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Concepts for Neural Networks pp 109–146Cite as

A Connectionist Approach to Spatial Memory and Planning

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Part of the book series: Perspectives in Neural Computing ((PERSPECT.NEURAL))

Abstract

This chapter describes the design and testing of a biologically inspired vision-based model of spatial memory. Three theories of biological spatial memory are discussed. The Topological Network-map theory is translated into general principles, and two forms of connectionist implementation of these principles are discussed. This is followed by a discussion of planning and map-learning experiments performed with a robot. These experiments reveal problems with the implementation of the view-graph principle. The causes of these problems are discussed and solutions proposed.

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  1. Centre for Neural and Adaptive Systems, School of Computing, University of Plymouth, UK

    G. Bugmann

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  1. Department of Mathematics, Kings College, Strand, London, WC2R 2LS, UK

    L. J. Landau BSc, MA, PhD

  2. Centre for Neural Networks, Department of Mathematics, Kings College, Strand, London, WC2R 2LS, UK

    J. G. Taylor BA, BSc, MA, PhD, FlnstP

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Bugmann, G. (1998). A Connectionist Approach to Spatial Memory and Planning. In: Landau, L.J., Taylor, J.G. (eds) Concepts for Neural Networks. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-3427-5_5

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