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.

颜色和形式通常被视为图像的独立特征。一个人可以识别出可观的照片中的形状，并概念化从形状抽象的物体的颜色。然而，特定于颜色特异性的处理是从视觉途径的第一阶段嵌入整个视觉途径的，其中三种不同类型的光传感器（“锥体”）对视觉频谱的不同部分最初将光转化为电脉冲。可以原理通过比较三种不同的锥类型的激活来确定给定点的颜色，但是要维持单独的颜色通道直到主要的视觉皮层（V1），最终它们在神经元中合并，同时对不同的空间模式具有敏感性。确实，尽管最初认为颜色和形式是通过V1中的单独途径处理的，但最近的实验强调，V1神经元的令人惊讶的一部分以多种方式将它们混合在一起。确切的混合方式以及出于什么目的，是理解人类视野的关键开放问题，并且很难回答，因为这种混合太复杂了，无法使用传统方法来表征。该项目结合了在量身定制的“空间 - 染色”视觉刺激期间对V1神经元活动的大规模记录，并结合了新的计算方法，这些方法为V1内的颜色处理提供了前所未有的高分辨率描述，同时允许确定支持自然色彩视觉的空间染色性混合的潜在功能。该项目还为基于神经生理学和机器学习的本科生和研究生的统计建模提供了跨学科培训的机会。该项目是视觉神经生理学，数据驱动计算建模和模拟的紧密组合。研究人员在皮质层层中执行大规模的多电极记录，以确定从皮质输入（分开的颜色通道）到皮质输出（混合其中）从皮质输入（其中颜色通道分开）的空间 - 色体表示的转换。这些记录是使用非线性数据驱动的模型来解释的，这些模型可以提供驱动每个V1神经元的刺激性的高分辨率空间 - 晶状体图，并区分每个阶段执行的基本计算。通过利用新型的基于模型的眼球跟踪来推动此类字符以实现锥分辨率，该型号可以用魔力顺序的终球灵敏度来解释小眼动。第一个目的决定了如何在V1中组合空间和色素信息的一组原则，这为整个视觉途径的处理奠定了基础。第二个目的确定了这些规则在处理中心（Fovea）的皮质区域中是否相同，该区域负责高积极色彩视觉。最后，最后一个目标建立了一个人群解码框架，用于将单个V1细胞的空间 - 色素敏感性与处理自然色素视觉的视觉皮层范围内的更大的全系统目标联系起来。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识智力和更广泛的影响来通过评估来获得支持的珍贵的支​​持，这些奖项是珍贵的。