MouseGPT: A Large-scale Vision-Language Model for Mouse Behavior Analysis

Abstract

Analyzing animal behavior is crucial in neuroscience, yet deciphering its intricate dynamics remains challenging. Traditional machine vision approaches detect spontaneous behaviors but are constrained by limited interpretability and reliance on manual labeling, restricting exploration of the full behavioral spectrum. Here, we introduce MouseGPT, an integrated vision-language model and analytical framework that translates raw video of animal activity into natural language to automate open-vocabulary behavior analysis. Built upon a comprehensive dataset of over 42 million frames spanning diverse psychiatric conditions, MouseGPT maps multi-view or single-camera recordings directly into computable semantic representations. An agentic framework leverages these representations for detailed behavior profiling, cross-view stable clustering, and zero-shot novel behavior discovery without requiring labor-intensive annotation. Surpassing conventional kinematics-based pipelines and generalist vision-language models in behavioral precision and adaptability, MouseGPT establishes continuous semantic representations as a powerful tool for deciphering complex ethological dynamics in animal models.