Population Coding in the Retina

描述（由申请人提供）：我们在系统神经科学中面临的最基本问题之一是理解来自外部世界的信息如何在神经元群体的活动中表现出来。这个建议是针对这个问题，并使用视网膜-具体地说，视网膜的输出细胞-作为模型系统。我们关注两个关键问题：1)细胞使用什么代码来携带信息，2)种群中不同类别的细胞扮演什么角色？为了解决这些问题，我们将使用电生理学和行为学相结合的方法，以啮齿动物为模型物种。我们的具体目标如下：在目标1中，我们将测试关于输出单元使用的代码的假设。具体来说，我们将以不同的编码解码它们的尖峰序列，测量编码在视觉识别任务中的表现，并将其与动物的表现进行比较。我们将专注于一系列广泛提出的代码，从一个简单的尖峰计数代码开始，然后系统地推进一组更复杂的代码（尖峰定时，时间相关和相互关联代码）。我们已经建立了一个实验协议，使我们能够获得每个代码性能的上限。有了上限，我们就可以严格地确定哪些代码对动物是可行的，哪些是不可行的——也就是说，动物可以使用哪些代码，哪些代码必须被排除。在目标2中，我们将测试关于不同类别的视网膜输出细胞对视觉场景表示的贡献的假设。我们将通过解码包含或不包含特定细胞类的输出细胞尖峰序列来做到这一点。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到绘制视网膜的输入/输出关系，因此这项研究也将有助于视网膜假肢算法的发展。

DESCRIPTION (provided by applicant): One of the most basic problems we face in systems neuroscience is understanding how information from the outside world is represented in the activity of populations of neurons. This proposal is directed toward this problem, and uses the retina - specifically, the output cells of the retina - as the model system. We focus on two key questions: 1) What code do the cells use to carry the information, and 2) what roles do the different classes of cells in the population play? To address these questions, we will use a combined electrophysiological and behavioral approach, with the rodent as the model species. Our specific aims are as follows: In Aim 1, we will test hypotheses about the codes used by the output cells. Specifically, we will decode their spike trains assuming different codes, measure the performance of the codes on visual discrimination tasks, and compare the performance to that of the animal. We will focus on a series of widely proposed codes, starting with a simple spike count code, and then advancing systematically through a set of more complex codes (a spike timing, a temporal correlation, and a cross-correlation code). We have set up an experimental protocol that allows us to obtain an upper bound on the performance of each code. With an upper bound, we can rigorously determine which codes are viable for the animal and which are not - that is, which codes the animal can be using and which codes must be ruled out. In Aim 2, we will test hypotheses about the contributions of the different classes of retinal output cells to the representation of visual scenes. We will do this by decoding the output cell spike trains with and without specific cell classes included. 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.