Evolving neural networks for orientation behavior of sand scorpions towards prey

Hyungu Yim, Daeeun Kim

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

Abstract

Sand scorpions have a good capability of detecting vibration caused by their prey. They have tactile sense organs in their legs, and they are sensitive to the vibration of surface wave. It is known that the receptor neurons (command neurons) from each leg have inhibitory connections to pinpoint the direction of vibration source, and triad inhibitory connections among receptor neurons have been suggested to explain their orientation behavior. In this paper, we explore the neural network mechanism for the orientation behavior of sand scorpions towards their prey, and by evolving neural networks, we found inhibitory connections among receptor neurons play a significant role for the behavior.

Original languageEnglish
Title of host publicationArtificial Neural Networks and Machine Learning, ICANN 2012 - 22nd International Conference on Artificial Neural Networks, Proceedings
Pages347-354
Number of pages8
EditionPART 1
DOIs
Publication statusPublished - 2012 Oct 25
Event22nd International Conference on Artificial Neural Networks, ICANN 2012 - Lausanne, Switzerland
Duration: 2012 Sep 112012 Sep 14

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume7552 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other22nd International Conference on Artificial Neural Networks, ICANN 2012
CountrySwitzerland
CityLausanne
Period12/9/1112/9/14

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All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Yim, H., & Kim, D. (2012). Evolving neural networks for orientation behavior of sand scorpions towards prey. In Artificial Neural Networks and Machine Learning, ICANN 2012 - 22nd International Conference on Artificial Neural Networks, Proceedings (PART 1 ed., pp. 347-354). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7552 LNCS, No. PART 1). https://doi.org/10.1007/978-3-642-33269-2_44