We’re very happy to share our latest paper: “Boosting Brain-to-Image Decoding with TRIBE v2 Data Augmentation“ with up to 68% gain in brain-to-image image decoding! 📝arxiv.org/abs/2606.06345 🧵Details in thread below:

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 🧵

Announcing: a new interactive tool for a quick and simple start of encoding or encoding: 🧠 fMRI, EEG, MEG, iEEG, spikes… preprocessing 💬 text 🔊 audio ▶️ video 🏞️ image… embeddings 📦 pip install neuralset 🔍facebookresearch.github.io/n… #NeuroAI #OpenSource

🧠 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/facebookresearch/… Paper: ai.meta.com/research/publica…

Meta FAIR Releases NeuralSet: A Python Package for Neuro-AI That Supports fMRI, M/EEG, Spikes, and HuggingFace Embeddings Every other tool supports some. NeuralSet supports all. Key Points: → One unified PyTorch DataLoader for fMRI, MEG, EEG, iEEG, fNIRS, EMG, and spike recordings → Native HuggingFace integration: DINOv2, CLIP, Wav2Vec, Whisper, GPT-2, LLaMA, VideoMAE — out of the box → Stimulus embeddings are always temporally aligned with neural recordings — no manual alignment code → Pydantic validation catches config errors at initialization, not hours into a cluster run → Same script runs on your laptop and a SLURM cluster — one config flag change → Hash-based caching means running a large language model over an entire corpus happens once, then never again The core design principle is structure–data decoupling. The entire experiment is represented as lightweight event metadata — a pandas DataFrame. No raw signals are loaded until a PyTorch DataLoader actually needs them. You can filter, explore, and recombine terabyte-scale datasets without touching a single file. 📦 pip install neuralset ↗ Full analysis: marktechpost.com/2026/04/29/… ↗ Docs: facebookresearch.github.io/n… ↗ Paper: kingjr.github.io/files/neura… @AIatMeta @Meta_Engineers @Meta @JeanRemiKing @JRaugel @JarodLevy @EvansonLinnea @LucyZ47712090 @juliengadonneix @asantosrevilla @SHouhamdi98568 @BenchetritYoha1 @stephanedascoli @DahanSimon @hubertjbanville @teonbrooks @klbegany @shubhkhanna__ @PierreOrhan @alexisthual @honualx ...

Today we’re happy to release the framework which powered TRIBE v2 and many other projects from the team! We hope this can speed up research in Neuro-AI 🧠

Excited to announce neuralset! 🚀🚀🚀 The new library that turns raw neuro-recordings into AI-ready tensors in seconds. 📦 `pip install neuralset` 💻 Code: lnkd.in/eamwxBUY 📄 Paper: lnkd.in/epbreyDy Details below 🧵👇

💫 Introducing NeuralSet: a simple, fast, scalable Python package for Neuro-AI 📦 pip install neuralset 📄 kingjr.github.io/files/neura… 🔍 facebookresearch.github.io/n… Supports 🧠 fMRI, EEG, MEG, ECoG, spike… preprocessing 💬 text 🔊 audio ▶️ video 🏞️ image… embeddings 🧵 Details👇

Great opportunity to work with the Rothschild Foundation Hospital on neuro foundation models.

Exciting new work by @juliengadonneix! Super happy to be part of it.

🔥 We're very pleased to release our latest study 🧠: "Temporal structure of the language hierarchy within small cortical patches" 📄 arxiv.org/abs/2604.03021 🧵 Summary thread below: 1/7

🚨 We're very happy to introduce TRIBE v2: a foundation model of the brain's responses to sight, sound & language. 📄 Paper: ai.meta.com/research/publica… ▶️ Demo: aidemos.atmeta.com/tribev2/ 💻 Code: github.com/facebookresearch/… 🤗 Model: huggingface.co/facebook/trib…

🧠Happy to share the 2025 highlights of our Brain & AI team @AIatMeta, pushing, one paper at a time, towards a unified model of cognition in the human brain. 🧵The thread ⬇️ #NeuroAI

Come check out our tutorial on scaling language decoding at CCN!