Reconciling Flexibility and Efficiency: Medial Entorhinal Cortex Represents a Compositional Cognitive Map

The influential concept of a cognitive map envisions that the brain builds mental representations of objects, barriers, and goals. This idea has been formalized in a range of computational models that show how such representations can be useful for guiding goal-directed behavior, for instance by planning novel routes to maximize long-run rewards. One key feature of flexible cognitive representations generally is that they exploit compositionality – the ability to build complex structures by recombining simpler parts. However, how this principle plays out in neural representations of cognitive maps and map-based planning remains largely unexplored. Indeed, as we show here, compositionality can be difficult to reconcile with efficient planning: because reuse tends to oppose flexibility, it is challenging to construct a compositional representation of the environment which is also organized in a way that enables generalizability and efficient planning. Here, we propose a novel model for efficiently creating and planning with compositional predictive maps, and further show that it successfully simulates various aspects of response fields in the medial entorhinal cortex, particularly object vector cells and grid cells. The model treats each object as an alteration to a baseline map linked to open space, creating complete predictive maps by combining object-related representations compositionally. Overall, this work provides a comprehensive and realistic model for efficient model learning and model-based planning in animals, and offers insights into the brain processes supporting efficient, flexible planning using compositional predictive maps.