Three-dimensional classification of insect neurons using self-organizing maps

  • Authors:

  • Hiroki Urata;Teijiro Isokawa;Yoich Seki;Naotake Kamiura;Nobuyuki Matsui;Hidetoshi Ikeno;Ryohei Kanzaki

  • Affiliations:

  • Graduate School of Engineering, University of Hyogo, Himeji, Hyogo, Japan;Graduate School of Engineering, University of Hyogo, Himeji, Hyogo, Japan;Graduate School of Information Science and Technology, The University of Tokyo;Graduate School of Engineering, University of Hyogo, Himeji, Hyogo, Japan;Graduate School of Engineering, University of Hyogo, Himeji, Hyogo, Japan;School of Human Science and Environment, University of Hyogo;Graduate School of Information Science and Technology, The University of Tokyo

  • Venue:
  • KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part III
  • Year:
  • 2007

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Abstract

In this paper, systematic three-dimensional classification is presented for sets of interneuron slice images of silkworm moths, using self-organizing maps. Fractal dimension values are calculated for target sets to quantify denseness of their branching structures, and are employed as element values in training data for constructing a map. The other element values are calculated from the sets to which labeling and erosion are applied, and they quantifies whether the sets include thick main dendrites. The classification result is obtained as clusters with units in the map. The proposed classification employing only two elements in training data achieves as high accuracy as the manual classification made by neuroscientists.