REMI: Reconstructing Episodic Memory During Internally Driven Path Planning

arXiv cs.AI / 3/24/2026

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Key Points

  • The paper proposes a system-level wiring theory connecting medial entorhinal cortex (MEC) grid cells and hippocampal (HC) place cells to support internally driven, cue-triggered path planning and goal retrieval.
  • It argues that place cells can autoassociate sensory inputs with grid-cell patterns, enabling cues to recall goal-location representations and support planning over both visited and unvisited areas.
  • Analytically, the authors show that grid-based planning can produce shortcut routes through unvisited locations and can generalize local transitions to longer-range paths.
  • During route planning, intermediate grid states are posited to trigger place-cell pattern completion, reconstructing predicted sensory experiences along the planned trajectory.
  • The theory is tested using a single-layer RNN modeling an HC–MEC loop with a planning subnetwork, validated in biologically grounded navigation simulations (RatatouGym) and visually realistic navigation tasks (Habitat Sim).

Abstract

Grid cells in the medial entorhinal cortex (MEC) and place cells in the hippocampus (HC) both form spatial representations. Grid cells fire in triangular grid patterns, while place cells fire at specific locations and respond to contextual cues. How do these interacting systems support not only spatial encoding but also internally driven path planning, such as navigating to locations recalled from cues? Here, we propose a system-level theory of MEC-HC wiring that explains how grid and place cell patterns could be connected to enable cue-triggered goal retrieval, path planning, and reconstruction of sensory experience along planned routes. We suggest that place cells autoassociate sensory inputs with grid cell patterns, allowing sensory cues to trigger recall of goal-location grid patterns. We show analytically that grid-based planning permits shortcuts through unvisited locations and generalizes local transitions to long-range paths. During planning, intermediate grid states trigger place cell pattern completion, reconstructing sensory experiences along the route. Using a single-layer RNN modeling the HC-MEC loop with a planning subnetwork, we demonstrate these effects in both biologically grounded navigation simulations using RatatouGym and visually realistic navigation tasks using Habitat Sim.