The locomotor activity (LMA) test is widely used to study spontaneous activity in rodents and the effects of drug treatments. However, standard automated metrics such as “distance traveled” often fail to capture the true complexity of behavior in this test, while manual annotations are labor intensive and prone to variability. Although recent advances in computational ethology, both unsupervised and supervised, have pushed forward analysis of spontaneous activity, challenges remain in providing interpretability and in temporal modeling. In this work, we adapt ActionFormer, a transformer-based architecture for temporal action localization, to analyze spontaneous activity of laboratory mice. ActionFormer significantly outperforms prior state-of-the-art methods in Average Precision (AP) across 7 common behaviors, and can capture characteristic drug-induced effects. By introducing behavior signatures, we showcase ActionFormer’s ability to uncover group differences and dose-response relationships in preclinical studies. Our findings demonstrate the potential of ActionFormer to further advance the frontier of computational ethology for laboratory rodents.