Watching a vertebrate brain learn and behave in a virtual environment

描述 摘要： 为了研究导航、学习和记忆的神经基础，同时记录参与行为执行的大量神经元的活动是很重要的。这在清醒的、自由移动的准备中是困难的或不可能的，用于促进记录的麻醉剂和麻痹药经常改变或废除相关行为。我提议建立一种虚构的游泳测试，允许瘫痪的斑马鱼幼鱼在虚拟环境中导航并与之互动。提供给动物的感官反馈将包括通过视频投影屏幕提供的视觉提示和由计算机控制的阀门系统提供的水流。这些鱼被允许通过它们的运动神经元的活动来控制这些刺激，这些活动由一组外部吸力电极记录。这种安排使被固定的鱼完全通过运动神经元的活动能力在虚拟环境中导航成为可能。作为第一步，我们将把这项测试与双光子激光扫描显微镜结合起来，允许在所有神经元中表达遗传编码钙指示物的转基因鱼系在虚拟环境中导航，同时监测神经元的活动。斑马鱼幼体的小尺寸和完美的半透明在原则上对这些记录没有障碍或限制，因此在这些行为任务中，我们可以在单细胞分辨率下访问整个大脑。因此，当动物在它们的虚拟世界中穿行时，我们可以独立地监控成百上千个神经元的活动。作为下一步，我们将实现动物在这个虚拟环境中必须执行的不同学习测试。原则上，这将允许我们跟踪单一动物的神经信息流，在特定的训练课程之前、期间和之后。产生的大量数据将用自动化算法进行分析和提取，并将结果用于SkETC

DESCRIPTION Abstract: In order to study the neural basis of navigation, learning, and memory, it is important to simultaneously record the activity of large populations of neurons involved in execution of behavior. This has been difficult or impossible in awake, freely moving preparations and the anesthetics and paralytics used to facilitate recordings often alter or abolish the relevant behaviors. I propose to establish a fictive swimming assay that allows paralyzed larval zebrafish to navigate through- and interact with a virtual environment. The sensory feedback provided to the animals will consist of visual cues through video projection screens and water flow provided by a computer controlled valve system. The fish are allowed to control these stimuli via activity in their motor-neurons recorded by a set of external suction electrodes. This arrangement makes it possible for immobilized fish to navigate a virtual environment entirely through power of activity in their motorneurons. As a first step we will combine this assay with 2-photon laser-scanning microscopy and allow transgenic fish lines that express genetically encoded calcium indicators in all neurons to navigate through virtual environments while neuronal activity is monitored. The small size and perfect translucence of the larval zebrafish provides in principle no barrier or restriction to these recordings and thus we have access to the whole brain at single cell resolution during these behavioral tasks. We can thus independently monitor the activities of hundreds or thousands of neurons as animals navigate their virtual worlds. As a subsequent step we will implement different learning assays that the animals have to execute in this virtual environment. In principle this will allow us to follow the flow of neural information in a single animal, before, during and after specific training sessions. The large volumes of resulting data will be analyzed and distilled with automated algorithms and the results used to sketc