Learning to recognize rat social behavior: Novel dataset and cross-dataset application

Journal of Neuroscience Methods
Malte LorbachRemco C Veltkamp

Abstract

Social behavior is an important aspect of rodent models. Automated measuring tools that make use of video analysis and machine learning are an increasingly attractive alternative to manual annotation. Because machine learning-based methods need to be trained, it is important that they are validated using data from different experiment settings. To develop and validate automated measuring tools, there is a need for annotated rodent interaction datasets. Currently, the availability of such datasets is limited to two mouse datasets. We introduce the first, publicly available rat social interaction dataset, RatSI. We demonstrate the practical value of the novel dataset by using it as the training set for a rat interaction recognition method. We show that behavior variations induced by the experiment setting can lead to reduced performance, which illustrates the importance of cross-dataset validation. Consequently, we add a simple adaptation step to our method and improve the recognition performance. Most existing methods are trained and evaluated in one experimental setting, which limits the predictive power of the evaluation to that particular setting. We demonstrate that cross-dataset experiments provide more insight in the perfo...Continue Reading

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Citations

Jul 1, 2020·Neuropsychopharmacology : Official Publication of the American College of Neuropsychopharmacology·Lukas von ZieglerJohannes Bohacek
Jul 28, 2020·Neuropsychopharmacology : Official Publication of the American College of Neuropsychopharmacology·Oliver SturmanJohannes Bohacek
Mar 22, 2018·Medical & Biological Engineering & Computing·Zheyuan WangMaysam Ghovanloo

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