{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/lisbet-a-self-supervised-transformer-model","title":"LISBET: a machine learning model for the automatic segmentation of social behavior motifs","arxiv_id":"2311.04069","date":"2023-11-07","proceeding":null,"authors":["Giuseppe Chindemi","Benoit Girard","Camilla Bellone"],"abstract":"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.","url_abs":"https://arxiv.org/abs/2311.04069v2","url_pdf":"https://arxiv.org/pdf/2311.04069v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"lisbet-a-self-supervised-transformer-model","repo_url":"https://github.com/bellonelab/lisbet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"self-supervised-learning","task_name":"Self-Supervised Learning"},{"task_slug":"feature-selection","task_name":"feature selection"}],"methods":[{"method_slug":"feature-selection","method_name":"Feature Selection"},{"method_slug":"focus","method_name":"Focus"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}