Open‐Source Automation of the Proboscis Extension Response Assay: From Odor Delivery to Deep Learning Behavior Analysis

Honeybees (Apis mellifera) rely on olfaction for key behaviors, such as foraging and communication, making them valuable models for studying odor-guided behavior and learning. Traditional paradigms like the proboscis extension response (PER) have been used to investigate associative olfactory learning. However, these protocols typically involve manual odor delivery and visual scoring, which can limit precision and reproducibility. This article presents an automated version of the PER assay using Arduino microcontrollers for odor delivery, Bonsai software for real-time synchronization devices, video recording, and DeepLabCut (DLC) for behavioral tracking. Most hardware components, including bee holders and structural parts, are custom-designed and 3D printed, making the setup cost-effective and easily replicable. The system enables high-resolution objective quantification of responses, such as proboscis and antennal movements. Compared to manual methods, this open-source, modular setup improves throughput, standardization, and analytical accuracy in behavioral neuroscience research. © 2026 Wiley Periodicals LLC. Basic Protocol 1: Building the experimental setup Basic Protocol 2: Main circuitry assembly and software configuration Basic Protocol 3: Preparing the animals and performing the PER training Basic Protocol 4: From DLC video analysis to presenting your data.