Distributed neural processing of self-generated visual input in a vertebrate brain

我们每天所做的大部分事情都涉及我们的感官和行动之间持续不断的协调。例如，泡一杯茶需要处理源源不断的视觉和触觉信息，以不断纠正我们如何移动肌肉，以避免牛奶溢出或打破杯子。我们的大脑毫不费力地协调这种感觉到运动的信息流，但这是一种现代工程仍然无法比拟的能力;想想今年DARPA机器人挑战赛中笨拙的机器人。协调感觉输入和运动动作的能力也经常在帕金森氏症和运动障碍等疾病中受损。因此，了解这种能力不仅是现代神经科学的核心目标，而且有望为工程技术带来进步并促进疾病的治疗。在上个世纪，神经科学研究表明，大脑中的各个区域都需要处理感觉和运动信息在活跃的行为中。这些包括感觉和运动系统，但也包括小脑和基底神经节等区域。虽然已经实现了对特定回路中潜在过程的部分理解，但理想情况下，完整的理解需要记录行为动物大脑中的神经活动。这种类型的实验在过去是不可能的，原因有两个：1。神经记录技术要求记录设备和神经组织之间有很大程度的稳定性;因此大多数实验涉及严重限制或麻醉的动物，这会阻止有意义的大脑/环境相互作用。2.典型的脑记录已被限制为细胞分辨率的少量神经元或低空间和/或时间分辨率的大面积脑组织的间接记录。为了应对这些挑战，将联合收割机结合实验和计算神经科学的先进技术。首先，一个游泳的斑马鱼幼体的虚拟现实;这将使我们能够从一个不动的大脑记录，但允许虚构的行为。第二，光片显微镜，一种可以同时成像10000的神经元从整个斑马鱼大脑的技术。第三，分布式计算技术，这将使我们能够分析从这些实验中获得的大量数据集（每次试验高达1 TB）。我们将使用这些工具来解决关于行为动物大脑功能的三个基本问题。首先，当动物积极地参与世界时，大脑接收两种类型的感觉输入：由外部世界的变化引起的感觉输入，例如当水扫过视网膜时鱼所经历的光流，以及它们自身行为的结果的感觉输入，例如鱼所经历的来自其自身游泳的光流。这两种类型的输入传递不同类型的信息，但同时到达视网膜。因此，我们要问的一个中心问题是，让鱼区分它们的大脑回路是什么。第二，当动物自己的行为引起的感官输入不符合它们的期望时，它们很容易适应自己的行为。例如，当水粘度的变化导致它们游泳的实际结果和预期结果之间的不匹配时，鱼调节游泳的强度，即，当他们的游泳没有像他们预期的那样推动他们时。我们会问，是什么分布式神经回路让鱼能够检测到这些不匹配的错误。我们将联合收割机结合我们的研究结果，在一个积极游泳的鱼，再现实验观察，并可用于激励机器人控制systems.This项目将开发新的技术来记录和分析大型神经数据集，并提供独特的洞察大脑功能的分布式和动态的性质所必需的成功的主动行为的闭环控制的生物学合理的模型。

Most of what we do on a day-to-day basis involves the ongoing and fluid coordination between our senses and our actions. For example, making a cup of tea involves processing a constant stream of visual and tactile (touch) information to continuously correct how we move our muscles in order to avoid spilling milk or breaking a mug. Our brains coordinate this flow of sensory to motor information effortlessly, yet it is an ability that modern engineering still cannot rival; think of the clumsy robots in this year's DARPA Robotics Challenge. The ability to coordinate sensory input and motor actions is also often impaired in diseases like Parkinson's and dyspraxia. Understanding this ability is therefore not only a central goal of modern neuroscience but also one that promises to deliver advances for engineering and facilitate the treatment of disease.Over the last century, neuroscience research has revealed that areas across the brain are required to process sensory and motor information during active behaviours. These include sensory and motor systems but also areas such as the cerebellum and the basal ganglia. While a partial understanding of the underlying processes in specific circuits has been achieved, a full understanding would ideally require recordings of the neural activity from across the brain in a behaving animal. This type of experiment has been impossible in the past for two reasons: 1. Neural recording techniques require a great degree of stability between recording devices and neural tissue; thus most experiments involve heavily restrained, or anesthetized, animals, which prevents meaningful brain/environment interactions. 2. Typical brain recordings have been limited to either small numbers neurons at cellular resolutions or indirect recordings from large areas of brain tissue at low spatial and/or temporal resolution. To address these challenges will combine advanced techniques in experimental and computational neuroscience. First, a virtual reality for a swimming larval zebrafish; this will allow us to record from a non-moving brain but allow fictive behaviour. Second, light-sheet microscopy, a technique that can simultaneously image 10000's of neurons from across the zebrafish brain. Third, distributed computing techniques, which will enable us to analyse the enormous data sets (upto a terabyte per trial) acquired from these experiments.We will use these tools to address three fundamental questions about brain function in behaving animals. First, when animals actively engage the world the brain receives two types of sensory input: Sensory input caused by changes in the external world, e.g. the optic flow experienced by a fish as water sweeps past its retina, and sensory input that is a consequence of their own actions, e.g. the optic flow experienced by the fish that results from its own swimming. These two types of inputs convey different types of information but arrive together on the retina. Thus a central question we will ask is what are the brain-wide circuits that allow the fish to distinguish between them. Second, animals readily adapt their behaviour when the sensory inputs caused by their own actions do not meet their expectations. For example, fish modulate the strength of swimming when changes in water viscosity cause a mismatch between the actual and expected consequences of their swimming, i.e., when their swimming does not propel them as far as they expect. We will ask what the distributed neural circuits are that allow fish to detect these mismatch errors. We will combine our results to produce a biologically plausible model of closed-loop control in an actively swimming fish that reproduces experimental observations and could be used to inspire robotic control systems.This project will develop new techniques to record and analyse large neural datasets and provide unique insight into the distributed and dynamic nature of brain function necessary for successful active behaviour.