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Simulating Path Integration with Continuous Attractor Neural Networks (Short Paper)

Authors Yulin Wu , Ed Manley

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LIPIcs.COSIT.2026.34.pdf
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Subject Classification

ACM Subject Classification
  • Computing methodologies → Spatial and physical reasoning
  • Computing methodologies → Bio-inspired approaches
Keywords
  • cognitive map
  • grid cell
  • continuous attractor neural network
  • path integration
  • spatial cognition
  • urban mobility

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Abstract

This paper explores how grid cell-inspired neural dynamics can simulate path integration during human navigation. We combine smartphone GPS traces, trajectory interpolation, and a two-dimensional continuous attractor neural network (2D CANN) to model path integration in York and Leeds, United Kingdom. The model reproduces grid cell-like firing and estimates trajectories that closely match mapped movement. We further introduce a logarithmic psychophysical transformation of velocity to examine how perceived speed may distort cognitive spatial structure, especially under mixed transportation modes. The results suggest that velocity-driven path integration can computationally account for asymmetric distance estimation and cognitive collage-like distortions in urban environments.

Cite As Get BibTex

Yulin Wu and Ed Manley. Simulating Path Integration with Continuous Attractor Neural Networks (Short Paper). In 17th International Conference on Spatial Information Theory (COSIT 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 393, pp. 34:1-34:8, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026) https://doi.org/10.4230/LIPIcs.COSIT.2026.34

Author Details

Yulin Wu
  • Shenzhen Graduate School, Peking University, Guangdong, China
  • University of Leeds, UK
Ed Manley
  • University of Leeds, UK

References

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