tribe-v2-neurosciencelisted

# TRIBE v2 Neuroscience In-silico neuroscience using Meta FAIR's TRIBE v2 — predict fMRI cortical responses to video, audio, or text using a single pretrained transformer. Run experiments on any hardware, without a scanner. ## What TRIBE v2 Is **TRansformer for In-silico Brain Experiments** (v2) is a brain encoding model released by Meta FAIR on March 26, 2026. It is **not** a language model — it predicts fMRI BOLD responses on the fsaverage5 cortical surface (~20,000 vertices per hemisphere) given multimodal sensory input. Architecture: ``` Video → V-JEPA2 (video encoder) Audio → Wav2Vec-BERT 2.0 (audio encoder) Text → LLaMA 3.2-3B (text encoder) ↓ Unified Transformer ↓ fsaverage5 mesh (~20k vertices) (n_timesteps × n_vertices) ``` Key properties: - **70x** resolution improvement over TRIBE v1 - **2-3x** accuracy improvement, zero-shot generalization to new subjects - **5-second temporal offset** built in — accounts for hemodynamic lag - **Log-linear scaling** with fMRI training data (like LLMs with tokens) - **License**: CC BY-NC 4.0 (non-commercial research only) - **HuggingFace**: `facebook/tribev2` - **Demo**: https://aidemos.atmeta.com/tribev2 --- ## Quick Start ### 1. Install TRIBE v2 ```bash # Requires Python 3.11+ git clone https://github.com/facebookresearch/tribev2 cd tribev2 pip install -e . ``` ### 2. Load the Model ```python from tribev2 import TribeModel model = TribeModel.from_pretrained("facebook/tribev2", cache_fol