YORU: Animal behavior detection with object-based approach for real-time closed-loop feedback

The advent of deep learning methodologies for animal behavior analysis has revolutionized neuroethology studies. However, the analysis of social behaviors, characterized by dynamic interactions among multiple individuals, continues to represent a major challenge. In this study, we present "YORU" (your optimal recognition utility), a behavior detection approach leveraging an object detection deep learning algorithm. Unlike conventional approaches, YORU directly identifies behaviors as "behavior objects" based on the animal's shape, enabling robust and accurate detection. YORU successfully classified several types of social behaviors in species ranging from vertebrates to insects. Furthermore, YORU enables real-time behavior analysis and closed-loop feedback. In addition, we achieved real-time delivery of photostimulation feedback to specific individuals during social behaviors, even when multiple individuals are close together. This system overcomes the challenges posed by conventional pose estimation methods and presents an alternative approach for behavioral analysis.