Elucidating novel features of visual processing and physiological connectivity from retina to primary visual cortex

阐明从视网膜到初级视觉皮层的视觉处理和生理连接的新特征

• 批准号: 10229447
• 负责人: Gregory Darin Field
• 金额: $ 48.76万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10229447
• 关键词: Affect; Animals; Brain; Collaborations; Complex; Cone; Data; Data Set; Development; Environment; Eye Movements; Future; Goals; Lateral Geniculate Body; Light; Light Adaptations; Machine Learning; Maps; Mathematics; Measurement; Measures; Mediating; Methods; Modeling; Movement; Mus; Neurons; Noise; Optics; Outcome; Output; Pathway interactions; Physiological; Population; Process; Property; Pupil; Research; Retina; Retinal Ganglion Cells; Rod; Signal Transduction; Stimulus; Structure; Sunlight; Techniques; Testing; Theoretical model; Variant; Vision; Visual Cortex; Visual system structure; Work; area striata; base; blind; cell type; computational neuroscience; experimental study; in vivo; innovation; luminance; multi-electrode arrays; novel; predictive modeling; receptive field; relating to nervous system; response; retinal neuron; sight restoration; stimulus processing; visual processing; visual stimulus

Project Summary The use of stimuli with increasingly naturalistic properties has become critical to advance our understanding of vision. Many studies demonstrate that simple artificial stimuli (e.g. sinusoidal gratings and white noise) fail to engage nonlinearities that profoundly alter responses in the retina, lateral geniculate nucleus (LGN), and primary visual cortex (V1). A recent and striking example comes from the use of naturalistic ‘flow’ stimuli, which engage robust responses in V1 that are not predicted from responses to gratings. This gap in understanding motivates the development of a stimulus ensemble and analysis framework that produces a quantitative understanding of visual processing to increasingly naturalistic stimuli and the nonlinearities that they engage. Our objective is to understand how flow stimuli are processed from retina through visual cortex. To meet this goal, we will make neural population recordings in retina (Aims 1 & 3), LGN (Aims 1 & 3) and V1 (Aim 3) using matched experimental conditions and a unified theoretical/modeling framework to map the transformations that occur across these stages of visual processing. Our central hypothesis is that V1 transforms a discrete and heavily light-level-de- pendent retinal representation of natural stimuli into a continuous (uniform) representation that is relatively in- variant to changes in the mean luminance. This invariance places a strong constraint on the class of nonlineari- ties that transform retinal responses to those observed in LGN and V1. We test this hypothesis in three aims: (1) determine early visual processing (retina & LGN) of naturalistic flow stimuli; (2) develop an encoding manifold to capture the population activity at each processing stage and transforms from one stage to the next; (3) test the ability of the manifold description to predict the impact of light adaptation on processing flow stimuli from retina to V1. Aim 1 will yield a matched experimental dataset to an interesting and novel class of ecologically-relevant stimuli. Aim 2 will yield a quantitative framework by which to understand the transformations that occur between retina, LGN, and V1. Aim 3 will provide a platform for globally perturbing the output of the retina by switching from photopic to mesopic and scotopic conditions, and thereby compare predictions of our model to measured changes in LGN and V1 activity. The primary significance of this research is that it will provide a computationally and experimentally unified framework for understanding the transformations that occur in the processing of stim- uli across multiple stages of visual processing. The major innovations are (1) presenting visual stimuli for retinal recordings that are matched to eye movements and pupil dynamics in alert animals; (2) creating a novel analysis framework that captures the responses of neurons at all three levels and the inter-level transformations to in- creasingly complex stimuli; (3) utilizing light adaptation as a method of perturbing retinal output to test our model and the stability (invariance) of LGN and V1 responses to adapting retinal signals. The expected outcome is a data-driven model of the processing from retina to LGN and V1 that generalizes from starlight to sunlight.

项目概要 使用具有越来越自然属性的刺激对于增进我们对事物的理解变得至关重要 想象。许多研究表明，简单的人工刺激（例如正弦光栅和白噪声）无法 涉及非线性，深刻改变视网膜、外侧膝状核（LGN）和初级反应 视觉皮层（V1）。最近一个引人注目的例子来自自然主义“心流”刺激的使用，它涉及 V1 中的鲁棒响应不是根据光栅响应预测的。这种理解上的差距激发了 开发刺激集合和分析框架，以定量理解 对日益自然的刺激及其所涉及的非线性进行视觉处理。我们的目标是 了解如何从视网膜通过视觉皮层处理流动刺激。为了实现这一目标，我们将 使用匹配的实验记录视网膜（目标 1 和 3）、LGN（目标 1 和 3）和 V1（目标 3）中的神经群体记录 条件和统一的理论/建模框架来映射这些条件中发生的转变 视觉处理的阶段。我们的中心假设是 V1 转换了离散且高度轻的层次化 自然刺激的悬垂视网膜表征转化为相对连续（均匀）的表征 平均亮度变化的变量。这种不变性对非线性类别产生了强烈的约束 将视网膜反应转变为 LGN 和 V1 中观察到的反应。我们通过三个目标检验这一假设：(1) 确定自然流刺激的早期视觉处理（视网膜和 LGN）； (2) 开发编码流形 捕获每个处理阶段的群体活动并从一个阶段转换到下一个阶段； (3) 测试 流形描述预测光适应对视网膜处理流刺激的影响的能力 到V1。目标 1 将产生一个与生态相关的有趣且新颖的类别相匹配的实验数据集 刺激。目标 2 将产生一个定量框架，通过该框架来理解之间发生的转变 视网膜、LGN 和 V1。 Aim 3 将提供一个平台，通过切换全局扰动视网膜的输出 从明视到中明和暗视条件，从而将我们模型的预测与测量进行比较 LGN 和 V1 活性的变化。这项研究的主要意义在于它将提供一个计算方法 和实验统一的框架，用于理解刺激处理过程中发生的转变 uli 跨越视觉处理的多个阶段。主要创新点是（1）为视网膜呈现视觉刺激 与警觉动物的眼球运动和瞳孔动态相匹配的记录； (2) 创造新颖的分析 该框架捕获所有三个级别的神经元的响应以及对输入的层间转换 日益复杂的刺激； (3) 利用光适应作为扰动视网膜输出的方法来测试我们的模型 以及 LGN 和 V1 对适应视网膜信号的响应的稳定性（不变性）。预期结果是 从视网膜到 LGN 和 V1 的处理数据驱动模型，从星光到阳光的推广。