# Rapid Learning of Spatial Representations for Goal-Directed Navigation Based on a Novel Model of Hippocampal Place Fields

arXiv: https://arxiv.org/abs/2206.02249 · HTML: https://mlsanity.com/paper/2206.02249/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2022.

## Built on by (most cited first, 3 of 3)

- [Improved Accuracy of Robot Localization Using 3-D LiDAR in a Hippocampus-Inspired Model](https://mlsanity.com/paper/2510.24029/) (2025)
- [A Reinforcement Learning-Based Model for Mapping and Goal-Directed Navigation Using Multiscale Place Fields](https://mlsanity.com/paper/2601.03520/) (2026)
- [The Role of Grid Cells in Reducing Spatial Aliasing in Hippocampal Place Representations](https://mlsanity.com/paper/2608.18569/) (2026)

## Abstract (arXiv)

The discovery of place cells and other spatially modulated neurons in the hippocampal complex of rodents has been crucial to elucidating the neural basis of spatial cognition. More recently, the replay of neural sequences encoding previously experienced trajectories has been observed during consummatory behavior potentially with implications for quick memory consolidation and behavioral planning. Several promising models for robotic navigation and reinforcement learning have been proposed based on these and previous findings. Most of these models, however, use carefully engineered neural networks and are tested in simple environments. In this paper, we develop a self-organized model incorporating place cells and replay, and demonstrate its utility for rapid one-shot learning in non-trivial environments with obstacles.