results summarized below 👇 pic.twitter.com/mdrJpbrb3M
Decoding speech from non-invasive brain recordings, arXiv 2022 👇
By Défossez, Caucheteux, Kabeli, Rapin & King
“Decoding speech from non-invasive brain recordings”,
— Jean-Rémi King (@JeanRemiKing) August 31, 2022
Our latest study (on 169 participants!), by @honualx and our wonderful team @MetaAI
- paper: https://t.co/QiB7Io8af8
- blog: https://t.co/H2W4prbbuD
- illustrated summary: below👇 pic.twitter.com/39eMnJ4IDv
Brains and algorithms partially converge in natural language processing, bioRxiv 2020, Nature Communications Biology 2022 👇
By Caucheteux & King
🎉Paper out: ‘Brains and algorithms partially converge in natural language processing’
— Jean-Rémi King (@JeanRemiKing) February 23, 2022
by @c_caucheteux, and now freely available at Nature @CommsBio:https://t.co/MpenOUaKwS
The summary thread below 👇 pic.twitter.com/gMruZgGIOv
Deep language algorithms predict semantic comprehension from brain activity, Nature Scientific Report 2022 👇
By Caucheteux, Gramfort & King
Our latest paper is out:
— Jean-Rémi King (@JeanRemiKing) June 9, 2021
GPT-2’s activations predict the degree of semantic comprehension in the human brain, by @c_caucheteux, @agramfort & @JeanRemiKinghttps://t.co/Xjc8IaXT64
The summary thread below 👇
1/8 pic.twitter.com/GF39doySMu
Deep Recurrent Encoder: A scalable end-to-end network to model magneto-encephalography at scale, NBDT 2022 👇
By Chehab*, Défossez*, Loiseau, Gramfort & King
Deep learning improves the analysis of time-resolved brain signals by ... 3️⃣ folds!
— Jean-Rémi King (@JeanRemiKing) April 7, 2021
Check out our latest paper by @lomarchehab*, @honualx*, @loiseau_jc, and @agramfort:
https://t.co/QxTxoySnBs
Below is the summary thread 👇 pic.twitter.com/h1WcoGm7UD
Neural dynamics of phoneme sequences reveal position-invariant code for content and order, Nature Communications 2022 👇
By Gwilliams, King, Marantz & Poeppel
our new paper "Neural dynamics of phoneme sequencing" is now on bioRxiv!https://t.co/jeTipPTXuf
— Laura Gwilliams (@GwilliamsL) April 6, 2020
conducted with dream-team @JeanRemiKing @AlecMarantz @davidpoeppel, we use MEG to study how phonemes are processed in continuous naturalistic speech
short summary in thread below:
1/8 pic.twitter.com/yT5bN2PfHw
Long-range and hierarchical language predictions in brains and algorithms, arXiv 2021 👇
By Caucheteux, Gramfort & King
‘Long-range and hierarchical language predictions in brains and algorithms’
— Jean-Rémi King (@JeanRemiKing) November 30, 2021
Check-out our latest paper https://t.co/rwfVCVLRWA by @c_caucheteux @agramfort @JeanRemiKing
tl;dr: Unlike deep language models, the brain makes long-range & hierarchical predictions
Thread below👇 pic.twitter.com/iP0BEYBjip
Model-based analysis of brain activity reveals the hierarchy of language, EMNLP 2021 👇
By Caucheteux, Gramfort & King
"Model-based analysis of brain activity reveals the hierarchy of language"
— Jean-Rémi King (@JeanRemiKing) October 12, 2021
Our EMNLP paper by @c_caucheteux @agramfort & myself is out: https://t.co/BxvrbZNkPt
It shows (w/ emoji-based equations!) how deepnets can efficiently recover the language hierarchy in the
Summary👇
1/7 pic.twitter.com/3QOcTfsivu
Disentangling Syntax and Semantics in the Brain with Deep Networks, ICML 2021 👇
By Caucheteux, Gramfort & King
"Disentangling Syntax and Semantics in the Brain with Deep Networks"
— Jean-Rémi King (@JeanRemiKing) July 22, 2021
Go check out our latest @icmlconf paper : https://t.co/4YPK7vJRsJ
by @c_caucheteux, @agramfort & @JeanRemiKing
The summary thread below 👇 pic.twitter.com/v0kxjtBtVP
Inductive biases, pretraining and fine-tuning jointly account for brain responses to speech, arXiv 2021 👇
Do convolutional networks process speech sounds like our brains does?
