Behaving animals continually reconcile the internal states of their neural circuits brain-wide with incoming sensory and environmental evidence to evaluate when and how to act. The brains of animals including humans exploit many evolutionary innovations, chiefly modularity—observable at the level of anatomically-defined brain regions, cortical layers, and cell types among others—that can be repurposed in a compositional manner to endow the animal with a highly flexible behavioral repertoire. Accordingly, behavioral output shows its own modularity, yet these behavioral modules seldom correspond directly to modularity in the brain. It remains unclear how to link neural and behavioral modularity compositionally. Here, we propose a comprehensive framework—compositional modes—to identify overarching compositional structure across specialized submodules such as brain regions. Our framework directly links the behavioral repertoire with distributed patterns of population activity brain-wide at multiple concurrent spatial and temporal scales. Using data from whole-brain recordings of larval zebrafish, we introduce an unsupervised pipeline based on neural network models to reveal highly conserved compositional modes across individuals despite the spontaneous nature of the behavior. These modes provided scaffolding for other modes that account for the idiosyncratic behavior of each fish. We then demonstrate experimentally that behavioral and pharmacological manipulation can consistently manipulate compositional modes. Our results demonstrate that even spontaneous behavior in different individuals can be decomposed and understood using a relatively small number of neurobehavioral modules—the compositional modes—and elucidate a compositional neural basis of behavior.

Learning Objectives: 

1. Explain the concept of modularity in the brain and its significance for understanding behavioral flexibility.

2. Describe the framework of compositional modes to link brain-wide neural activity with behavior.

3. Demonstrate how data-constrained modeling can reveal the neural basis of behavior in models like the larval zebrafish.