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Computational Intelligence and Neuroscience
Volume 2012 (2012), Article ID 795291, 10 pages
http://dx.doi.org/10.1155/2012/795291
Research Article

Development of a Scheme and Tools to Construct a Standard Moth Brain for Neural Network Simulations

1School of Human Science and Environment, University of Hyogo, 1-1-12 Shinzaike-Honcho, Himeji, Hyogo 670-0092, Japan
2Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo 153-8904, Japan
3College of Information Science and Engineering, Ritsumeikan University, Kusatsu, Shiga 525-8577, Japan

Received 27 April 2012; Revised 5 July 2012; Accepted 10 July 2012

Academic Editor: Shinichi Tamura

Copyright © 2012 Hidetoshi Ikeno et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Understanding the neural mechanisms for sensing environmental information and controlling behavior in natural environments is a principal aim in neuroscience. One approach towards this goal is rebuilding neural systems by simulation. Despite their relatively simple brains compared with those of mammals, insects are capable of processing various sensory signals and generating adaptive behavior. Nevertheless, our global understanding at network system level is limited by experimental constraints. Simulations are very effective for investigating neural mechanisms when integrating both experimental data and hypotheses. However, it is still very difficult to construct a computational model at the whole brain level owing to the enormous number and complexity of the neurons. We focus on a unique behavior of the silkmoth to investigate neural mechanisms of sensory processing and behavioral control. Standard brains are used to consolidate experimental results and generate new insights through integration. In this study, we constructed a silkmoth standard brain and brain image, in which we registered segmented neuropil regions and neurons. Our original software tools for segmentation of neurons from confocal images, KNEWRiTE, and the registration module for segmented data, NeuroRegister, are shown to be very effective in neuronal registration for computational neuroscience studies.