Harnessing Machine Learning for Decoding <em>Caenorhabditis elegans</em> Behavior

Caenorhabditis elegans (C. elegans) has emerged as a genetically tractable model for decoding the neural and molecular underpinnings of behavior. Traditional methods of behavioral analysis are limited in scalability, resolution, and reproducibility, especially in high-throughput and longitudinal studies. Recent advances in machine learning have revolutionized the field, offering powerful tools for automated behavior tracking, posture estimation, phenotype classification, and neural decoding. This review systematically categorizes and evaluates the growing repertoire of machine learning models applied to C. elegans behavioral analysis, including handcrafted classifiers, deep neural networks, graph models, connectome-constrained simulators, and recurrent neural networks. It highlights their applications in decoding locomotion, aging, egg-laying, mating, sensory-guided navigation, and internal state transitions. Furthermore, it discusses the computational architecture, accuracy, interpretability, and translational relevance of these tools. Moreover, the review also addresses challenges such as model generalizability, reproducibility, and integration into lab workflows. The integration of machine learning into behavioral neuroscience underscores its transformative potential with C. elegans acting as a central model system linking the fields of biology and artificial intelligence.