Biological agents continually reconcile the internal states of their brain circuits with incoming sensory and environmental evidence to evaluate when and how to act. The brains of biological agents, including animals and 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, their behaviors show their own modularity, yet such behavioral modules seldom correspond directly to traditional notions of modularity in brains. It remains unclear how to link neural and behavioral modularity in a compositional manner. We propose a comprehensive framework—compositional modes—to identify overarching compositionality spanning 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 whole-brain recordings of zebrafish brains, we introduce an unsupervised pipeline based on neural network models, constrained by biological data, to reveal highly conserved compositional modes across individuals despite the naturalistic (spontaneous or task-independent) nature of their behaviors. These modes provided a scaffolding for other modes that account for the idiosyncratic behavior of each fish. We then demonstrate experimentally that compositional modes can be manipulated in a consistent manner by behavioral and pharmacological perturbations. Our results demonstrate that even natural 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. This approach aligns with recent progress in understanding how reasoning capabilities and internal representational structures develop over the course of learning or training, offering insights into the modularity and flexibility in artificial and biological agents.

Bio: Kanaka Rajan, PhD, is a computational neuroscientist, Associate Professor of Neurobiology at Harvard Medical School, and a founding faculty member of the Kempner Institute for the Study of Natural and Artificial Intelligence at Harvard University. Her research seeks to understand how important cognitive functions — such as learning, remembering, and deciding — emerge from the cooperative activity of multi-scale neural processes. Using data from neuroscience experiments, Dr. Rajan applies computational frameworks derived from machine learning and statistical physics to uncover integrative theories about the brain that bridge neurobiology and artificial intelligence.

Dr. Rajan’s work has been recognized with several awards (CIFAR Azrieli Global Scholars Program, Allen Institute’s Next Generation Leaders Council, The Harold and Golden Lamport Basic Science Research Award, McKnight Scholars Award, Young Investigator Award from the Brain and Behavior Foundation, Understanding Human Cognition Scholar Award from the James S McDonnell Foundation, Sloan Research Fellowship), and her work is supported by the NIH BRAIN Initiative and the NSF. For more information about Dr. Rajan and her lab, please visit www.rajanlab.com