| First Author | Nieh EH | Year | 2021 |
| Journal | Nature | Volume | 595 |
| Issue | 7865 | Pages | 80-84 |
| PubMed ID | 34135512 | Mgi Jnum | J:393553 |
| Mgi Id | MGI:7287765 | Doi | 10.1038/s41586-021-03652-7 |
| Citation | Nieh EH, et al. (2021) Geometry of abstract learned knowledge in the hippocampus. Nature 595(7865):80-84 |
| abstractText | 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. |
