# Visual Place Cell Encoding: A Computational Model for Spatial Representation and Cognitive Mapping

arXiv: https://arxiv.org/abs/2504.15953 · HTML: https://mlsanity.com/paper/2504.15953/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 43 references that are in the map.
- First posted on arXiv 22 Apr 2025.

## Builds on (most cited first, 3 of 3)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Coordinated Robot Navigation via Hierarchical Clustering](https://mlsanity.com/paper/1507.01637/) (2015)
- [Incremental Semantic Localization using Hierarchical Clustering of Object Association Sets](https://mlsanity.com/paper/2208.13210/) (2022)

## Abstract (arXiv)

This paper presents the Visual Place Cell Encoding (VPCE) model, a biologically inspired computational framework for simulating place cell-like activation using visual input. Drawing on evidence that visual landmarks play a central role in spatial encoding, the proposed VPCE model activates visual place cells by clustering high-dimensional appearance features extracted from images captured by a robot-mounted camera. Each cluster center defines a receptive field, and activation is computed based on visual similarity using a radial basis function. We evaluate whether the resulting activation patterns correlate with key properties of biological place cells, including spatial proximity, orientation alignment, and boundary differentiation. Experiments demonstrate that the VPCE can distinguish between visually similar yet spatially distinct locations and adapt to environment changes such as the insertion or removal of walls. These results suggest that structured visual input, even in the absence of motion cues or reward-driven learning, is sufficient to generate place-cell-like spatial representations and support biologically inspired cognitive mapping.