Deep learning improves automated rodent behavior recognition within a specific experimental setup.

Automated observation and analysis of rodent behavior is important to facilitate research progress in neuroscience and pharmacology. Available automated systems lack adaptivity and can benefit from advances in AI. In this work we compare a state-of-the-art conventional rat behavior recognition (RBR) system to an advanced deep learning method and evaluate its performance within and across experimental setups. We show that using a multi-fiber network (MF-Net) in conjunction with data augmentation strategies within-setup dataset performance improves over the conventional RBR system. Two new methods for video augmentation were used: video cutout and dynamic illumination change. However, we also show that improvements do not transfer to videos in different experimental setups, for which we discuss possible causes and cures.