IDBR: A GPU-accelerated 3D-imaging and 3D-Illumination Sytem for Feedback Control of Light Fields in Biological Light Microscopy

分子神经科学的最新发展使科学家能够用光学记录神经元的活动，并通过用光刺激神经元来引起神经元放电。在斑马鱼和老鼠等遗传模式生物中，这些技术可以使用非侵入性全光方法同时观察和操纵数千个神经元的活动模式。虽然这些技术有望彻底改变我们看待大脑回路的方式，但神经回路基本上是三维结构。因此，必须开发能够成像和选择性刺激3D体积的脑组织的光学工具。该项目将开发一种设备，可以记录每次摄像头曝光时的神经元体积，随着时间的推移从数千个神经元中提取信息，然后使用这些信息来选择用光刺激体积中的哪组单个神经元。这个反馈回路将允许科学家以快速而强大的方式测试关于大脑网络功能及其与行为关系的因果假设，利用目前在机器人学和航空学中使用的反馈控制技术来在线构建和完善大脑的动态模型。该设备的核心是计算显微镜的新发展：光场显微镜(LFM)，它可以根据一张快照计算重建整个体积，以及光场照明器(LFI)，它可以创建(几乎)三维任意光图案。该项目将使用商业显卡(GPU)将这两个设备连接起来，并使用商业显卡(GPU)来实时控制动物行为中的生物神经网络。项目成果，包括将该设备应用于生物标本所产生的科学发现，如何构建物理设备的详细说明，以及运行该设备的免费开源软件，将在http://graphics.stanford.edu/projects/lfmicroscope/.在线提供

Recent developments in molecular neuroscience allow scientists to both optically record neuronal activity and to cause neurons to fire by stimulating them with light. In genetic model organisms like zebrafish and mice, these technologies can be used to observe and manipulate the activity patterns of thousands of neurons at once using non-invasive all-optical methods. While such techniques promise to revolutionize how we look at brain circuits, neural circuits are fundamentally three-dimensional structures. As a result, optical tools able to both image and selectively stimulate 3D volumes of brain tissue must be developed.This project will develop a device that can record a volume of neurons at each camera exposure, extract information from thousands of these neurons over time, and then use this information to choose which groups of individual neurons in the volume to stimulate with light. This feedback loop will allow scientists to test causal hypotheses about brain network function and its relationship to behavior in a fast and powerful way, leveraging feedback-control technologies currently used in robotics and aeronautics to build and refine dynamical models of the brain online. At the core of this device are new developments in computational microscopy: the light field microscope (LFM), which can computationally reconstruct an entire volume from a single snapshot, and the light field illuminator (LFI), which can create (nearly) arbitrary patterns of light in three dimensions. The project will couple these two devices and accelerate their performance using commercial graphics cards (GPUs) to allow real-time control of biological neural networks in behaving animals.Project outcomes, including scientific findings resulting from the application of the device to biological specimens, detailed directions on how to construct the physical device, and free, open-source software to run the device, will be provided online at http://graphics.stanford.edu/projects/lfmicroscope/.