School of Human Science and Environment, University of Hyogo, 1-1-12 Shinzaike-Honcho, Himeji, Hyogo 670-0092, Japan.
Comput Intell Neurosci. 2012;2012:795291. doi: 10.1155/2012/795291. Epub 2012 Aug 16.
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.
理解感知环境信息和控制自然环境中行为的神经机制是神经科学的主要目标之一。实现这一目标的一种方法是通过模拟重建神经系统。尽管与哺乳动物相比，昆虫的大脑相对简单，但它们能够处理各种感觉信号并产生适应性行为。然而，由于实验限制，我们在网络系统水平上的整体理解是有限的。当整合实验数据和假设时，模拟对于研究神经机制非常有效。然而，由于神经元的数量和复杂性，构建整个大脑水平的计算模型仍然非常困难。我们专注于蚕的一种独特行为，以研究感觉处理和行为控制的神经机制。标准大脑用于通过整合来巩固实验结果并产生新的见解。在这项研究中，我们构建了一个蚕的标准大脑和大脑图像，其中我们注册了分割的神经突区域和神经元。我们用于从共聚焦图像中分割神经元的原始软件工具 KNEWRiTE 和分割数据的注册模块 NeuroRegister，在计算神经科学研究中的神经元注册中非常有效。
