Abstract
The medial entorhinal cortex is part of a neural system for mapping the position of an individual within a physical environment1. Grid cells, a key component of this system, fire in a characteristic hexagonal pattern of locations2, and are organized in modules3 that collectively form a population code for the animal’s allocentric position1. The invariance of the correlation structure of this population code across environments4,5 and behavioural states6,7, independent of specific sensory inputs, has pointed to intrinsic, recurrently connected continuous attractor networks (CANs) as a possible substrate of the grid pattern1,8–11. However, whether grid cell networks show continuous attractor dynamics, and how they interface with inputs from the environment, has remained unclear owing to the small samples of cells obtained so far. Here, using simultaneous recordings from many hundreds of grid cells and subsequent topological data analysis, we show that the joint activity of grid cells from an individual module resides on a toroidal manifold, as expected in a two-dimensional CAN. Positions on the torus correspond to positions of the moving animal in the environment. Individual cells are preferentially active at singular positions on the torus. Their positions are maintained between environments and from wakefulness to sleep, as predicted by CAN models for grid cells but not by alternative feedforward models12. This demonstration of network dynamics on a toroidal manifold provides a population-level visualization of CAN dynamics in grid cells.
| Original language | American English |
|---|---|
| Pages (from-to) | 123-128 |
| Number of pages | 6 |
| Journal | Nature |
| Volume | 602 |
| Issue number | 7895 |
| DOIs | |
| State | Published - 3 Feb 2022 |
Bibliographical note
Funding Information:Acknowledgements We thank M. P. Witter for help with evaluation of recording locations, and A. M. Amundsgård, K. Haugen, K. Jenssen, E. Kråkvik, I. Ulsaker-Janke and H. Waade for technical assistance. The work was supported by a Synergy Grant to E.I.M. and Y.B. from the European Research Council (‘KILONEURONS’, grant agreement no. 951319); an RCN FRIPRO grant to E.I.M. (grant no. 286225); a Centre of Excellence grant to M.-B.M. and E.I.M. and a National Infrastructure grant to E.I.M. and M.-B.M. from the Research Council of Norway (Centre of Neural Computation, grant number 223262; NORBRAIN, grant number 295721); the Kavli Foundation (M.-B.M. and E.I.M.); the Department of Mathematical Sciences at the Norwegian University of Science and Technology (B.A.D., E.H. and N.A.B.); a direct contribution to M.-B.M. and E.I.M. from the Ministry of Education and Research of Norway; and grants to Y.B. from the Israel Science Foundation (grant no. 1745/18) and the Gatsby Charitable Foundation. Some of the computations were performed on resources provided by the NTNU IDUN/EPIC computing cluster.
Funding Information:
We thank M. P. Witter for help with evaluation of recording locations, and A. M. Amundsgård, K. Haugen, K. Jenssen, E. Kråkvik, I. Ulsaker-Janke and H. Waade for technical assistance. The work was supported by a Synergy Grant to E.I.M. and Y.B. from the European Research Council (‘KILONEURONS’, grant agreement no. 951319); an RCN FRIPRO grant to E.I.M. (grant no. 286225); a Centre of Excellence grant to M.-B.M. and E.I.M. and a National Infrastructure grant to E.I.M. and M.-B.M. from the Research Council of Norway (Centre of Neural Computation, grant number 223262; NORBRAIN, grant number 295721); the Kavli Foundation (M.-B.M. and E.I.M.); the Department of Mathematical Sciences at the Norwegian University of Science and Technology (B.A.D., E.H. and N.A.B.); a direct contribution to M.-B.M. and E.I.M. from the Ministry of Education and Research of Norway; and grants to Y.B. from the Israel Science Foundation (grant no. 1745/18) and the Gatsby Charitable Foundation. Some of the computations were performed on resources provided by the NTNU IDUN/EPIC computing cluster.
Publisher Copyright:
© 2022, The Author(s).