— Jean-Rémi King (@JeanRemiKing) March 9, 2021
Check out our latest study with Juliette [Millet](https://jamju.github.io/): https://t.co/dcupYxSxKA
Here is the summary thread 👇: 1/n pic.twitter.com/LI6kr8PY9j
Bifurcation in brain dynamics reveals a signature of conscious processing independent of report, Nature Communications 2021 👇
By Sergent, Corazzol, Labouret, Stockart, Wexler, King, Meyniel & Pressnitzer
Most work on the neural basis of consciousness relies on self-report, however @MmeJeanserre, @JeanRemiKing et al. suggest bifurcation in EEG brain dynamics may reflect an independent signature of conscious perception @Univ_Paris @Cognition_ENS @mne_python https://t.co/nHMPaSVxnU pic.twitter.com/n4TXgh2XNt
— Nature Communications (@NatureComms) February 20, 2021
The Human Brain Encodes a Chronicle of Visual Events at Each Instant of Time Through the Multiplexing of Traveling Waves, Journal of Neuroscience 2021 👇
"The Human Brain encodes a Chronicle of Visual Events at each Instant of Time", by @valentinwyart and I: the tl;dr thread: https://t.co/YfLLZ1ZStr pic.twitter.com/iySGP52al9
— Jean-Rémi King (@JeanRemiKing) November 19, 2019
Back-to-back regression: Disentangling the influence of correlated factors from multivariate observations, Neuroimage 2020 👇
By King, Charton, Lopez-Paz & Oquab
Back-to-back regression: Disentangling the influence of correlated factors from multivariate observations.
— Jean-Rémi King (@JeanRemiKing) July 9, 2020
Our latest paper with @f_charton, David Lopez Paz & Maxime Oquab at @facebookai is now freely available at Neuroimage: https://t.co/2hBgODEeAw
Here's the summary thread ⤵️ pic.twitter.com/i1ZLF2dZ5e
COVID-19: the promises and pitfalls of Machine Learning, Nature Machine Intelligence 2020 👇
By Peiffer-Smadja, Maatoug, Lescure, D’Ortenzio, Pineau & King
"#MachineLearning for #COVIDー19 needs global collaboration and data-sharing"
👉https://t.co/ouY7MYX59p
#ArtificialIntelligence #SARSCoV2 pic.twitter.com/lZsZh8Hqvm</p>— Nathan Peiffer-Smadja (@nathanpsmad) May 22, 2020
Recurrent Processes Emulate a Cascade of Hierarchical Decisions, eLife 2020 👇
0/9: "Recurrent Processes Emulate a Cascade of Hierarchical Decisions", by @GwilliamsL and I, the tl;dr thread:
— Jean-Rémi King (@JeanRemiKing) November 15, 2019
3/9 Their average brain response confirm a fast feedforward recruitment of their visual hierarchies pic.twitter.com/Y39WYwJ2Yx
— Jean-Rémi King (@JeanRemiKing) November 15, 2019
Public interventions:
- BBC Tech Life: Decoding the brain
- MIT Techonological Review: Meta has an AI for brain typing, but it’s stuck in the lab
- France Info: Meta met au point une technique permettant d’écrire une phrase par la pensée, sans avoir recours à un implant cérébral
- World Science Festival: The little voice in our head.
- France TV documentary: L’Homme à la Machine
- Yahoo! Finance: Big Tech sees neurotechnology as its next AI frontier.
- France Culture: La Science CQFD: Mettre les points sur les IA.
- Le Monde: Artificial Intelligence deciphers images perceived by the human brain
- Epsiloon Podcast: Dans la tête de l’IA la plus puissante du monde.
- Pontifical Academy: The unreasonable effectiveness of artificial intelligence in the neurosciences.
- Meta AI: Toward a real-time decoding of images from brain activity.
- Les Echos: “Dans le sillage de Yann Lecun, trois autres Francais chercheurs stars de l’IA.” (#HumbleTitleHello)
- Time: Meta Is Building AI That Reads Brainwaves. The Reality, So Far, Is Messy.
- Meta AI: Using AI to decode speech from brain activity.
- The Language Neuroscience Podcast: Natural language processing, and the brain.
- Pour la Science: Quand les IA miment l’activité cérébrale.
- La Recherche: Décrypter les réseaux du langage dans le cerveau.
- Towards Data Science Podcast: Does the brain run on deep learning?
- Quanta: Self-Taught AI Shows Similarities to How the Brain Works.
- Meta AI: Studying the brain to build AI that processes language as people do.
- Ecole Normale Supérieure: Jean-Rémi King, entre I.A. et neurosciences (video)
- New York Times: ‘It’s Gigantic’: A New Way to Gauge the Chances for Unresponsive Patients
- CBC News: Your brain registers more than you think you see, NYU researchers find.