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  View-Based Cognitive Mapping and Path Planning

Schölkopf, B., & Mallot, H. (1995). View-Based Cognitive Mapping and Path Planning. Adaptive Behavior, 3(3), 311-348. doi:10.1177/105971239500300303.

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Schölkopf, B1, 2, Author              
Mallot, HA1, 2, Author              
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1Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497797              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: This article presents a scheme for learning a cognitive map of a maze from a sequence of views and movement decisions. The scheme is based on an intermediate representation called the view graph, whose nodes correspond to the views whereas the labeled edges represent the movements leading from one view to another. By means of a graph theoretical reconstruction method, the view graph is shown to carry complete information on the topological and directional structure of the maze. Path planning can be carried out directly in the view graph without actually performing this reconstruction. A neural network is presented that learns the view graph during a random exploration of the maze. It is based on an unsupervised competitive learning rule translating temporal sequence (rather than similarity) of views into connectedness in the network. The network uses its knowledge of the topological and directional structure of the maze to generate expectations about which views are likely to be encountered next, improving the view-recognition performance. Numerical simulations illustrate the network's ability for path planning and the recognition of views degraded by random noise. The results are compared to findings of behavioral neuroscience.

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 Dates: 1995-01
 Publication Status: Published in print
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 Identifiers: DOI: 10.1177/105971239500300303
BibTex Citekey: 947
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Title: Adaptive Behavior
Source Genre: Journal
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Publ. Info: Cambridge, MA : Sage Publications, Inc.
Pages: - Volume / Issue: 3 (3) Sequence Number: - Start / End Page: 311 - 348 Identifier: ISSN: 1059-7123
CoNE: https://pure.mpg.de/cone/journals/resource/954925597585