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 脊椎动物视网膜将视觉图像转化为视神经中的电信号，启动 所有视觉感知的基础。这个过程是通过数十种类型的中间神经元来完成的， 每个人都包含数千个细胞的人群。这些人群中的每一个都涵盖了 视觉场，共同作用以处理视觉图像的不同方面。虽然有很多信息 视网膜神经功能的研究使用了单细胞记录，了解了协调的 许多细胞的作用需要记录和分析细胞群体。该提议重点 无抑制性中间神经元种群的无长熟细胞。特别是我们学习广场 小麦细胞，一种突出的细胞，在视网膜上建立长距离连接，作用 从图像中遥远的位置组合视觉信号。我们几乎没有分配的信息 计算此类特定单元格。使用遗传鉴定的广阔场所 小鼠视网膜中的无链氨氨酸细胞，我们将光学地记录这些人群的神经活动， 同时从视网膜神经节细胞的种群中进行电记录。神经反应 复杂的刺激将使用高级计算模型来解释。这 这些研究的主要目标是1）执行稀疏的第一个人口量表测量值 宽田伸长细胞，特别是为了测量其对视觉特征的选择性如何变化 在自然场景中动态动态，2）在自然场景下分析这些细胞的神经代码 使用最新的计算模型，可以捕获对任意复杂的视网膜响应 刺激，3）检验以下假设：稀疏的宽田无向细胞对 不同的信息渠道，采取行动以消除神经节细胞种群中的相关性 自然场景。这些结果将立即适用于视网膜的新兴领域 假体，如治疗普遍疾病（例如年龄相关的黄斑变性和） 通过高分辨率电子代替受损视网膜的功能，通过替换视网膜炎色素 电路。视网膜神经代码和执行的计算的测量将是 直接用于纳入视网膜假体系统。