9月24日担当の東京理科大学大学院 理学研究科 物理学専攻 の深澤健です。
明日は、以下の論文を紹介します。どうぞよろしくお願いいたします。
Simple Behavioral Analysis (SimBA) as a platform for explainable machine learning in behavioral neuroscience. Nastacia L. Goodwin, Jia J. Choong, Sophia Hwang, Kayla Pitts, Liana Bloom, Aasiya Islam, Yizhe Y. Zhang, Eric R. Szelenyi, Xiaoyu Tong, Emily L. Newman, Klaus Miczek, Hayden R. Wright, Ryan J. McLaughlin, Zane C. Norville, Neir Eshel, Mitra Heshmati, Simon R. O. Nilsson & Sam A. Golden.
Nature Neuroscience volume 27, pages1411–1424 (2024)
The study of complex behaviors is often challenging when using manual annotation due to the absence of quantifiable behavioral definitions and the subjective nature of behavioral annotation. Integration of supervised machine learning approaches mitigates some of these issues through the inclusion of accessible and explainable model interpretation. To decrease barriers to access, and with an emphasis on accessible model explainability, we developed the open-source Simple Behavioral Analysis (SimBA) platform for behavioral neuroscientists. SimBA introduces several machine learning interpretability tools, including SHapley Additive exPlanation (SHAP) scores, that aid in creating explainable and transparent behavioral classifiers. Here we show how the addition of explainability metrics allows for quantifiable comparisons of aggressive social behavior across research groups and species, reconceptualizing behavior as a sharable reagent and providing an open-source framework. We provide an open-source, graphical user interface (GUI)-driven, well-documented package to facilitate the movement toward improved automation and sharing of behavioral classification tools across laboratories.