Well, we did not expect that: in 10 days: 1.6K ⭐ on Github, 70K downloads on HuggingFace 🤗, >6M views on socials. 🧠 TRIBE v2, our new foundation model of the human brain responses to sight, sound, and language, has led to a surge of community demos, dozens of PRs and issues, and a level of engagement that rivals major flagships model at Meta (e.g. Llama4: 3.6M views, and DINOv3: 900K): https://lnkd.in/eQgqyDvf As this raised several questions, let me emphasize some elements of clarification: 1. #Precision: This model exclusively uses fMRI recordings. While powerful, these data are slow-paced proxy measurements of brain activity, and aggregate responses ~100k-3M neurons per data point. This means we are very far from a neuronal-level modeling of the brain. 2. #Scaling: We leveraged ~1,000 hours of naturalistic fMRI data. This is very substantial for a functional imaging study with naturalistic data; but still this is "small data" compared to medical or biology foundation models in general (e.g. structural MRI models are typically tens of thousands of individual brains). 3. #Generalization: The model shows surprising out-of-domain generalization, but it is **not** a replacement for new data collection. The model will almost surely fail in areas it hasn't seen: task-specific behaviors, memory protocols, touch sensation etc. And these are the "known unknowns" - I expect we'll discover many more unknown unknowns along the way. 4. #Impact: The goal of this model, and of our team in general, is fundamental research in neuroscience. While I am optimistic about how such foundation models will help clinical diagnosis, prognosis, and patient care, the physics of MRI is such that there is no clear path for making this kind of technology directly usable to consumer/wearable products. We're in for the science, hence the open sourcing: 📄 Paper: https://lnkd.in/e7cbunJp 💻 Code: https://lnkd.in/ebwBVuJp ▶️ Demo: https://lnkd.in/eEUVxP4S 🤗 Model: https://lnkd.in/e2T8nPJP
🚨 We're very happy to introduce TRIBE v2: a foundation model of the human brain's responses to sight, sound, and language. Leveraging 1,000+ hours of fMRI across 720 subjects, it generalizes… | Stéphane d'Ascoli | 16 comments
🚨 We're very happy to introduce TRIBE v2: a foundation model of the human brain's responses to sight, sound, and language. Leveraging 1,000+ hours of fMRI across 720 subjects, it generalizes zero-shot to new stimuli, tasks and people, finetunes efficiently, and enables in-silico experiments. ❓How does it work? Stemming from our v1, which won the Algonauts 2025 challenge, TRIBE v2 combines video, audio, and language embeddings to predict brain activity for any brain, then adapts to each individual. Key results: 📊 High-quality predictions — TRIBE v2 predicts brain activity across cortical and subcortical regions, significantly better than standard linear models, with a log-linear scaling law and no plateau in sight. 🎯 Zero-shot generalization — Without retraining, the predictions of TRIBE v2 are more correlated with group-averaged brain responses than almost any individual fMRI scan! A short finetuning step vastly improves over linear models trained, from scratch, on each individual. 🧪 In-silico experiments — Can we do useful experiments with TRIBE v2? Yes: classic vision and language paradigms replicate in-silico. It zero-shot recovers the FFA, PPA, EBA, VWFA, Broca's lateralization, and syntactic responses in STG — all without training on these artificial tasks. 🔍 Interpretability & multimodality — ICA on the weights rediscovers known functional networks (auditory, language, motion, default mode, visual) from naturalistic data alone. Ablating modalities further maps how vision, audition, and language integrate, with the largest gains at the temporo-parietal-occipital junction. 🧠 This effort is a step toward a foundation model of the human brain. Much remains to be understood, but we hope this opens a path for neuroscience, AI, and medical research alike. All code, weights, and a live demo are open — find it useful or mistaken in some conditions? Let us know, new test cases can only help improving this effort. 📄 Paper: https://lnkd.in/e7cbunJp 💻 Code: https://lnkd.in/ebwBVuJp ▶️ Demo: https://lnkd.in/eEUVxP4S 🤗 Model: https://lnkd.in/e2T8nPJP Joint work with Jérémy RAPIN, Yohann Benchetrit, Teon Brooks, Katie Begany, Joséphine Raugel, Hubert Banville and Jean-Rémi King. 🙏 Special thanks to Elisa Cascardi, Diego Marcos, Dominic Giardini, AI at Meta, and the open-source and neuroscience communities (in particular Lune Bellec and Bertrand Thirion for the amazing Courtois NeuroMod and IBC datasets) | 16 comments on LinkedIn
#brainfoundationmodels #neuroai #eeg #meg #fmri #neuroscience #machinelearning #opensource #fair | Jarod Lévy
