2026-09-10T09:45:04Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:27578 2026-09-10T05:38:43Z ddc:004 open_access

Simulating Path Integration with Continuous Attractor Neural Networks (Short Paper) Wu, Yulin Manley, Ed cognitive map grid cell continuous attractor neural network path integration spatial cognition urban mobility 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. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Yulin Wu and Ed Manley 2026 Is Part Of LIPIcs, Volume 393, 17th International Conference on Spatial Information Theory (COSIT 2026) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.COSIT.2026.34 urn:nbn:de:0030-drops-275784 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.COSIT.2026.34 eng https://creativecommons.org/licenses/by/4.0/legalcode