Statistical estimation of visual features in retinal circuitry

视网膜电路视觉特征的统计估计

• 批准号: 9020092
• 负责人: Benjamin Nathan Naecker
• 金额: $ 3.44万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-01-02 至 2018-01-01
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9020092
• 关键词: Affect; Age related macular degeneration; Amacrine Cells; American; Area; Bayesian Analysis; Behavior; Blindness; Brain; Cations; Cells; Complex; Computer Simulation; Disease; Environment; Exhibits; Future; Goals; Indium; Injection of therapeutic agent; Lead; Left; Location; Maps; Measurement; Metalcaptase; Modeling; Patients; Pattern; Probability; Process; Property; Prosthesis; Research; Retina; Retinal; Retinal Diseases; Retinal Ganglion Cells; Signal Transduction; Staging; Stimulus; Stream; Structure; Testing; Time; Update; Vision; Visual; Work; base; design; digital; direct application; experience; flexibility; ganglion cell; improved; insight; luminance; neural circuit; neuromechanism; photoreceptor degeneration; public health relevance; receptive field; relating to nervous system; research study; response; retinal prosthesis; sensory neuroscience; sensory system; spatiotemporal; theories; visual stimulus

DESCRIPTION (provided by applicant): The overall goal of this project is to understand how retinal computations are modified in response to the structure of the visual environment. It has long been known that the sensitivity of retinal ganglion cells (RGCs) is depen- dent on recent visual input. These adaptive changes in sensitivity show remarkable flexibility; some RGCs adapt to ignore predictable components of the visual world in favor of encoding what is unexpected. This strategy is thought to maximize the amount of information transmitted to the higher brain. Recent work from our lab, how- ever, suggests that some RGCs use previous visual stimuli to predict future input. High-contrast input to these RGCs, such as a moving object, increases their sensitivity, thereby encoding the object's likely future location. It remais unknown, however, whether RGCs sensitize to properties of the visual environment beyond luminance and contrast, or, more simply, whether the retina maintains predictions about more complex features of the visual world. The first goal of this work is to understand the class of visual features to which the retina exhibits predictive sensitization. A second goal is to take advantage of the tractability of the retina to explore the neural mechanisms underlying prediction, a computation thought to be performed throughout the brain. In addition to improving our fundamental understanding of the retina, these results will have direct application. Retinal prosthetic devices hold the promise of substantially restored vision for those suffering from photoreceptor degeneration diseases, which affect more than two million Americans. As the computation of predictive sensitization is easily implemented in digital circuits, this research wil provide basic scientific insight and computational models to inform in the design of processing elements in retinal prosthetics.

描述（由申请人提供）：该项目的总体目标是了解如何根据视觉环境的结构修改视网膜计算。早就知道，视网膜神经节细胞（RGC）的敏感性取决于最近的视觉输入。灵敏度的这些自适应变化表现出显着的灵活性。一些RGC可以忽略视觉世界的可预测组成部分，而支持编码出乎意料的内容。人们认为这种策略可以最大程度地提高传递给更高大脑的信息量。但是，我们实验室的最新工作表明，一些RGC使用以前的视觉刺激来预测未来的输入。对这些RGC的高对比度输入（例如移动对象）会提高其灵敏度，从而编码对象的未来位置。然而，RGC是否对视觉环境的性质超越亮度和对比度，还是简单地说，视网膜是否维持有关视觉世界更复杂的特征的预测，这是未知的。这项工作的第一个目标是了解视网膜表现出预测敏化的视觉特征类别。第二个目标是利用视网膜探索预测的神经机制的典型性，这是整个大脑中进行的计算。除了提高我们对视网膜的基本了解外，这些结果还将直接应用。视网膜假体设备对患有感光受体变性疾病的人实质性恢复的愿景承诺，这会影响超过200万美国人。由于在数字电路中很容易实施预测敏化的计算，因此这项研究提供了基本的科学见解和计算模型，以告知视网膜假肢中的处理元素的设计。