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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2311.04069 |
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| _version_ | 1866910639568453632 |
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| author | Chindemi, Giuseppe Girard, Benoit Bellone, Camilla |
| author_facet | Chindemi, Giuseppe Girard, Benoit Bellone, Camilla |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_04069 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | LISBET: a machine learning model for the automatic segmentation of social behavior motifs Chindemi, Giuseppe Girard, Benoit Bellone, Camilla Computer Vision and Pattern Recognition Machine Learning Quantitative Methods 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. |
| title | LISBET: a machine learning model for the automatic segmentation of social behavior motifs |
| topic | Computer Vision and Pattern Recognition Machine Learning Quantitative Methods |
| url | https://arxiv.org/abs/2311.04069 |