BRAIN EAGER: Using Optogenetic Techniques in Combination with Free Flight Perturbations to Elucidate Neural Structure Governing Flight Control in D. Melanogaster

建立单个神经元功能和能够产生复杂行为的神经元集合行为之间的联系是当代神经科学的中心目标。实现这一目标的道路正在被基因技术彻底改变，这种技术允许操纵神经元的活动并测量对行为的影响。果蝇的飞行行为非常适合这种联合分析，因为可以非常容易地通过遗传操作苍蝇，并且可以非常详细地定量描述它们丰富的自由飞行行为。这个项目将使用这种方法来解开一个非凡的神经电路的设计和操作，该电路负责为苍蝇提供动物界最快的反应时间之一，并控制它们的高速机动能力。利用附着在苍蝇身上的小磁铁和施加的磁场，苍蝇将受到半空中改变其飞行状态的力。通过开启和关闭控制电路中控制它们对这种力的反应的神经元，并确定它们翅膀运动的结果变化，将确定每个单独的神经元在控制这些昆虫恢复到实验扰动的神经电路中所扮演的角色。更广泛地说，这项工作将为询问和建立对许多其他复杂神经电路的理解的一般、强大的方法奠定基础。此外，这些发现将为开发可用于机器人的高效控制策略提供设计原则。这一发现的渠道将通过会议、出版物和研讨会进行宣传。此外，分析程序和结果数据将通过首席调查员小组网站提供。果蝇(果蝇)的飞行提供了一套丰富的自由飞行行为，可以使用PI最近开发的方法来非常详细地定量描述这些行为。为此，这个项目将开发一个平台，在这个平台上，可以使用光遗传学来操纵这个电路中的每个神经元，并将改变后的行为反应量化，目的是以前所未有的细节解剖一个在行为上至关重要但知之甚少的神经电路。至关重要的是，所采取的方法需要使用飞行运动学的大量经验数据集，并结合动力系统的数学理论来生成降阶模型。这些模型将用于指导实验设计和所产生的运动学数据的解释。将这种方法应用于运动神经元，将被用来阐明特定的转向肌肉在飞行控制过程中的作用。将这种方法应用于将感觉反应传递给运动神经元的中间神经元，正在被用来阐明神经控制电路的设计和功能，该电路决定了苍蝇对空中扰动的反应。更广泛地说，昆虫飞行所需机械的复杂性和层次化布局是其他复杂神经电路的典型特征。

Forging the link between individual neuron function and the behavior of collections of neurons that can produce complex behaviors is the central goal of contemporary neuroscience. The path towards achieving this goal is being revolutionized by genetic techniques that allow for manipulation of the activity of neurons and measurement of the effect on behavior. Flight behavior in the fruit fly, Drosophila, is highly suitable for this combined analysis, since flies can be genetically manipulated very easily and their rich set of free flight behaviors can be quantitatively characterized in great detail. This project will use this approach to unravel the design and operation of a remarkable neural circuit responsible for giving flies one of the fastest response times in the animal kingdom and controlling their high speed maneuvering capabilities. Using small magnets attached to the flies and applied magnetic fields, flies will be subjected to forces in midair that alter their flight. By turning on and off the neurons in the control circuit that governs their response to such forces and determining the resulting change in their wing motions, the role that each individual neuron plays in the neural circuit that governs the recovery of these insects to the experimental perturbation will be determined. More broadly this work will lay the framework for a general powerful approach for interrogating and building an understanding of many other complex neural circuits. Moreover, the discoveries made will inform design principles for the development of efficient control strategies that can be used in robots. This pipeline for discovery will be publicized through conference meetings, publications, and workshops. In addition, the analysis routines and resulting data will be made available through the Principal Investigator's group web site. The flight of fruit flies (Drosophila) provides a rich set of free flight behaviors that can be quantitatively characterized in great detail using methods recently developed by the PI. Towards this end, this project will develop a platform in which each neuron in this circuit can be manipulated using optogenetics and the altered behavioral response quantified, with the aim of dissecting with unprecedented detail a behaviorally vital yet poorly understood neural circuit. Crucially, the approach taken entails using large empirical data sets of flight kinematics in conjunction with the mathematical theory of dynamical systems to generate reduced order models. These models will be used to guide the experiment design and interpretation of the resulting kinematic data. Application of this approach to motor-neurons will be used to elucidate the role of specific steering muscles in the flight control process. Application of this approach to the inter-neurons, which relay sensory responses to the motor-neurons, is being used to elucidate the design and function of the neural control circuit that determines the fly's response to mid-air perturbations. More broadly the complexity and hierarchical layout of the machinery necessary for insect flight is typical of other complex neural circuits.