Generating whole-brain neural activity and behavior through unified latent dynamics

Understanding how high-dimensional neural activity and behavior emerge from shared underlying dynamics remains a fundamental challenge in neuroscience. Addressing this problem is key to enabling digital twins that can faithfully reproduce and predict the multiscale brain-behavior dynamics of living systems. Here we present NEBULA (NEural and Behavioral modeling through Unified LAtent dynamics), a generative framework that jointly models whole-brain neural activity and behavior. Using brain-wide recordings from C. elegans, the model learns a unified latent dynamical structure that supports long-horizon generation of neural and behavioral trajectories, realistic simulations of behavior, and targeted virtual interventions. Perturbations of the learned dynamics reveal behaviorally relevant transition points, whereas steering interventions enable controlled manipulation of neural and behavioral states without retraining. These results establish a framework for linking brain dynamics to behavior in a living organism and provide a foundation for scalable virtual experimentation in neuroscience.