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: 10376246
• 负责人: Gregory Darin Field
• 金额: $ 46.15万
• 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 2022-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10376246
• 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; data-driven model; 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.

项目摘要 使用具有越来越自然主义性质的刺激已成为促进我们对 幻象。许多研究表明，简单的人工刺激(如正弦光栅和白噪声)不能 参与可深刻改变视网膜、外侧膝状体(LGN)和初级神经元反应的非线性反应 视皮层(V1)。最近一个引人注目的例子是使用自然主义的“流动”刺激，这种刺激 V1中的强健响应不能从对栅格的响应中预测。这种理解上的差距促使人们 开发刺激集合和分析框架，以产生对 视觉处理对日益自然主义的刺激和它们所涉及的非线性。我们的目标是 了解流动刺激是如何从视网膜通过视觉皮质处理的。为了实现这一目标，我们将使 用配对实验记录视网膜(AIMS 1和3)、LGN(AIMS 1和3)和V1(AIMS 3)的神经群记录 条件和统一的理论/建模框架，以映射在这些环境中发生的转变 视觉处理的各个阶段。我们的中心假设是，V1将一个离散的、重度的轻能级-de-De- 将自然刺激的悬垂视网膜表示转化为连续的(统一的)表示，该表示相对在- 随着平均亮度的变化而变化。这种不变性对这类非线性系统施加了很强的约束。 将视网膜反应转化为LGN和V1中观察到的反应的纽带。我们从三个方面检验这一假说：(1) 确定自然流动刺激的早期视觉加工(视网膜和LGN)；(2)开发编码流形以 捕获每个处理阶段的种群活动，并从一个阶段转换到下一个阶段；(3)测试 多种描述对光适应对加工视网膜流刺激的影响的预测能力 转到V1。目标1将产生一个与生态相关的有趣而新颖的类别的匹配实验数据集 刺激物。目标2将提供一个量化框架，通过该框架可以理解 视网膜、LGN和V1。AIM 3将提供一个通过切换来全局干扰视网膜输出的平台 从明视到中间视和暗视条件，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种计算上的 和实验上统一的框架，用于理解在Stim过程中发生的转变- 跨越视觉处理的多个阶段的ULi。主要创新点是：(1)呈现视网膜的视觉刺激 与警觉动物的眼球运动和瞳孔动力学相匹配的记录；(2)创造了一种新的分析 一个框架，捕获所有三个水平的神经元的反应和层间转换，以在不同的 (3)利用光适应作为干扰视网膜输出的方法来测试我们的模型 LGN和V1对适应视网膜信号的反应的稳定性(不变性)。预期的结果是 数据驱动的从视网膜到LGN和V1的处理模型，概括了从星光到阳光的过程。