In a step towards advancing neuroscience and artificial intelligence, Meta has introduced TRIBE v2 (Trimodal Brain Encoder), a foundation model designed to predict how the human brain responds to different inputs.
The model is built to mirror brain activity across sight, sound, and language. Meta said it can help researchers run experiments more efficiently and may support progress towards superintelligence, where AI systems can understand and respond to the physical world similar to humans.
Studying the brain usually requires fresh recordings for every experiment, making the process slow, expensive, and difficult to scale. TRIBE v2 aims to reduce this challenge by converting months of lab work into seconds of computation.
“Today, we’re releasing TRIBE v2. This foundation model acts as a digital mirror of human brain activity in response to sight, sound and language – transforming months of lab work into seconds of computation,” Meta wrote in a blog post.
The model works through a 3-stage pipeline. First, it converts sounds, visuals, and text into numerical data for analysis. Next, it combines this data to identify patterns in how humans process information. Finally, it predicts which areas of the brain will activate in response to specific inputs and links them to actual brain activity.
Meta said TRIBE v2 offers improved accuracy compared to its earlier version. It is trained on more people and larger datasets, allowing it to perform better in new situations and deliver more precise predictions.
Brain activity is often recorded using fMRI, but such data can include noise caused by movement or unrelated signals. Instead of relying on raw data, TRIBE v2 predicts what a typical brain response should look like. Meta noted that in some cases, these predictions can match average brain responses more closely than individual scans.
The company has made the TRIBE v2 paper, code, and model weights open source. This is expected to support faster research across neuroscience, artificial intelligence, and healthcare.
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