Decoding Natural Behavior from Neuroethological Embedding

Animals engage with their natural environment through rich and dynamic brain activity. Understanding how neural population dynamics encode naturalistic behavior remains a fundamental challenge in systems neuroscience. Recent advances in deep learning-based behavior analysis and miniature fluorescence imaging have opened new avenues for investigating how the brain encodes natural behavior. Here, this study presents an integrated experimental and computational framework that combines the Social Behavior Atlas (SBeA), miniature Two-Photon Microscopy (mTPM), and Consistent EmBeddings of high-dimensional Recordings using Auxiliary variables (CEBRA) to decode complex behaviors from brain dynamics. This study uses naturalistic social interactions between freely moving mice as a model system, enabling high-resolution behavioral annotation alongside simultaneous neural imaging. This framework includes precise behavioral pose estimation, synchronized dual-mouse tracking, neural embedding alignment, and decoding of behavioral features directly from neural principal components. This study demonstrates that this approach achieves a decoding precision of 3. ± 1.5 pixels for posture and 89 ± 6% accuracy for motif decoding across animals, highlighting its robustness and generalizability. This method provides a powerful tool for exploring how brain activity reflects structured behavioral states, and it lays the groundwork for future studies of naturalistic neural coding principles.