Slow synaptic plasticity from the hippocampus underlies gradual mapping and fragmentation of novel spaces by grid cells.

Animals construct internal "cognitive maps" of the world during navigation in spatial and non-spatial domains, with grid cells in the medial entorhinal cortex (MEC) playing a key role. This requires associating internal position estimates with external cues to reduce spatial uncertainty over time. However, how grid cell representations evolve in novel spaces to support map formation is unclear. To address this question, we longitudinally record grid cells with two-photon calcium imaging over 10 days as mice learn operant tasks in novel virtual linear tracks. We observe that spatial tuning of grid cells is present immediately in novel tracks but evolves as a significant fraction of spatial fields shift backward on a run-by-run basis, within and across days. Backward shifts are more prevalent and persistent in successful learners. The fields gradually stabilize across days, anchored by landmarks, suggesting a slow plasticity mechanism that results in an increasingly fragmented and stable map. The backward shifts partially reset daily, reflecting a slower consolidation timescale. We show that though individual fields of a cell shift differentially, co-active fields of co-modular grid cells shift together, indicating their coupled dynamics keep them on the same two-dimensional torus during this plastic period. Next, we build an entorhinal-hippocampal model that provides a mechanistic explanation of the diverse phenomena - grid field shifts, fragmentation, and increasing fidelity of the spatial map - and predicts slow Hebbian plasticity in the return hippocampus-to-entorhinal pathway. Finally, using slice electrophysiology, we show that plasticity in an indirect hippocampus-to-MEC pathway correlates with spatial learning performance and could account for the hypothesized slow plasticity of the model. Together, our study provides multifaceted evidence of slow plasticity in synapses from the hippocampus to the MEC, elucidating the formation of stable and fragmented maps that combine internal and cue-driven positional estimates in rich environments, elucidating cognitive map formation during spatial learning.