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: 10613476
• 负责人: Gregory Darin Field
• 金额: $ 47.31万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 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 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10613476
• 关键词: Affect; Animals; Brain; Collaborations; Communication; 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; Vision; Visual Cortex; Visual System; Work; area striata; blind; cell type; computational neuroscience; data-driven model; experimental study; in vivo; innovation; luminance; multi-electrode arrays; neural; novel; predictive modeling; receptive field; 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.

项目摘要 刺激与越来越自然的特性的使用已成为促进我们对我们对的理解的关键 想象。许多研究表明，简单的人造刺激（例如正弦光栅和白噪声）无法 参与非线性，深刻改变视网膜的反应，侧向核（LGN）和原发性 视觉皮层（V1）。最近且引人注目的例子来自使用自然主义的“流”刺激 V1中未通过对光栅的响应预测的强大响应。理解动机的差距 刺激合奏和分析框架的开发产生对 视觉处理越来越自然主义的刺激及其参与的非线性。我们的目标是 了解如何从视网膜到视觉皮层处理流动刺激。为了实现这一目标，我们将实现 视网膜（AIMS 1和3），LGN（AIMS 1和3）和V1（AIM 3）使用匹配的实验 条件和一个统一的理论/建模框架，以绘制这些发生的转换 视觉处理的阶段。我们的中心假设是，V1改变了离散且严重的光级 - de- 自然刺激的垂直残留表示为连续的（均匀）表示，该表示 变体变化平均亮度。这种不变性对非线性类别构成了强烈限制 将剩余响应转化为对LGN和V1中观察到的关系的纽带。我们以三个目的检验了这一假设：（1） 确定自然主义流动刺激的早期视觉处理（视网膜和LGN）； （2）将编码歧管开发到 在每个处理阶段捕获人口活动，并从一个阶段转变为另一个阶段； （3）测试 流形描述预测光适应对视网膜加工流动刺激的影响的能力 到V1。 AIM 1将产生一个匹配的实验数据集，为一类有趣且新颖的生态发展类别 AIM 2将产生一个定量框架，通过该框架了解之间发生的转换 视网膜，LGN和V1。 AIM 3将通过切换提供一个平台，用于在全球范围内扰动视网膜的输出 从显影到介篇条件，从而将模型的预测与测量的预测进行比较 LGN和V1活性的变化。这项研究的主要意义是它将在计算上提供 以及实验统一的框架，以理解刺激处理中发生的转换 ULI跨越视觉处理的多个阶段。主要的创新是（1）为视网膜提供视觉刺激 与警报动物中的眼动和学生动态相匹配的录音； （2）创建新颖的分析 捕获所有三个级别的神经元的反应以及对内部的层间转换的框架 刺激性刺激； （3）利用光适应作为扰动剩余输出的一种方法来测试我们的模型 LGN和V1对适应剩余信号的响应的稳定性（不变性）。预期的结果是 从视网膜到LGN和V1的数据驱动模型从星光到阳光。