Population Coding in the Retina

描述(申请人提供)：视觉研究的一个基本目标是了解视网膜输出细胞，即神经节细胞，如何代表视觉世界。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长期存在的问题--对于基础科学(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)来说，都需要这个问题的答案。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。它与其他模型的不同之处在于，它对广泛的图像统计有效，包括白噪声、栅格、自然场景(风景、人脸等)。有了这个模型，我们就可以在这些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。为此，我们使用该模型作为筛选工具。因为它再现了神经节细胞对各种刺激的反应，我们可以用它来快速和系统地探索刺激空间，并评估哪些细胞类别或类别的组合代表不同的刺激特征。给出来自模型的特定预测，然后我们可以使用电生理和/或行为分析进行实验测试，这两种方法在我们实验室都是标准的。目的2专注于开发一种新的视网膜假体策略。有了这个模型，我们可以可靠地将视觉输入转换为神经节细胞使用的代码-也就是说，我们可以驱动假体系统产生正常的视网膜输出，这产生的系统具有比现有方法更好的视觉表现。 与公共健康相关：这些研究将提供有关神经系统用来表示和处理信息的策略的基本信息。由于这项工作的很大一部分涉及绘制视网膜的输入/输出关系，这项研究也将有助于视网膜假体算法的开发。

DESCRIPTION (provided by applicant): A basic goal in vision research is to understand how the retinal output cells, the ganglion cells, represent the visual world. 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 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 these 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. For this, we use the model as a screening tool. Because it reproduces ganglion cell responses to a wide array of stimuli, we can use it to explore stimulus space in a fast and systematic way and assess which cell classes, or combinations of classes, represent different stimulus features. Given specific predictions from the model, we can then test them experimentally using electrophysiological and/or behavioral assays, both of which are standard in our lab. Aim 2 focuses on the development of a new retinal prosthetic strategy. With the model we can reliably convert visual input into the code used by the ganglion cells - that is, we can drive a prosthetic system to produce normal retinal output, which produces a system with substantially better visual representation than existing methods. PUBLIC HEALTH RELEVANCE: These studies will provide fundamental information about the strategies the nervous system uses to represent and process information. Since a substantial portion of the work involves mapping the input/output relationship of the retina, this research will also contribute to the development of algorithms for retinal prosthetics.