Neural Network Modeling and Analysis of Turn Duration Time Changing of Silkmoth Using Genetic Algorithm

Author(s): Ryosuke Chiba, Sunao Hashimoto, Tomoki Kazawa, Ryohei Kanzaki, Jun Ota.

Journal Name: Neuroscience and Biomedical Engineering (Discontinued)

Volume 2 , Issue 2 , 2014

Submit Manuscript
Submit Proposal

Graphical Abstract:


Abstract:

In this study, we investigate the reproductive behavior of the male silkmoth, Bombyx mori. When a male silkmoth senses the sexual pheromones of a female through its antennas, it shows a certain walking pattern by which it approaches the female. Interestingly, the degree of pheromone stimulation influences the turn duration time in this pattern. This walking pattern is considered to be generated in the silkmoth brain, specifically in the lateral accessory lobe (LAL) and the ventral protocerebrum (VPC) domain, which control physical movements However, the system responsible for this behavior remains unknown. In this study, we investigate the generation of this behavior through a neural network model of the LAL and VPC domains. Specifically, we model many neurons in the silkmoth brain using one artificial neuron and estimate the strength of each connection using a genetic algorithm between 10 neurons that represent neuron groups with a fitness function of turn duration time. The model of the silkmoth brain is verified and evaluated from both engineering and biological viewpoints. The modeling results show that only 6 LAL- VPC regions can make the turn duration time shortening and buffering regions play very important roles in the reproductive behavior. Subsequently, we developed a new hypothesis that a male silkmoth adjusts its walking pattern using the time delay of transition the signals between lateral and bi-lateral regions.

Keywords: Genetic algorithm, mobiligence, neural network modeling, silkmoth zig-zag walking.

Rights & PermissionsPrintExport Cite as


Article Details

VOLUME: 2
ISSUE: 2
Year: 2014
Page: [59 - 67]
Pages: 9
DOI: 10.2174/2213385203666150122002353
Price: $58

Article Metrics

PDF: 6