Differential geometry to link neural population activity with flexible behavior and generalization

Abstract: Humans and other animals exhibit a remarkable capacity for cognitive flexibility and generalization, yet the neurobiological mechanisms underlying these intelligent behaviors remain poorly understood. While traditional neuroscience often focuses on the firing properties of individual neurons, this project proposes that intelligence emerges from the specific geometric structure of neural population activity across the brain. By utilizing artificial neural networks to create accurate encoding models and applying tools from differential geometry to infer the shape of these “representational manifolds,” the researchers aim to map how neural populations organize information. This framework will provide a new, quantitative approach to understanding how the brain’s geometric representation of the world supports flexible, context-dependent behavior.