Neural coding of interneuron populations in the retina

摘要 脊椎动物的视网膜将视觉图像转化为视神经中的电信号，启动视觉信号。 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， 每一个细胞都包括数千个细胞的群体。这些人群中的每一个都涵盖了 视觉领域，共同作用，以处理视觉图像的不同方面。虽然许多信息 视网膜神经功能的研究使用单细胞记录，了解协调的 许多细胞的活动需要记录和分析细胞群。该提案的重点是 无长突细胞，一种多样的抑制性中间神经元。特别是我们研究宽场 无长突细胞，一种在视网膜上进行长距离连接的突出细胞， 以联合收割机组合来自图像中远处位置的视觉信号。我们几乎没有分配信息 计算到这种类型的特定细胞。利用遗传鉴定的宽领域种群 无长突细胞在小鼠视网膜，我们将记录这些群体的神经活动光学，沿着 同时对视网膜神经节细胞群进行电记录。Neural responses to 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。的 这些研究的主要目标是：1）进行稀疏的第一次人口规模测量， 宽视野无长突细胞，特别是测量它们对视觉特征的选择性如何变化 2）分析自然场景下这些细胞的神经代码 使用最先进的计算模型，可以捕捉视网膜反应， 刺激，3）测试稀疏的宽视野无长突细胞对刺激进行类似计算的假设。 不同的信息渠道，作用是从神经节细胞群体中去除相关性， 自然场景。这些结果将立即适用于新兴领域的视网膜 假体，如用于治疗流行疾病，如年龄相关性黄斑变性， 视网膜色素变性通过用高分辨率电子替代受损视网膜的功能， 电路.视网膜神经代码的测量和所执行的计算将是 直接用于结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.