Social behavior is crucial for survival in many animal species, and a heavily investigated research subject. Current analysis methods generally rely on measuring animal interaction time or annotating predefined behaviors. However, these approaches are time consuming, human biased, and can fail to capture subtle behaviors. Here we introduce LISBET (LISBET Is a Social BEhavior Transformer), a machine learning model for detecting and segmenting social interactions. Using self-supervised learning on body tracking data, our model eliminates the need for extensive human annotation. We tested LISBET in three scenarios across multiple datasets in mice: supervised behavior classification, unsupervised motifs segmentation, and unsupervised animal phenotyping. Additionally, in vivo electrophysiology revealed distinct neural signatures in the Ventral Tegmental Area corresponding to motifs identified by our model. In summary, LISBET automates data annotation and reduces human bias in social behavior research, offering a promising approach to enhance our understanding of behavior and its neural correlates.

翻译：社交行为对许多动物物种的生存至关重要，是一个被广泛研究的重要课题。当前的分析方法通常依赖于测量动物互动时间或对预定义行为进行标注。然而，这些方法耗时费力、存在人为偏差，且可能无法捕捉细微的行为模式。本文介绍LISBET（LISBET Is a Social BEhavior Transformer），一种用于检测和分割社交互动的机器学习模型。通过对身体追踪数据进行自监督学习，我们的模型消除了对大量人工标注的需求。我们在小鼠的多个数据集上测试了LISBET在三种场景下的应用：有监督行为分类、无监督基元分割以及无监督动物表型分析。此外，体内电生理学记录显示，腹侧被盖区存在与模型识别出的基元相对应的独特神经活动特征。总之，LISBET实现了数据标注的自动化并减少了社交行为研究中的人为偏差，为深化我们对行为及其神经关联的理解提供了一种前景广阔的方法。