Analysis of antennal responses to motion stimuli in the honey bee by automated tracking using DeepLabCut

Abstract

Considering recent developments in gene manipulation methods for honey bees, establishing simple, robust, and indoor assay systems which can analyze behavioral components in detail is important for the rise of honey bee behavioral genetics. We focused on the antennal movements of the honey bee, which are used for not only multimodal sensory perception but also interactions between individuals. We developed an experimental system for analyzing the antennal responses (ARs) of the honey bee using DeepLabCut, a markerless posture-tracking tool using deep learning. The tracking of antennal movements during the presentation of vertical (downward and upward) motion stimuli using DeepLabCut successfully detected the ARs reported in the previous studies, where bees tilted their antennae in the direction opposite to the motion stimuli. In addition, we successfully detected ARs in response to horizontal (forward and backward) motion stimuli. An investigation of the developmental maturation of honey bee ARs showed that ARs to motion stimuli were not detected in bees immediately after emergence but became detectable through post-emergence development in an experience-independent manner. Furthermore, unsupervised clustering analysis using multidimensional data created by processing tracking data using DeepLabCut classified antennal movements into different clusters, suggesting that data-driven behavioral classification can apply to AR paradigms. These results reveal novel AR to visual stimuli and developmental maturation of ARs and suggest the efficacy of data-driven analysis for behavioral classification in behavioral studies of the honey bee.