Abstract
Animal behavior is crucial for understanding both normal brain function and dysfunction. To facilitate behavior analysis of mice within their home environments, we developed DeepEthoProfile, an open-source software powered by a deep convolutional neural network for efficient behavior classification. DeepEthoProfile requires no spatial cues for either training or processing and is designed to perform reliably under real laboratory conditions, tolerating variations in lighting and cage bedding. For data collection, we introduce EthoProfiler, a mobile cage rack system capable of simultaneously recording up to 10 singly housed mice. We used 36 h of manually annotated video data sampled in 5 min clips from a 48 h video database of 10 mice. This published dataset provides a reference that can facilitate further research. DeepEthoProfile achieved an overall classification accuracy of over 83%, comparable with human-level accuracy. The model also performed on par with other state-of-the-art solutions on another published dataset ( Jhuang et al., 2010). Designed for long-term experiments, DeepEthoProfile is highly efficient-capable of annotating nearly 2,000 frames per second and can be customized for various research needs.