Innate Fear Responses in Mice

The development of algorithms for high-accuracy animal tracking is fundamental to unravel the neural basis of behavior. The recent growth of Deeplabcut, an open source toolbox for marker-less pose estimation based on transfer learning with deep neural networks, promises to greatly enhance our ability to track animal pose with high-accuracy and high-efficiency, because it can match human labelling accuracy with minimal training data. Here, we implement Deeplabcut routines and develop a pipeline to accurately track the pose of mice undergoing defensive behaviors. Defensive behaviors are elicited by unconditioned threat stimuli such as predators, predator odors, and include flight, avoidance, freezing, risk assessment, and defensive threat/attack. We provide a MATLAB-based framework to analyze and classify these behavioral responses tracked by Deeplabcut. The coordinates predicted by Deeplabcut are passed through this algorithm, where they are further filtered, transformed from image to real world coordinates and then used to understand and classify the fear responses. This application, which we called FMT (fear-mouse-tracker) is especially designed for users not very experienced in using programming languages, and allows them to analyze Deeplabcut output data in a user-friendly manner, thus making this pipeline more accessible to a broader audience. We anticipate our pipeline as a starting point for more intricate analysis to unravel the neural fear circuit in mice.