A predictive map learned from diverse entorhinal inputs explains the role of context-dependent reorganization of...

The hippocampus is thought to support spatial memory and navigation by constructing predictive representations of the environment. Predictive map theory formalizes this function as a successor representation (SR). However, existing models assume a fixed and uniform distribution of place fields, despite experimental findings that place cell density is dynamically modulated by rewards and objects. Here, we propose a biologically inspired neural model in which predictive maps emerge from diverse entorhinal inputs. In the model, place cell-like representations are generated via non-negative sparse coding of medial entorhinal spatial signals and lateral entorhinal contextual and motivational signals, and are subsequently transformed into predictive maps using successor features. By coupling the predictive map to an actor-critic framework, the model supports goal-directed navigation in continuous environments. Furthermore, the model reproduces experience-dependent restructuring of hippocampal representations, including object-centered overrepresentation of place fields in two-dimensional environments and reward-centered overrepresentation in one-dimensional environments. Together, these results demonstrate that hippocampal predictive maps can emerge from the integration of diverse entorhinal inputs, providing a unified account of how spatial, contextual, and motivational information jointly shape hippocampal representations and behavior.