Department of Information and Computing Sciences, Utrecht University, Princetonplein 5, 3584 CC Utrecht, The Netherlands; Noldus Information Technology BV, Nieuwe Kanaal 5, 6709 PA Wageningen, The Netherlands.
Department of Cognitive Neuroscience, Radboud University Medical Centre, Kapittelweg 29, 6525 EN Nijmegen, The Netherlands; Noldus Information Technology BV, Nieuwe Kanaal 5, 6709 PA Wageningen, The Netherlands.
J Neurosci Methods. 2018 Apr 15;300:166-172. doi: 10.1016/j.jneumeth.2017.05.006. Epub 2017 May 8.
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 performance of classifiers.
With our novel, public dataset we encourage the development and validation of automated recognition methods. We are convinced that cross-dataset validation enhances our understanding of rodent interactions and facilitates the development of more sophisticated recognition methods. Combining them with adaptation techniques may enable us to apply automated recognition methods to a variety of animals and experiment settings.
社会行为是啮齿类动物模型的一个重要方面。利用视频分析和机器学习的自动化测量工具是手动注释的一种越来越有吸引力的替代方法。由于基于机器学习的方法需要进行训练，因此使用来自不同实验设置的数据验证它们非常重要。
为了开发和验证自动化测量工具，需要有经过注释的啮齿动物相互作用数据集。目前，此类数据集的可用性仅限于两个小鼠数据集。我们引入了第一个公开的大鼠社交互动数据集 RatSI。
我们通过将其用作大鼠相互作用识别方法的训练集，展示了新数据集的实用价值。我们表明，实验设置引起的行为变化会导致性能下降，这说明了跨数据集验证的重要性。因此，我们在方法中添加了一个简单的自适应步骤，从而提高了识别性能。
大多数现有的方法都是在一个实验设置中进行训练和评估的，这限制了评估对特定设置的预测能力。我们证明了跨数据集实验可以更深入地了解分类器的性能。
通过我们新颖的公共数据集，我们鼓励开发和验证自动化识别方法。我们相信，跨数据集验证可以增强我们对啮齿动物相互作用的理解，并促进更复杂的识别方法的发展。将它们与自适应技术相结合，可能使我们能够将自动化识别方法应用于各种动物和实验设置。