Today, the Brain&AI team announces a new release with NeuralBench. 📊📊📊 A unified framework for benchmarking foundation models of brain activity. This wouldn’t have been possible without the tremendous work of Hubert Banville 🎉 🎉 🎉 - 🧠 36 EEG tasks - 🗄️ 94 public datasets. - 💻 Code: https://lnkd.in/dPDMwja3 - 📄 Paper: https://lnkd.in/ddDU2p6d This package allows systematic evaluation of any foundation models. NeuralBench addresses this by defining each task end-to-end with config files (data source, preprocessing, splits, optimizer, metrics, architecture) so all models can be evaluated on the same footing. We invite the community to contribute new tasks, datasets, and models, especially for fMRI, MEG, and iEEG. The long-term goal is a fully unified benchmark across neuroimaging tasks and modalities. What's in the first release, NeuralBench-EEG v1.0: - 36 EEG tasks across 94 public datasets, spanning motor imagery, clinical classification, cognitive decoding, and phenotype prediction. - Task-specific deep learning architectures (EEGNet, Deep4, EEGConformer, CTNet, ...) benchmarked side-by-side with recent EEG foundation models (BENDR, LaBraM, BIOT, CBraMod, LUNA, REVE). - Extensible to other neuroimaging modalities: the framework already runs MEG and fMRI tasks, leveraging our NeuralSet ecosystem for accessing brain imaging data and the broader neuroscientific software stack. - Released under the MIT license. Big team effort with Stéphane d'Ascoli, Simon Dahan, Jérémy RAPIN, Marlène Careil, Yohann Benchetrit, Saarang P., Antoine Ratouchniak, Lucy (Mingfang) Zhang, Elisa Cascardi, Katie Begany Teon Brooks, and Jean-Rémi King. And special thanks: Alexandre Gramfort Thomas Moreau Arnaud Delorme Bruno A. Pierre Guetschel #BrainFoundationModels #NeuroAI #EEG #MEG #fMRI #Neuroscience #MachineLearning #OpenSource #FAIR
Introducing TRIBE v2: AI Model Predicts Human Brain Responses | AI at Meta posted on the topic | LinkedIn
Today we're introducing TRIBE v2, 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 to create a digital twin of neural activity. It enables zero-shot predictions for new subjects, languages, and tasks, consistently outperforming standard modeling approaches. We’re releasing the model, codebase, paper, and an interactive demo to help researchers advance neuroscience, apply brain insights to build better AI, and use computational simulation to speed up breakthroughs in neurological disease diagnosis and treatment. Try the demo and learn more here: https://go.meta.me/tribe2 | 175 comments on LinkedIn
Introducing TRIBE v2: A Predictive Foundation Model Trained to Understand How the Human Brain Processes Complex Stimuli | Keith Doelling
This is some very cool work by some awesome colleagues Jean-Rémi King, and Teon Brooks! Seriously not enough good things can be said about how cool it is. You should enjoy it and play with it. And kudos to Meta for open sourcing it. At the same time, I'm already seeing posts about how the model will replace fMRI experiments as researchers will simulate how the brain "really works" instead of running costly experiments. I think this goes WELL beyond what its creators intend. We are already seeing that use of AI in science allows you to explore charted ideas more thoroughly and much more rapidly but slows us down in finding novel ideas (https://lnkd.in/eMR2akqt). At the same time, there is growing concern that LLM performance will collapse as they are increasingly trained on their own output (https://lnkd.in/eavgfyuY). Leaving neuroscience to AI simulations risks following the same fate, where we generate seemingly new findings without gaining new meaning. A mechanistic understanding of how the brain works (if that is still your goal) will be found at the margins, in errors and idiosyncrasies of neural function. What TRIBE provides is a super useful and cool instantiation of our current understanding on how and where neural activity is instantiated in the brain. But it won't help us make groundbreaking new findings of how neural circuits lead to cognition and behavior. Experiments on real human brains, may be costly, but they will always be necessary!
Introducing TRIBE v2: A Predictive Foundation Model Trained to Understand How the Human Brain Processes Complex Stimuli | Keith Doelling
This is some very cool work by some awesome colleagues Jean-Rémi King, and Teon Brooks! Seriously not enough good things can be said about how cool it is. You should enjoy it and play with it. And kudos to Meta for open sourcing it. At the same time, I'm already seeing posts about how the model will replace fMRI experiments as researchers will simulate how the brain "really works" instead of running costly experiments. I think this goes WELL beyond what its creators intend. We are already seeing that use of AI in science allows you to explore charted ideas more thoroughly and much more rapidly but slows us down in finding novel ideas (https://lnkd.in/eMR2akqt). At the same time, there is growing concern that LLM performance will collapse as they are increasingly trained on their own output (https://lnkd.in/eavgfyuY). Leaving neuroscience to AI simulations risks following the same fate, where we generate seemingly new findings without gaining new meaning. A mechanistic understanding of how the brain works (if that is still your goal) will be found at the margins, in errors and idiosyncrasies of neural function. What TRIBE provides is a super useful and cool instantiation of our current understanding on how and where neural activity is instantiated in the brain. But it won't help us make groundbreaking new findings of how neural circuits lead to cognition and behavior. Experiments on real human brains, may be costly, but they will always be necessary!
