Neural coding of interneuron populations in the retina

视网膜中间神经元群的神经编码

• 批准号: 10225643
• 负责人: STEPHEN A BACCUS
• 金额: $ 37.6万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-12-01 至 2023-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10225643
• 关键词: Address; Age related macular degeneration; Amacrine Cells; Anatomy; Cells; Code; Complex; Computer Models; Disease; Distant; Eye Movements; Goals; Image; Inner Plexiform Layer; Interneurons; Location; Measurement; Measures; Modeling; Molecular; Motion; Mus; Neural Network Simulation; Neural Retina; Neurons; Neurophysiology - biologic function; Optic Nerve; Optics; Pharmacogenetics; Photoreceptors; Population; Population Heterogeneity; Principal Investigator; Process; Research; Resolution; Retina; Retinal Diseases; Retinal Ganglion Cells; Retinitis Pigmentosa; Saccades; Signal Transduction; Stimulus; System; Testing; Translating; Vision; Visual; Visual Fields; Visual Perception; calcium indicator; cell type; comparative; convolutional neural network; ganglion cell; individual response; inhibitory neuron; object motion; optical imaging; photoreceptor degeneration; presynaptic; programs; relating to nervous system; response; retinal damage; retinal prosthesis; sample fixation; sight restoration; therapy design; visual information; visual stimulus

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.

抽象的 脊椎动物的视网膜将视觉图像转化为视神经中的电信号，从而启动 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， 每个细胞都包含数千个细胞群。这些人群中的每一个都覆盖了 视野，共同作用来处理视觉图像的不同方面。虽然信息量很多 视网膜神经功能的研究使用了单细胞记录，了解协调 许多细胞的活动需要对细胞群进行记录和分析。该提案的重点是 无长突细胞，一种多样化的抑制性中间神经元群体。我们特别研究宽领域 无长突细胞是一类重要的细胞，可在视网膜上进行长距离连接，发挥作用 结合图像中远处位置的视觉信号。我们分配的信息很少 对该类型的特定单元格的计算。利用基因鉴定的广域群体 小鼠视网膜中的无长突细胞，我们将以光学方式记录这些群体的神经活动，以及 同时对视网膜神经节细胞群进行电记录。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 这些研究的主要目标是 1）对稀疏性进行首次人口规模测量 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 在自然场景下动态地进行，2）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，可以捕获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执行类似计算的假设 不同的信息渠道，作用是消除神经节细胞群体的相关性 自然场景。这些结果将立即适用于新兴的视网膜领域 假体，用于治疗常见疾病，例如与年龄相关的黄斑变性和 通过用高分辨率电子替代受损视网膜的功能来治疗色素性视网膜炎 电路。视网膜神经代码的测量和执行的计算将是 可直接用于融入视网膜假体系统。