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的可分离特征。人们可以识别非彩色照片中的形状，并概念化从形状中抽象出来的物体的颜色，但颜色特定的处理从视觉通路的第一阶段就嵌入到整个视觉通路中。 ，其中对视觉光谱的不同部分敏感的三种不同类型的光传感器（“视锥细胞”）最初将光转换为电脉冲，原则上可以通过比较三种不同视锥细胞类型的激活来确定给定点的颜色。 ，但保留单独的颜色通道直到初级视觉皮层（V1），它们最终被组合成同时对不同空间模式敏感的神经元。事实上，虽然最初认为颜色和形状是通过 V1 内的不同通路进行处理的，但最近的实验强调了这一点。令人惊讶的是，V1 神经元以多种方式将它们混合在一起，混合的具体方式和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而无法使用来表征。该项目结合了V1的大规模录制。该项目还提供了新的计算方法，可在定制的“空间色彩”视觉刺激过程中对神经活动进行研究，该方法在 V1 内提供前所未有的高分辨率颜色处理描述，同时允许确定空间色彩混合在支持自然色彩视觉中的基本功能。本科生和研究生基于神经生理学和机器学习的统计建模的跨学科培训的机会。该项目是视觉神经生理学、数据驱动的计算建模和模拟的紧密结合。研究人员进行大规模多电极记录。穿过皮质层来确定从皮质输入（其中颜色通道是独立的）到皮质输出（其中它们是混合的）的空间色彩表示的转换，这些记录使用可以提供高分辨率空间色彩图的非线性数据驱动模型进行解释。驱动每个 V1 神经元的刺激，并区分每个阶段执行的底层计算，通过利用可以解释小眼睛的新型基于模型的眼睛跟踪来实现视锥分辨率。第一个目标确定了控制空间和色彩信息如何在 V1 中组合的一组原则，这为整个视觉通路的处理奠定了基础。处理注视中心（中央凹）的皮层区域的规则是相同的，该区域负责高敏锐度的色觉。最后，最后一个目标建立了一个群体解码框架，用于链接空间色敏感度。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。