Tucker James Lancaster Kathryn Leatherbury Kseniia Shilova Jeffrey Todd Streelman ^* Patrick McGrath ^*

• Georgia Institute of Technology, Atlanta, United States

Methods from Machine Learning (ML) and Computer Vision (CV) have proven powerful tools for quickly and accurately analyzing behavioral recordings. The computational complexity of these techniques, however, often precludes applications that require real-time analysis: for example, experiments where a stimulus must be applied in response to a particular behavior or samples must be collected soon after the behavior occurs. Here, we describe SARTAB (Scalable Automated Real-Time Analysis of Behavior), a system that achieves automated real-time behavior detection by continuously monitoring animal positions relative to behaviorally relevant Regions Of Interest (ROIs). We then show how we used this system to detect infrequent courtship behaviors in Pseudotropheus demasoni (a species of Lake Malawi African cichlid fish) to collect neural tissue samples from actively behaving individuals for multiomic profiling at single nucleus resolution. Within this experimental context, we achieve high ROI and animal detection accuracies (mAP @[.5 : .95] of 0.969 and 0.718 respectively) and 100% classification accuracy on a set of 32 manually selected behavioral clips. SARTAB is unique in that all analysis runs on low-cost, edge-deployed hardware, making it a highly scalable and energy-efficient solution for real-time experimental feedback. Although our solution was developed specifically to study cichlid courtship behavior, the intrinsic flexibility of neural network analysis ensures that our approach can be adapted to novel species, behaviors, and environments.