Geometry of abstract learned knowledge in the hippocampus

Hippocampal neurons encode physical variables(1-7) such as space(1) or auditory frequency(6) in cognitive maps(8). In addition, functional magnetic resonance imaging studies in humans have shown that the hippocampus can also encode more abstract, learned variables(9-11). However, their integration into existing neural representations of physical variables(12,13) is unknown. Here, using two-photon calcium imaging, we show that individual neurons in the dorsal hippocampus jointly encode accumulated evidence with spatial position in mice performing a decision-making task in virtual reality(14-16). Nonlinear dimensionality reduction(13) showed that population activity was well-described by approximately four to six latent variables, which suggests that neural activity is constrained to a low-dimensional manifold. Within this low-dimensional space, both physical and abstract variables were jointly mapped in an orderly manner, creating a geometric representation that we show is similar across mice. The existence of conjoined cognitive maps suggests that the hippocampus performs a general computation-the creation of task-specific low-dimensional manifolds that contain a geometric representation of learned knowledge.

Automated summary

Hippocampal neurons encode both physical and abstract variables, and research using two-photon calcium imaging showed that individual neurons in the dorsal hippocampus jointly encode accumulated evidence with spatial position in mice performing a decision-making task. The findings suggest that neural activity in the hippocampus is constrained to a low-dimensional manifold where physical and abstract variables are jointly mapped in an orderly manner.