Developing a network-based understanding of Drosophila larvae locomotion using computational neuroscience and live imaging of neural activity

利用计算神经科学和神经活动的实时成像开发对果蝇幼虫运动的基于网络的理解

• 批准号: 2024565
• 金额: --
• 依托单位: University of St Andrews
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2024565
• 关键词: Developing; network; based; understanding; Drosophila

Understanding how neural networks implement behavioural decision-making is a fundamental goal of neuroscience. In this studentship, you will apply cutting-edge techniques in neurogenetics and live imaging to explore how a locomotor-related central pattern generator (CPG) selects and implements motor programmes. You will perform live imaging of CPG activity using genetically-encoded calcium indicators in Drosophila larvae. You will then apply and develop computational neuroscience tools for the analysis of live imaging data. Your aims will be to 1) uncover how the architecture of locomotor networks governs how different types of motor patterns are initiated, maintained, and modified and 2) develop new computational methods for 'mining' of large live imaging datasets in neuroscience. You will work with Bayesian network inference algorithms, a powerful computational methodology for revealing network structure. Bayesian network algorithms have been applied successfully to several types of neural electrophysiology data but not yet to live imaging data, thus you will pioneer this approach.This project is unique in that it will be jointly supervised by both a Drosophila motor systems researcher (SRP) and a Computation neuroscientist (AS). The project will be based in Scotland, but depending on student interest, may involve international travel and collaboration with neuroscientists at Janelia Research Campus and Cold Spring Harbor Laboratories in the United States. This project is a CASE studentship. It will also involve working with Cairn Instruments, a leading optoelectronics company based in Kent, UK.

了解神经网络如何实现行为决策是神经科学的一个基本目标。在这个实习中，你将应用神经遗传学和实时成像的尖端技术来探索运动相关的中央模式发生器（CPG）如何选择和实施运动程序。您将在果蝇幼虫中使用遗传编码的钙指标对CPG活性进行实时成像。然后，您将应用和开发用于分析实时成像数据的计算神经科学工具。您的目标是1）揭示运动网络的架构如何控制不同类型的运动模式的启动，维持和修改，2）开发新的计算方法，用于“挖掘”神经科学中的大型实时成像数据集。您将使用贝叶斯网络推理算法，这是一种用于揭示网络结构的强大计算方法。贝叶斯网络算法已成功应用于多种类型的神经电生理数据，但尚未应用于实时成像数据，因此您将率先采用这种方法。该项目的独特之处在于它将由果蝇运动系统研究员（SRP）和计算神经科学家（AS）共同监督。该项目将以苏格兰为基地，但根据学生的兴趣，可能涉及国际旅行以及与美国Janelia研究校园和冷泉港实验室的神经科学家合作。这个项目是一个案例助学金。它还将涉及与凯恩仪器，一个领先的光电子公司总部设在英国肯特。