Efficient and flexible representation of higher-dimensional cognitive variables with grid cells.

ABSTRACT: We shed light on the potential of entorhinal grid cells to efficiently encode variables of dimension greater than two, while remaining faithful to empirical data on their low-dimensional structure. Our model constructs representations of high-dimensional inputs through a combination of low-dimensional random projections and "classical" low-dimensional hexagonal grid cell responses. Without reconfiguration of the recurrent circuit, the same system can flexibly encode multiple variables of different dimensions while maximizing the coding range (per dimension) by automatically trading-off dimension with an exponentially large coding range. It achieves high efficiency and flexibility by combining two powerful concepts, modularity and mixed selectivity, in what we call "mixed modular coding". I

SUBMITTER: Klukas M 

PROVIDER: S-EPMC7209352 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature