Neural coding of interneuron populations in the retina

摘要 脊椎动物的视网膜在视神经中将视觉图像转换为电信号，启动 所有视觉感知的基础。这个过程是由数十种不同类型的中间神经元完成的， 每一个细胞都由数千个细胞组成。这些人口中的每一个都覆盖了 视场，共同作用以处理视觉图像的不同方面。尽管有许多信息量 视网膜神经功能的研究已经使用了单细胞记录，了解协调 许多细胞的活动需要记录和分析细胞群体。这项提案的重点是 无长突细胞，一组不同的抑制性中间神经元。特别是，我们研究的领域很广。 无长突细胞是一类重要的细胞，它通过视网膜进行长距离连接，起作用 以组合来自图像中遥远位置的可视信号。我们几乎没有什么信息可以分配给 对此类型的特定单元格的计算。利用遗传识别的广域种群 小鼠视网膜中的无长突细胞，我们将记录这些群体的神经活动，沿着 同时从视网膜神经节细胞群中进行电子记录。神经反应 包括自然场景在内的复杂刺激将使用先进的计算模型进行解释。这个 这些研究的主要目标是1)执行稀疏的第一个种群规模测量 广域无长突细胞，特别是测量它们对视觉特征的选择性如何变化 在自然场景下动态分析这些细胞在自然场景下的神经编码 使用最先进的计算模型，可以捕捉视网膜对任意复杂的反应 刺激，3)检验稀疏广域无长突细胞对 不同的信息渠道，在过程中消除神经节细胞群体的相关性 自然风光。这些结果将立即适用于新兴的视网膜领域 假体，用于治疗诸如老年性黄斑变性和 用高分辨率电子仪替代受损视网膜的功能治疗视网膜色素变性 巡回赛。视网膜神经代码的测量和执行的计算将是 直接用于整合到视网膜假体系统中。

Abstract The vertebrate retina translates visual images into electrical signals in the optic nerve, initiating the basis of all visual perception. This process is accomplished by dozens of diverse types of interneurons, each of which comprises a population of many thousands of cells. Each of these populations cover the visual field, acting together to process different aspects of visual images. Although many informative studies of retinal neural function have used single cell recordings, understanding the coordinated actions of many cells requires the recording and analysis of cell populations. This proposal focuses on amacrine cells, a diverse population of inhibitory interneurons. In particular we study wide-field amacrine cells, a prominent class of cells that make long distance connections across the retina, acting to combine visual signals from distant locations in the image. We have little information assigning computations to specific cells of this type. Using genetically identified populations of wide-field amacrine cells in the mouse retina, we will record neural activity from these populations optically, along with simultaneously recording electrically from populations of retinal ganglion cells. Neural responses to complex stimuli including natural scenes will be interpreted using advanced computational models. The primary goals of these studies are to 1) perform the first population scale measurements of sparse wide-field amacrine cells, in particular to measure how their selectivity for visual features varies dynamically during natural scenes, 2) Analyze the neural code of these cells under natural scenes using state-of-the-art computational models that can capture retinal responses to arbitrarily complex stimuli, 3) Test the hypothesis that sparse wide-field amacrine cells perform similar computations on different channels of information, acting to remove correlations from the ganglion cell population during natural scenes. These results will have immediate applicability to the emerging field of retinal prostheses, as is used to treat prevalent diseases such as age-related macular degeneration and retinitis pigmentosa by replacing the function of the damaged retina with a high resolution electronic circuit. Measurements of the retinal neural code and the computations that are performed will be directly useful for incorporation into retinal prosthesis systems.