Name
Title | ||
|---|---|---|
Development of a scheme and tools to construct a standard moth brain for neural network simulations. |
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1155/2012/795291 | Comp. Int. and Neurosc. |
Keywords | Field | DocType |
standard moth brain,adaptive behavior,experimental constraint,simple brain,silkmoth standard brain,brain image,neural mechanism,experimental data,neural network simulation,controlling behavior,neural system,computer simulation | Computational neuroscience,Pattern recognition,Computer science,Segmentation,Software,Artificial intelligence,Neuropil,Sensory system,Artificial neural network,Adaptive behavior,Machine learning,Sensory processing | Journal |
Volume | ISSN | Citations |
2012 | 1687-5273 | 2 |
PageRank | References | Authors |
0.38 | 4 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hidetoshi Ikeno | 1 | 40 | 11.24 |
| Tomoki Kazawa | 2 | 8 | 2.74 |
| Shigehiro Namiki | 3 | 4 | 0.99 |
| Daisuke Miyamoto | 4 | 3 | 0.72 |
| Yohei Sato | 5 | 2 | 0.38 |
| Stephan Shuichi Haupt | 6 | 2 | 0.38 |
| Ikuko Nishikawa | 7 | 40 | 17.68 |
| Ryohei Kanzaki | 8 | 51 | 18.59 |