WHole Animal Modelling (WHAM): Toward the integrated understanding of sensory motor control in C. elegans

整体动物建模（WHAM）：全面理解秀丽隐杆线虫的感觉运动控制

基本信息

批准号：
EP/J004057/1
负责人：
Netta Cohen
金额：
$ 151.12万
依托单位：
University of Leeds
依托单位国家：
英国
项目类别：
Fellowship
财政年份：
2011
资助国家：
英国
起止时间：
2011 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FJ004057%2F1
关键词：
WHole Animal Modelling WHAM Toward

项目摘要

Animals are remarkable creatures. No man-made machine even comes close in its ability to navigate complex environments, respond to rich sensory cues, learn and adapt its behaviour when encountering completely novel scenarios and much much more. But even the simplest animals, ruled by even the simplest nervous systems, can achieve this. Simple animals may not be able to play chess or balance bank statements, but there is much we can learn from them about their robust mechanisms for sensory-integration and motor control which may be of use to us in control engineering, bio-robotics and even in future brain-machine interfaces that are being developed for neuro-prosthetic applications.An excellent starting point for understanding animal behaviour is a tiny, free living 1mm long roundworm, called C. elegans. By comparison to our 100 billion nerve cells, or even a fly's 100 thousand nerve cells, this worm's entire nervous system consists of a mere 302 nerve cells. Unlike our nervous system, the worm's circuitry is hard wired and identical across individuals of the species, making it possible to study rigorously and reproducibly. Due in part to its simplicity, and in part to its ease of manipulation in the lab, this is the only animal for which this entire nervous system has been mapped in exquisite detail (to sub-cellular resolution). But despite its relative simplicity, this worm possesses many of the functions that are attributed to more complex animals, including feeding, mating, complex sensory abilities, memory and learning. It is not surprising, therefore, that the modelling of this worm has captured the imagination of physicists, computer scientists and engineers alike. The integrated modelling of C. elegans has even been proposed as one of the UK's ``Grand Challenges for Computing Research.''In this Fellowship, I will begin to integrate our understanding of C. elegans sensory motor behaviour in a single computational model. The challenge is to bridge the gap between the effectively static neural circuit architecture and the dynamic neural computation it sustains. This fellowship will enable me to deliver a step change, not only in our understanding of an important model organism, but also in advancing the science and engineering of complex systems, whether in the context of reverse engineering real world networks, or in the context of designing them.

动物是杰出的生物。没有人造的机器在遇到完全新颖的场景时，还可以接近驾驶复杂环境，响应丰富的感官提示，学习和适应其行为的能力，等等。但是，即使是由最简单的神经系统统治的最简单的动物也可以实现这一目标。简单的动物可能无法下棋或平衡银行的陈述，但是我们可以从中了解到它们的强大机制用于感觉融合和运动控制，这可能在控制工程，生物机器人技术，甚至在未来的脑机界接口中都可以使用，甚至在未来的Brain-Machine界面中，这些界面是为神经性的应用程序而开发的，用于神经性良好的应用。与我们的1000亿神经细胞，甚至是苍蝇的100,000个神经细胞相比，该蠕虫的整个神经系统仅由302个神经细胞组成。与我们的神经系统不同，蠕虫的电路在物种的个体之间是坚硬的和相同的，因此可以进行严格和可重复的研究。部分原因是它的简单性以及在实验室中易于操作的部分原因，这是唯一为整个神经系统绘制出精美细节（以下细胞分辨率）的动物。但是，尽管它相对简单，但这种蠕虫具有许多归因于更复杂动物的功能，包括喂养，交配，复杂的感官能力，记忆和学习。因此，毫不奇怪，这种蠕虫的建模捕捉了物理学家，计算机科学家和工程师的想象。秀丽隐杆线虫的综合建模甚至被提议是英国``计算研究的巨大挑战''之一。在这一奖学金中，我将开始将我们对秀丽隐杆线虫感官运动行为的理解整合到单个计算模型中。面临的挑战是弥合有效的静态神经回路结构与它所维持的动态神经计算之间的差距。这项奖学金将使我能够做出一步的变化，不仅在我们对重要模型有机体的理解中，而且还可以推进复杂系统的科学和工程，无论是在反向工程现实世界网络还是在设计它们的背景下。