Learning shapes neural geometry in the prefrontal cortex

The relationship between the geometry of neural representations and the task being performed is a central question in neuroscience^1–6. The primate prefrontal cortex (PFC) is a primary focus of inquiry in this regard, as under different conditions, PFC can encode information with geometries that either rely on past experience^7–13or are experience agnostic^3,14–16. One hypothesis is that PFC representations should evolve with learning^4,17,18, from a format that supports exploration of all possible task rules to a format that minimises metabolic cost^4,17,18and supports generalisation^7,8. Here we test this idea by recording neural activity from PFC when learning a new rule (‘XOR rule’) from scratch. We show that PFC representations progress from being high dimensional and randomly mixed to low dimensional and rule selective, consistent with predictions from metabolically constrained optimised neural networks. We also find that this low-dimensional representation facilitates generalisation of the XOR rule to a new stimulus set. These results show that previously conflicting accounts of PFC representations can be reconciled by considering the adaptation of these representations across learning in the service of metabolic efficiency and generalisation.