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  Dual Population Coding for Path Planning in Graphs with Overlapping Place Representations

Mallot, H., Ecke, G., & Baumann, T. (2020). Dual Population Coding for Path Planning in Graphs with Overlapping Place Representations. In J. Šķilters, N. Newcombe, & D. Uttal (Eds.), Spatial Cognition XII: 12th International Conference, Spatial Cognition 2020, Riga, Latvia, August 26–28, 2020 (pp. 3-17). Cham, Switzerland: Springer.

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 Creators:
Mallot, HA, Author                 
Ecke, GA, Author
Baumann, T1, Author                 
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1External Organizations, ou_persistent22              

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 Abstract: Topological schemes for navigation from visual snapshots have been based on graphs of panoramic images and action links allowing the transition from one snapshot point to the next; see, for example, Cartwright and Collett [5] or Franz et al. [9]. These algorithms can only work if at each step a unique snapshot is recognized to which a motion decision is associated. Here, we present a population coding approach in which place is encoded by a population of overlapping “firing fields”, each of which is activated by the recognition of an unspecific “micro-snapshot” (i.e. feature), and associated to a subsequent action. Agent motion is then computed by a voting scheme over all activated snapshot-to-action associations. The algorithm was tested in a large virtual environment (Virtual Tübingen [24]) and shows biologically plausible navigational abilities.

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 Dates: 2020-08
 Publication Status: Issued
 Pages: -
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 Rev. Type: -
 Identifiers: DOI: 10.1007/978-3-030-57983-8_1
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Title: 12th International Conference on Spatial Cognition
Place of Event: Riga, Latvia
Start-/End Date: 2020-08-26 - 2020-08-28

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Title: Spatial Cognition XII: 12th International Conference, Spatial Cognition 2020, Riga, Latvia, August 26–28, 2020
Source Genre: Proceedings
 Creator(s):
Šķilters , J, Editor
Newcombe, NS, Editor
Uttal, D, Editor
Affiliations:
-
Publ. Info: Cham, Switzerland : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 3 - 17 Identifier: ISBN: 978-3-030-57982-1
DOI: 10.1007/978-3-030-57983-8