REMI: Reconstructing Episodic Memory During Internally Driven Path Planning

2025-07-02 · Zhaoze Wang, Genela Morris, Dori Derdikman, Pratik Chaudhari, Vijay Balasubramanian arxiv

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.

📄 PDF Abstract BibTeX arXiv:2507.02064

Code (0)

등록된 구현이 없습니다.

Neural correlates of episodic memory in the Memento cohort

2018-12-19

IntroductionThe free and cued selective reminding test is used to identify memory deficits in mild cognitive impairment and demented patients. It allows assessing three processes: encoding, storage, and recollection of v…

Retrieval

Continual and Multi-task Reinforcement Learning With Shared Episodic Memory

2019-05-07 · Artyom Y. Sorokin, Mikhail S. Burtsev

Episodic memory plays an important role in the behavior of animals and humans. It allows the accumulation of information about current state of the environment in a task-agnostic way. This episodic representation can be …

Continual Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Two-Memory Reinforcement Learning

2023-04-20 · Zhao Yang, Thomas. M. Moerland, Mike Preuss, Aske Plaat

While deep reinforcement learning has shown important empirical success, it tends to learn relatively slow due to slow propagation of rewards information and slow update of parametric neural networks. Non-parametric epis…

Deep Reinforcement Learning reinforcement-learning Reinforcement Learning Representation Learning+1

Episodic Memory Deep Q-Networks

2018-05-19 · Zichuan Lin, Tianqi Zhao, Guangwen Yang, Lintao Zhang

Reinforcement learning (RL) algorithms have made huge progress in recent years by leveraging the power of deep neural networks (DNN). Despite the success, deep RL algorithms are known to be sample inefficient, often requ…

Atari Games Reinforcement Learning Reinforcement Learning (RL)

Saliency Guided Experience Packing for Replay in Continual Learning

2021-09-10 · Gobinda Saha, Kaushik Roy

Artificial learning systems aspire to mimic human intelligence by continually learning from a stream of tasks without forgetting past knowledge. One way to enable such learning is to store past experiences in the form of…

Continual Learning Image Classification