The way grid cells represent space in the rodent brain has been a striking discovery, with theoretical implications still unclear. Di↵erently from hippocampal place cells, which are known to encode multiple, environment-dependent spatial maps, grid cells have been widely believed to encode space through a single low dimensional manifold, in which coactivity relations between di↵erent neurons are preserved when the environment is changed. Does it have to be so? Here, we compute – using two alternative mathematical models – the storage capacity of a population of grid-like units, embedded in a continuous attractor neural network, for multiple spatial maps. We show that distinct representations of multiple environments can coexist, as existing models for grid cells have the potential to express several sets of hexagonal grid patterns, challenging the view of a universal grid map. This suggests that a population of grid cells can encode multiple non-congruent metric relationships, a feature that could in principle allow a grid-like code to represent environments with a variety of di↵erent geometries and possibly conceptual and cognitive spaces, which may be expected to entail such context-dependent metric relationships.
