Population Coding in the Retina

描述(申请人提供)：视觉研究中最基本的目标之一是了解视觉信息是如何在视网膜输出细胞(神经节细胞)的水平上表现出来的，因为这些细胞提供了关于大脑接收的视觉世界的所有信息。这些细胞由许多不同的类别组成，每个类别对视觉刺激有自己的敏感性，每个类别都产生自己的一组信号。这些细胞如何共同工作以共同形成视觉表征一直是一个关键问题--这是基础科学(理解视觉处理的基本原理)和应用科学(开发驱动视觉假肢的算法)都需要的答案。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。简而言之，该工具是一个视网膜输入/输出模型。它与其他型号的不同之处在于，它适用于范围广泛的 图像统计，包括白噪声、栅格、自然场景(风景、人脸等)的统计有了这个模式，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是测试关于不同类型的神经节细胞在视觉图像表征中的作用的假说。我们使用该模型来构建假设，然后通过电生理学(多电极记录)实验对其进行检验。目的2专注于开发一种新的视网膜假体策略。我们将该模型与光遗传学相结合，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、退化的视网膜对包括时空变化的自然场景在内的广泛刺激产生正常的放电模式。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，因为这是视网膜假体的两个主要刺激目标，每个细胞都有自己的优势。这种方法产生了比现有方法更好的(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.