Quantitative descriptions of animal behavior are essential to study the neural substrates of cognitive and emotional processes. Analyses of naturalistic behaviors are often performed by hand or with expensive, inflexible commercial software. Recently, machine learning methods for markerless pose estimation enabled automated tracking of freely moving animals, including in labs with limited coding expertise. However, classifying specific behaviors based on pose data requires additional computational analyses and remains a significant challenge for many groups. We developed BehaviorDEPOT (DEcoding behavior based on POsitional Tracking), a simple, flexible software program that can detect behavior from video timeseries and can analyze the results of experimental assays. BehaviorDEPOT calculates kinematic and postural statistics from keypoint tracking data and creates heuristics that reliably detect behaviors. It requires no programming experience and is applicable to a wide range of behaviors and experimental designs. We provide several hard-coded heuristics. Our freezing detection heuristic achieves above 90% accuracy in videos of mice and rats, including those wearing tethered head-mounts. BehaviorDEPOT also helps researchers develop their own heuristics and incorporate them into the software’s graphical interface. Behavioral data is stored framewise for easy alignment with neural data. We demonstrate the immediate utility and flexibility of BehaviorDEPOT using popular assays including fear conditioning, decision-making in a T-maze, open field, elevated plus maze, and novel object exploration.

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Behavior is often dichotomized into model-free and model-based systems1, 2. Model-free behavior prioritizes associations that have high value, regardless of the specific consequence or circumstance. In contrast, model-based behavior involves considering all possible outcomes to produce behavior that best fits the current circumstance. We typically exhibit a mixture of these behaviors so we can trade-off efficiency and flexibility. However, substance use disorder shifts behavior more strongly towards model-free systems, which produces a difficulty abstaining from drug-seeking due to an inability to withhold making the model-free high-value response3–10. The lateral hypothalamus (LH) is implicated in substance use disorder11–17and we have demonstrated that this region is critical to Pavlovian cue-reward learning18, 19. However, it is unknown whether learning occurring in LH is model-free or model-based, where the necessary teaching signal comes from to facilitate learning in LH, and whether this is relevant for learning deficits that drive substance use disorder. Here, we reveal that learning occurring in the LH is model-based. Further, we confirm the existence of an understudied projection extending from dopamine neurons in the ventral tegmental area (VTA) to the LH and demonstrate that this input underlies model-based learning in LH. Finally, we examine the impact of methamphetamine self-administration on LH-dependent model-based processes. These experiments reveal that a history of methamphetamine administration enhances the model-based control that Pavlovian cues have over decision-making, which was accompanied by a bidirectional strengthening of the LH to VTA circuit. Together, this work reveals a novel bidirectional circuit that underlies model-based learning and is relevant to the behavioral and cognitive changes that arise with substance use disorders. This circuit represents a new addition to models of addiction, which focus on instrumental components of drug addiction and increases in model-free habits after drug exposure3–10.

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