NeuralBench: A Unifying Framework to Benchmark NeuroAI Models | Hubert Banville
🧠 NeuralBench is now open source. Today we're releasing NeuralBench, a unified framework for benchmarking foundation models of brain activity, developed by the Brain & AI team at FAIR, Meta. 💻 Code: https://lnkd.in/dNJsgBgM 📄 White paper: https://lnkd.in/dvWMg7rx Brain foundation models are starting to show positive transfer to a range of downstream tasks, from brain-computer interfacing to clinical classification. But systematically evaluating them is hard: heterogeneous preprocessing pipelines, input structures, and adaptation methodologies make results difficult to compare. Most prior work also focuses on a narrow set of downstream tasks. NeuralBench addresses this by defining each task end-to-end with config files (data source, preprocessing, splits, optimiser, metrics, architecture) so all models can be evaluated on the same footing. What's in our first release, NeuralBench-EEG v1.0: ⚡ 36 EEG tasks across 94 public datasets, spanning motor imagery, clinical classification, cognitive decoding, and phenotype prediction. 🤖 Task-specific deep learning architectures (EEGNet, Deep4, EEGConformer, CTNet, ...) benchmarked side-by-side with recent EEG foundation models (BENDR, LaBraM, BIOT, CBraMod, LUNA, REVE). 🧩 Extensible to other neuroimaging modalities: the framework already runs MEG and fMRI tasks, leveraging our NeuralSet ecosystem for accessing brain imaging data and the broader neuroscientific software stack. 📜 Released under the MIT license. Help us make it better. Through our white paper, we invite the community to contribute new tasks, datasets, and models, especially for fMRI, MEG, and iEEG. The long-term goal is a fully unified benchmark across neuroimaging tasks and modalities. 🙏🙏🙏 This was a big team effort with Stéphane d'Ascoli, Simon Dahan, Jérémy RAPIN, Marlène Careil, Yohann Benchetrit, Jarod Lévy, Saarang P., Antoine Ratouchniak, Lucy (Mingfang) Zhang, Elisa Cascardi, Katie Begany, Teon Brooks, and Jean-Rémi King. Special thanks to Alexandre Gramfort, Thomas Moreau, Arnaud Delorme, Bruno A. and Pierre Guetschel for feedback and support. #Neuroscience #AI #NeuroAI #Python #OpenSource
Foundation Model Predicts Brain Responses to Visual and Auditory Stimuli | Elisa Cascardi posted on the topic | LinkedIn
Thrilled to share this work with the world! Today, we're releasing a foundation model that predicts how the human brain responds to almost any sight or sound -- and replace the need for human scans to significantly fast-track neuroscience and clinical research. 🧠 With this model, we can simulate brain responses to advance our understanding of the brain -- without the need for costly human brain scans 🌐 By using improved understanding of how efficient our brains perceive the world around us, we can guide the development of more advanced AI systems 👩⚕️ With computer-simulated experimentation, we can now speedup clinical research to diagnose neurological diseases and find treatments faster We've open sourced the model and code for researchers to use and build on, and an interactive demo for you to learn more -- see below! 📄 Paper: https://lnkd.in/e7cbunJp 💻 Code: https://lnkd.in/ebwBVuJp ▶️ Demo: https://lnkd.in/eEUVxP4S 🤗 Model: https://lnkd.in/e2T8nPJP So thrilled to be a part of this team with Stéphane d'Ascoli Jean-Rémi King Jérémy RAPIN Yohann Benchetrit Teon Brooks Katelyn Begany Joséphine Raugel Hubert Banville and for the great teamwork with Diego Marcos Dominic Giardini bringing this research to life! #neuroscience #AI #aiforscience #opensource #neuroAI
Addressing the Precision-Breadth-Simplicity Impossible Trinity in Psychological Research: A Comprehensive Exploration Approach
Psychological research faces a fundamental challenge—the Precision-Breadth-Simplicity (PBS) impossible trinity. While experimental findings are often precise and simple, they tend to be narrow in scope. Conversely, broad-and-simple concepts frequently lack precision. Developing theories that are both precise and broad is scientifically valuable but inevitably introduces complexity, which conflicts with humans’ cognitive limitations in processing complexity. To address this impossible trinity, I propose a comprehensive exploration (CE) approach—a data-guided theory-building framework that involves: (1) designing experimental conditions in a stimulus-driven way, with minimal upfront theoretical specification; (2) conducting experiments with tens of millions of observations (e.g., 40 million responses in Huang, 2025a); (3) modeling the results through iterative improvements; and (4) producing the outcome: a moderately complex quantitative information-processing model to integrate diverse empirical findings. Inspired by similar strategies that drove breakthroughs in artificial intelligence (e.g., ImageNet’s role in advancing object recognition), the CE approach offers a promising path toward more integrative psychological theories. Initial implementations in visual working memory research demonstrate both its practicality and potential to transform how we study mental processes.
