Population Coding in the Retina

描述（由申请人提供）：视觉研究的最基本目标之一是了解视觉信息如何在视网膜输出细胞（神经节细胞）的水平上表示，因为这些细胞提供了大脑接收的有关视觉世界的所有信息。这些细胞由许多不同的种类组成，每种细胞对视觉刺激都有自己的敏感性，并且每种细胞都产生自己的一组信号。这些细胞如何共同形成视觉表征一直是一个长期存在的关键问题-基础科学（理解视觉处理的基本原理）和应用科学（开发驱动视觉修复的算法）都需要回答这个问题。我们最近开发了一个工具来解决这个问题，并将其用于这两个目的。简言之，该工具是视网膜输入/输出模型。它与其他模型的不同之处在于，它对广泛的 图像统计，包括白色噪声、光栅、自然场景（风景、人脸等）有了这个模型，我们可以在这两个目标上取得快速进展。我们的具体目标如下：目标1是测试假设的不同的神经节细胞类的作用，在代表视觉图像。我们使用模型来建立假设，然后进行电生理（多电极记录）实验来验证它们。目标2的重点是开发一种新的视网膜修复策略。我们使用该模型结合光遗传学开发了一种可以产生正常视网膜输出的系统，即它可以使失明的退化视网膜对广泛的刺激产生正常的放电模式，包括时空变化的自然场景。在这里，我们将开发和扩展该方法，特别是，使其不仅对神经节细胞有效，而且对双极细胞也有效，因为这是视网膜修复术的两个主要刺激目标，并且每个都有自己的优势。这种方法比现有的方法产生更好的（接近正常）的视觉图像表示。

DESCRIPTION (provided by applicant): One of the most basic goals in vision research is to understand how visual information is represented at the level of the retinal output cells, the ganglion cells, as these cells provide all the information about the visual world the brain receives. These cells are made up of many different classes, each with its own sensitivities to visual stimuli, and each producing its own set of signals. How these cells work together to collectively form visual representations has been a long-standing critical question - one whose answer is needed both for basic science (for understanding fundamentals of visual processing) and for applied science (for developing algorithms to drive visual prosthetics). We recently developed a tool for addressing this and use it for both these purposes. Briefly, the tool is a retinal input/output model. It differs from other models in that it's effective on a broad range of image statistics, including those of white noise, gratings, natural scenes (landscapes, faces, etc.) With the model we can make rapid advances on both goals. Our Specific Aims are the following: Aim 1 is to test hypotheses about the roles of the different ganglion cell classes in representing visual images. We use the model to build the hypotheses, and then electrophysiology (multi-electrode recording) experiments to test them. Aim 2 focuses on the development of a new retinal prosthetic strategy. We used the model combined with optogenetics to develop a system that can produce normal retinal output, that is, it can make blind, degenerated retinas produce normal firing patterns to a broad range of stimuli, including spatiotemporally varying natural scenes. Here we will develop and expand the method, specifically, so that it is effective not just for ganglion cells but also for bipolar cells, as thse are the two major stimulation targets for retinal prosthetics, and each has its own strengths. This approach produces substantially better (near-normal) representation of visual images than existing methods.