CRCNS Research Proposal: Computations for spatial-chromatic interactions and their physiological implementation in primary visual cortex

CRCNS 研究提案：空间色彩相互作用的计算及其在初级视觉皮层中的生理实现

• 批准号: 2113197
• 负责人: Daniel Butts
• 金额: $ 64.98万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2113197&HistoricalAwards=false
• 关键词: CRCNS; Research; Proposal; Computations; spatial

Color and form are often treated as separable features of an image. One can recognize shapes in achromatic photographs and conceptualize the color of an object abstracted from shape. Yet color-specific processing is embedded throughout the visual pathway from the first stage of the visual pathway, where three different types of light sensors (“cones”) with sensitivity to different parts of the visual spectrum initially convert light into electrical impulses. The color of a given point can in principle be determined by comparing the activation of the three different cone types, but the separate color channels are maintained until the primary visual cortex (V1), where they are finally combined in neurons that concurrently have sensitivity to different spatial patterns. Indeed, while it was initially thought that color and form were processed through separate pathways within V1, recent experiments have highlighted that a surprising fraction of V1 neurons mix them together in a diversity of ways. Exactly how the mixing occurs, and for what purpose, are critical open questions in understanding human vision, and have been difficult to answer because such mixing is too complicated to characterize using traditional approaches. This project combines large-scale recording of V1 neural activity during tailored “spatio-chromatic” visual stimulation with new computational approaches that offer an unprecedented high-resolution description of color processing within V1 while allowing determination of the underlying function of spatio-chromatic mixing in supporting natural color vision. The project also provides opportunity for cross-disciplinary training in neurophysiological and machine-learning based statistical modeling of undergraduate and graduate students.This project is a tight combination of visual neurophysiology, data-driven computational modeling, and simulation. The investigators perform large-scale multi-electrode recordings across cortical lamina to determine the transformations of spatio-chromatic representations from cortical inputs (where color channels are separate) to cortical outputs (where they are mixed). These recordings are interpreted using nonlinear data-driven models that can provide high-resolution spatio-chromatic maps of the stimuli driving each V1 neuron, and distinguish the underlying computations being performed at each stage. Such characterizations are pushed to achieve cone-resolution by leveraging novel model-based eye-tracking that can account for small eye movements with an order-of-magnitude finer sensitivity than standard approaches. The first Aim determines the set of principles governing how spatial and chromatic information is combined in V1, which sets the foundation for processing throughout the visual pathway. The second Aim determines whether these rules are the same in the area of cortex processing the center-of-gaze (fovea), which is responsible for high-acuity color vision. Finally, the last Aim establishes a population decoding framework for linking spatio-chromatic sensitivity of individual V1 cells to the larger systems-wide goals of the visual cortex in processing natural color vision.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

颜色和形状通常被视为图像中可分离的特征。人们可以在消色差照片中识别形状，并将从形状中抽象出来的物体的颜色概念化。然而，从视觉通路的第一阶段开始，特定颜色的处理就嵌入在整个视觉通路中，其中三种不同类型的光传感器（“锥”）对视觉光谱的不同部分具有灵敏度，最初将光转换为电脉冲。原则上，可以通过比较三种不同的视锥细胞类型的激活来确定给定点的颜色，但分离的颜色通道一直保持到初级视觉皮层（V1），在那里它们最终结合在同时对不同空间模式具有敏感性的神经元中。事实上，虽然最初认为颜色和形状是通过V1内部的不同途径处理的，但最近的实验强调，一部分V1神经元以多种方式将它们混合在一起。确切地说，混合是如何发生的，以及为了什么目的，是理解人类视觉的关键开放问题，并且很难回答，因为这种混合太复杂了，无法用传统方法来表征。该项目结合了量身定制的“空间-色彩”视觉刺激期间V1神经活动的大规模记录和新的计算方法，在V1中提供前所未有的高分辨率颜色处理描述，同时允许确定支持自然色彩视觉的空间-色彩混合的潜在功能。该项目还为本科生和研究生提供了基于神经生理学和机器学习的统计建模的跨学科培训机会。这个项目是视觉神经生理学、数据驱动的计算建模和仿真的紧密结合。研究人员在皮质层上进行大规模的多电极记录，以确定从皮质输入（其中颜色通道是分开的）到皮质输出（其中颜色通道是混合的）的空间颜色表征的转换。这些记录使用非线性数据驱动模型进行解释，该模型可以提供驱动每个V1神经元的刺激的高分辨率空间色图，并区分每个阶段正在执行的潜在计算。通过利用新颖的基于模型的眼动追踪技术，这些特征被推向了锥分辨率，这种方法可以用比标准方法更高的灵敏度来解释小的眼球运动。第一个目标决定了一套控制V1中空间和色彩信息如何组合的原则，这为整个视觉通路的处理奠定了基础。第二个目的是确定这些规则在处理注视中心（中央凹）的皮质区域是否相同，中央凹负责高灵敏度的色觉。最后，本文建立了一个群体解码框架，将单个V1细胞的空间色彩敏感性与视觉皮层在处理自然色觉过程中更大的系统范围目标联系起来。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。