A neural model of landmark navigation in the fiddler crab Uca lactea

Hyunggi Cho, Daeeun Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

The fiddler crabs, Uca lactea, which live on intertidal mudflats, exhibit a remarkable ability to return to its burrow. It has been reported that the species usually use path integration, an ideothetic mechanism for short-range homing. During the mating season, however, the accumulation error of the process increases due to vigorous courtship movement. To compensate for this, most courting males construct the vertical mud structures, called semidomes, at the entrance of their burrows and use them as landmarks. Here, we suggest a possible neural model that demonstrates how visual landmark navigation could be implemented in the fiddler crab's central nervous system. The model consisting of two levels of population of neurons, is based on the snapshot hypothesis and a simplified version of Franz's algorithm is used for the computation of home vector.

Original languageEnglish
Title of host publicationESANN 2009 Proceedings, 17th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning
Pages355-360
Number of pages6
Publication statusPublished - 2009
Event17th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning, ESANN 2009 - Bruges, Belgium
Duration: 2009 Apr 222009 Apr 24

Publication series

NameESANN 2009 Proceedings, 17th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning

Other

Other17th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning, ESANN 2009
CountryBelgium
CityBruges
Period09/4/2209/4/24

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Information Systems

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    Cho, H., & Kim, D. (2009). A neural model of landmark navigation in the fiddler crab Uca lactea. In ESANN 2009 Proceedings, 17th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning (pp. 355-360). (ESANN 2009 Proceedings, 17th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning).