Psychedelics are a class of drugs that produce unique subjective effects via agonist actions at the 5-hydroxytryptamine 2A receptor (5-HT 2A ). The 5-HT 2A -mediated head twitch response (HTR) in rodents is used as a reliable proxy for psychedelic drug activity in humans, but existing methods for measuring HTRs require surgery or time-consuming visual scoring. In the present work, we validated a simple noninvasive method for quantitating HTRs using computer-based analysis of experimental video recordings. Male C57BL/6J mice received injections of the 5-HT 2 receptor agonist (±)2,5-dimethoxy-4-iodoamphetamine (DOI; 0.03−3 mg/kg, s.c.) and were placed into cylindrical arenas. High frame rate videos were recorded via cameras mounted above the arenas. Antagonist experiments, which entailed pretreatment with the 5-HT 2A antagonist M100907 (0.01 or 0.1 mg/kg s.c.) prior to DOI (1 mg/kg s.c.), were also recorded. The experimental videos were analyzed for HTRs using a newly developed feature of a commercial software package and compared to visual scoring carried out by trained observers. As expected, DOI produced dose-related increases in HTRs, which were blocked by M100907. Computer scoring was positively correlated with visual scoring, and no statistical difference between the two methods was found. The software captured nearly all visually observed HTRs, false positives induced by other behaviors (e.g., grooming) were rare and easily identified, and results were improved by optimizing lighting conditions. Our findings demonstrate the utility of combining high frame rate video recordings with commercial software analyses to measure HTRs, validating an additional reliable method to study psychedelic-like drug activity in mice.

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This paper presents a vision-based, computationally efficient method for simultaneous robot motion estimation and dynamic target tracking while operating in GPS-denied unknown or uncertain environments. While numerous visionbased approaches are able to achieve simultaneous ego-motion estimation along with detection and tracking of moving objects, many of them require performing a bundle adjustment optimization, which involves the estimation of the 3D points observed in the process. One of the main concerns in robotics applications is the computational effort required to sustain extended operation. Considering applications for which the primary interest is highly accurate online navigation rather than mapping, the number of involved variables can be considerably reduced by avoiding the explicit 3D structure reconstruction and consequently save processing time. We take advantage of the light bundle adjustment method, which allows for ego-motion calculation without the need for 3D points online reconstruction, and thus, to significantly reduce computational time compared to bundle adjustment. The proposed method integrates the target tracking problem into the light bundle adjustment framework, yielding a simultaneous ego-motion estimation and tracking process, in which the target is the only explicitly online reconstructed 3D point. Our approach is compared to bundle adjustment with target tracking in terms of accuracy and computational complexity, using simulated aerial scenarios and real-imagery experiments.