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

Abstract

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. By applying NEBULA to brain-wide recordings from C. elegans, we identify a shared low-dimensional dynamical manifold that underlies both neural activity and behavior, enabling long-horizon generation and targeted in silico 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.