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、自由活动的准备中是困难或不可能的，用于促进记录的麻醉剂和麻痹剂经常改变或取消相关行为。我建议建立一个虚构的游泳试验，使瘫痪的斑马鱼幼虫导航通过-并与虚拟环境进行交互。提供给动物的感觉反馈将包括通过视频投影屏幕的视觉提示和由计算机控制的阀门系统提供的水流。这些鱼被允许通过一组外部吸力电极记录的运动神经元的活动来控制这些刺激。这种安排使得固定的鱼完全通过其运动神经元的活动能力来导航虚拟环境成为可能。作为第一步，我们将结合联合收割机这种测定与双光子激光扫描显微镜，并允许转基因鱼线，表达遗传编码的钙指标，在所有神经元导航通过虚拟环境，而神经元的活动进行监测。斑马鱼幼体的小尺寸和完美的透明度原则上对这些记录没有障碍或限制，因此我们在这些行为任务期间可以以单细胞分辨率访问整个大脑。因此，我们可以独立地监测动物在虚拟世界中导航时数百或数千个神经元的活动。作为后续步骤，我们将实施不同的学习试验，动物必须在这个虚拟环境中执行。原则上，这将使我们能够在特定的训练课程之前，期间和之后跟踪单个动物的神经信息流。将使用自动算法分析和提取大量的结果数据，并将结果用于绘制草图。

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