#digitalbrainproject #neuroscience #artificialintelligence #openscience #callforprojects #research #ai #brain | Fondation Adolphe de Rothschild
The Rothschild Foundation Hospital is excited to launch the Digital Brain Project, a $5M collaborative research initiative to build an open source foundational model of the brain through open-access neural data. Apply by May 15th for up to $500k in funding. Help crack the neural code 👉digitalbrainproject.org Our objective is to develop a standardized, publicly available dataset of human brain recordings to accelerate fundamental research in cognitive neuroscience, AI system development, and clinical health. Our independent, multidisciplinary Scientific Committee oversees data collection, ethical review, privacy, and open science practices of our project — setting standards for large-scale brain research. The Digital Brain Project is conceived as an open collaborative infrastructure, at the intersection of neuroscience, artificial intelligence, and data science. It aims to accelerate data development, a key bottleneck to developing a foundational AI model of the human brain. Key Focus Areas: 1️⃣ Brain recording data related to visual processing, language functions and cognitive actions 2️⃣ Rigorous quality, privacy, and ethical control, along with procedures for standardizing data across sites 3️⃣ Support for teams selected by an international multidisciplinary Scientific Committee Call for applications: Research teams working in neuroscience, neuroimaging, or artificial intelligence are invited to submit their applications by May 15, 2026. Selected teams will receive up to $500k USD funding to support their work. We appreciate our Scientific Committee (Lune Bellec Anne-Marie Kermarrec Arthur Mensch Russell Poldrack Lucia Melloni Jose Alain Sahel) for their independent scientific leadership, and partners Université de Montréal and AI at Meta for collaborating on this exciting project. #DigitalBrainProject #Neuroscience #ArtificialIntelligence #OpenScience #Callforprojects #Research #AI #Brain -------------------------------------------------------------- Pierre Bourdillon Julie Boyle Elisa Cascardi Jean-Rémi King Guillaume Le Hénanff Amélie Yavchitz Julien Gottsmann Fabrice VERRIELE Charlotte Cardin-Taillia
✨🧠 Tribe v2, our latest model of human brain responses to sound, sight and language can now be (partly) explored on your phone📱: ▶️demo: https://lnkd.in/eT6uFMnm 📄paper: https://lnkd.in/eDbFsmHx… | Jean-Rémi King | 10 comments
✨🧠 Tribe v2, our latest model of human brain responses to sound, sight and language can now be (partly) explored on your phone📱: ▶️demo: https://lnkd.in/eT6uFMnm 📄paper: https://lnkd.in/eDbFsmHx 💻code: https://lnkd.in/eTtKHu_7 | 10 comments on LinkedIn
Precision Medicine in Neuroscience: Tools, Translation, and Implementation: A Workshop
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nubrain - A foundation model for neural decoding
Building the world's largest dataset of human brain activity.
Introducing TRIBE v2: A Predictive Foundation Model Trained to Understand How the Human Brain Processes Complex Stimuli
Understanding how the human brain processes the world around us is one of the greatest open challenges in neuroscience. Breakthroughs here could transform how we understand and treat neurological conditions affecting hundreds of millions of people — and improve AI systems by directly guiding their development from neuroscientific principles.
Gemma 4 Technical Report
We introduce Gemma 4, a new generation of open-weight, natively multimodal language models in the Gemma model family. Designed to advance compute efficiency and reasoning, the Gemma 4 model suite features dense and Mixture-of-Experts architectures, ranging from 2.3B to 31B parameters. Alongside improved vision and audio encoders for all model sizes, we propose a unified, encoder-free architecture for our 12B model, which ingests raw audio and image patches. Furthermore, we integrate a thinking mode, enabling Gemma models to generate reasoning traces prior to responding. We improve inference speed, memory, and compute efficiency, as well as long-context abilities through critical design choices. Gemma 4 establishes a leap in performance across STEM, multimodal, and long-context benchmarks, and rivals larger, frontier open models in human-rated tasks.
Massively Scalable Neurotechnologies
Backed by £50m, this programme sits within the Scalable Neural Interfaces opportunity space and is seeking radically new ways to deliver responsive neurotechnologies to the brain without brain surgery.
facebookresearch/tribev2
This repository contains the code to train and evaluate TRIBE v2, a multimodal model for brain response prediction