High-Throughput Quantification of Sex Differences in Ferret Hunting Behavior Using Autonomous Robotics and Deep Learning

Introduction: : Behavioral analysis in animal research traditionally relies on manual scoring methods that are time-intensive, subjective, and prone to observer variability. This lack of standardization often fails to capture complex spatiotemporal dynamics at sufficient resolution, especially in animal-robot interactions which can generate high-frequency behavioral data that exceeds human scoring capabilities. We integrated preexisting tools—DeepLabCut (a deep learning-based pose estimation software) and Sphero Mini robots—to develop a rapid, objective, and automated method for ferret behavioral quantification to advance animal behavior research, particularly in terms of identifying early neurobehavioral biomarkers.

Materials and

Methods: : Twelve adult ferrets (8 female, 4 male) were tested in a 36×36-in arena with overhead video capture. A Sphero Mini robot (4 cm diameter) was programmed to mimic natural prey movement patterns (25-45 cm/s, random movement every 0.5 s, collision detection). DeepLabCut was trained (ResNet-50 backbone) using deep neural networks to track nose, skull, and bilateral paw positions relative to the moving Sphero throughout 10-minute trials. Custom MATLAB algorithms computed multi-dimensional behavioral metrics based on ferret-Sphero movement patterns—for instance, bout identification was calculated via a combination of dataset training, confidence thresholding, and minimum ferret-Sphero distances.

Results, Conclusions, and Discussions:: Females front‐load their engagement (see Fig. 1), initiating far more bouts in the first 60 s of sessions (19.1 ± 7.9 vs. 4.3 ± 1.5 bouts [females and males, respectively]; t(7.89)=4.97, P=.001) and concentrating interactions in the first half (63% ± 22% vs. 37% ± 13%; t(9.32)=2.65, P=.026). Males, however, show more extreme radial kinematics. Their fastest 10% of approach speeds towards the Sphero were higher (7.36 ± 0.85 vs. 5.95 ± 1.54 in/s; P=0.048), as were their fastest 10% of retreat speeds (7.28 ± 1.21 vs. 5.79 ± 1.61 in/s; P=0.048). Their peak decelerations and accelerations are also notably sharper (565.9 ± 43.4 vs. 403.6 ± 93.6 in/s², and 563.2 ± 42.2 vs. 403.5 ± 94.7 in/s²; both Wilcoxon rank-sum: P=.002). Overall, these results suggests that females prioritize rapid, sustained engagement, whereas males favor high‐velocity, sharply modulated maneuvers.

This is the first study to explore hunting behavior in ferrets by quantifying their movement parameters. We present this data as a baseline for future related work and will soon pair these behavioral phenotypes with MRI biomarkers in ferret Alzheimer’s models to identify early neurodegenerative signatures and accelerate preclinical screening. Given that motor decline can be seen with cognitive disorders in humans, this naturalistic and high-frequency hunting behavior offers a valuable window into ferret neuromotor function. More broadly, this approach provides a powerful methodology for automated, objective, and scalable behavioral quantification with applications spanning human-animal-robot interaction studies, comparative psychology, and neuroscience research.

Acknowledgements and/or References (Optional):: N/A