CRCNS Research Proposal: Collaborative Research: New dimensions of visual cortical organization

CRCNS 研究提案：合作研究：视觉皮层组织的新维度

• 批准号: 1822598
• 负责人: Michael Stryker
• 金额: $ 77.58万
• 依托单位: University of California-San Francisco
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1822598&HistoricalAwards=false
• 关键词: CRCNS; Research; Proposal; Collaborative; New

The visual system of the mouse is now widely studied as a model for developmental neurobiology, as well as for the understanding of human disease, because it can be studied with the most powerful modern genetic and optical tools. This project aims to discover how neurons in the visual cortex of the mouse allow it to see well by measuring how the cortex represents ecologically-relevant properties of the visual world. Quantitative studies of neurons in the mouse's primary visual cortex to date reveal only very poor vision, but their behavior indicates that mice can see much better than that -- they avoid predators and catch crickets in the wild. To understand mouse vision, the investigators will study responses to novel, mathematically tractable stimuli resembling the flow of images across the retina as the mouse moves through a field of grass. Studies based on these new stimuli indicate that most V1 neurons respond reliably to fine details of the visual scene. A mathematical understanding of how the brain takes in the visual world should have real implications for how we see, and should have great benefits for artificial vision by computers and robots. Bringing these ideas into the classroom will provide the foundation for new technologies, and will expose students to both real and artificial vision systems.Analyses of the brain's visual function are limited by the stimuli used to probe them. Conventional quantitative approaches to understanding biological vision have been based on models with linear kernels in which only the output might be subject to a nonlinearity, all derived from responses of neurons in the brain to gratings of a range of spatial frequencies. This analysis fails to capture relevant features of natural images, which can not be constrained to linearity. The goal of this project is to probe the mouse visual system beyond the linear range but below the barrier posed by the complexity of arbitrary natural images. The investigators have identified an intermediate stimulus class--visual flow patterns--that formally approximate important features of natural visual scenes, resembling what an animal would see when running through grass. Flow patterns have a rich geometry that is mathematically tractable. This project will develop such stimuli and test them on awake-behaving mice, while recording the resultant neural activity in the visual cortex. Studying the mouse opens up the possibility of applying the entire range of powerful modern neuroscience tools-- genetic, optical, and electrophysiological. Visual responses will be analyzed using a novel variety of machine learning algorithms, which will allow the investigators to model the possible neural circuits and then test predictions from those model circuits. Such an understanding of the brain will inform both primate vision and the next generation of artificially-intelligent algorithms which, as a result, should benefit from being more "brain-like."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.

现在，对小鼠的视觉系统进行了广泛的研究，作为发育神经生物学以及对人类疾病的理解的模型，因为它可以使用最强大的现代遗传和光学工具进行研究。 该项目旨在发现小鼠视觉皮层中的神经元如何通过测量皮质如何代表视觉世界中与生态相关的特性来很好地看待它。 迄今为止，对小鼠主要视觉皮层中神经元的定量研究仅显示出非常差的视力，但是它们的行为表明，小鼠可以看得比这更好 - 它们避免了捕食者并在野外捕捉板球。为了了解小鼠视力，研究人员将研究对新颖的，数学可触及的刺激的反应，类似于小鼠穿过草的田地，类似于图像的流动。 基于这些新刺激的研究表明，大多数V1神经元对视觉场景的细节可靠地做出了可靠的反应。 对视觉世界中大脑如何摄入的数学理解应该对我们的看法具有真正的影响，并且应该对计算机和机器人对人工视力有很大的好处。 将这些想法带入教室将为新技术提供基础，并将使学生了解真实和人工视觉系统。理解生物视觉的常规定量方法是基于具有线性内核的模型，在该模型中，只有输出可能会受到非线性的影响，这所有模型均来自大脑中神经元的响应，源自大脑中的响应，以提供一系列空间频率的光栅。 该分析无法捕获自然图像的相关特征，而自然图像不能限制为线性。该项目的目的是探测鼠标视觉系统以外的线性范围，但在任意自然图像的复杂性所带来的屏障之下。研究人员已经确定了一种中间刺激类 - 视觉流动模式 - 正式近似自然视觉场景的重要特征，类似于动物在草中奔跑时会看到的东西。流动模式具有丰富的几何形状，可在数学上进行牵引。 该项目将开发这种刺激，并在醒着的小鼠上测试它们，同时记录视觉皮层中产生的神经活动。 研究小鼠为应用了整个强大的现代神经科学工具（遗传，光学和电生理学）的可能性打开了可能性。将使用新型的机器学习算法对视觉响应进行分析，该算法将使研究人员能够对可能的神经回路进行建模，然后测试这些模型电路的预测。 对大脑的这种理解将为灵长类动物的视野和下一代人的人工智能算法提供信息，结果，这些算法应该从更“大脑”中受益。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查标准来通过评估来支持的。

项目成果

Contrast versus luminance in retina and visual cortex

视网膜和视觉皮层的对比度与亮度

• 发表时间: 2021
• 期刊: Abstracts Society for Neuroscience
• 作者: Dyballa, L;Hoseini, MS;Rudzite, M;Field, GD;Stryker, MP;Zucker, SW
• 通讯作者: Zucker, SW

Aberrant cortical spine dynamics after concussive injury are reversed by integrated stress response inhibition.

• DOI: 10.1073/pnas.2209427119
• 发表时间: 2022-10-18
• 期刊: Proceedings of the National Academy of Sciences of the United States of America
• 影响因子: 11.1

Flow stimuli reveal ecologically appropriate responses in mouse visual cortex

• DOI: 10.1073/pnas.1811265115
• 发表时间: 2018-10-30
• 期刊: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• 影响因子: 11.1
• 作者: Dyballa, Luciano;Hoseini, Mahmood S.;Stryker, Michael P.
• 通讯作者: Stryker, Michael P.