Stressor exposure is a predisposing risk factor for many psychiatric conditions such as PTSD and depression. However, stressors do not influence all individuals equally and in response to an identical stressor some individuals may be vulnerable while others are resilient. While various biological and behavioral factors contribute to vulnerability versus resilience, an individual's degree of control over the stressor is among the most potent. Even with only one experience with control over stress, behavioral control has been shown to have acute and long-lasting stress-mitigating effects. This suggests that control both blunts the response to acute stress and prepares the subject to be resilient to future stressors. In this review, we first summarize the evidence which suggests the ventromedial prefrontal cortex (vmPFC) is a critical component of stressor controllability circuits and a locus of neuroplasticity supporting the acute and long-lasting consequences of control. We next review the central endocannabinoid (eCB) system as a possible mediator of short and long-term synaptic transmission in the vmPFC, and offer a hypothesis whereby eCBs regulate vmPFC circuits engaged when a subject has control over stress and may contribute to the encoding of acute stress coping into long lasting stressor resilience.

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The description and quantification of social behavior in laboratory rodents is central to basic and translational research. Conventional ethological approaches to social behavior are fraught with challenges including bias, significant human effort and temporal accuracy. Here we show proof of principle that machine learning can be applied to laboratory tests of social decision making. Rats underwent social novelty preference tests which were scored both by hand and again by a convolutional neural network generated in the DeepLabCut computer vision package of Mathis and colleagues. The CNN generated temporally (30Hz) and locally (<5pixels) accurate identification of rat nose, eye and ear positions which were then used to compute social interaction and topography heat maps. In sum, hand-and computer-scoring were strongly correlated, and each identified significant preferences to interact with novel conspecifics which sets the stage for applying DeepLabCut analysis to other types of social interaction in the future.