Map-Based Spatial Navigation: A Cortical Column Model for Action Planning

Authors:
Louis-Emmanuel Martinet;Jean-Baptiste Passot;Benjamin Fouque;Jean-Arcady Meyer;Angelo Arleo
Affiliations:
UPMC Univ Paris 6, FRE2507, ISIR, Paris, France F-75016 and UPMC Univ Paris 6, UMR 7102, Paris, France F-75005 and CNRS, UMR 7102, Paris, France F-75005;UPMC Univ Paris 6, UMR 7102, Paris, France F-75005 and CNRS, UMR 7102, Paris, France F-75005;UPMC Univ Paris 6, FRE2507, ISIR, Paris, France F-75016 and UPMC Univ Paris 6, UMR 7102, Paris, France F-75005 and CNRS, UMR 7102, Paris, France F-75005;UPMC Univ Paris 6, FRE2507, ISIR, Paris, France F-75016;UPMC Univ Paris 6, UMR 7102, Paris, France F-75005 and CNRS, UMR 7102, Paris, France F-75005
Venue:
Proceedings of the international conference on Spatial Cognition VI: Learning, Reasoning, and Talking about Space
Year:
2008

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Abstract

We modelled the cortical columnar organisation to design a neuromimetic architecture for topological spatial learning and action planning. Here, we first introduce the biological constraints and the hypotheses upon which our model was based. Then, we describe the learning architecture, and we provide a series of numerical simulation results. The system was validated on a classical spatial learning task, the Tolman & Honzik's detourprotocol, which enabled us to assess the ability of the model to build topological representations suitable for spatial planning, and to use them to perform flexible goal-directed behaviour (e.g., to predict the outcome of alternative trajectories avoiding dynamically blocked pathways). We show that the model reproduced the navigation performance of rodents in terms of goal-directed path selection. In addition, we present a series of statistical and information theoretic analyses to study the neural coding properties of the learnt space representations.