CRCNS Research Proposal: Collaborative Research: New Dimensions of Visual Cortical Organization

小鼠的视觉系统现在被广泛研究，作为发育神经生物学的模型，以及了解人类疾病，因为它可以用最强大的现代遗传和光学工具进行研究。 该项目旨在通过测量大脑皮层如何代表视觉世界的生态相关属性，来发现小鼠视觉皮层中的神经元如何让它看得更清楚。 迄今为止，对老鼠初级视觉皮层神经元的定量研究显示，它们的视力非常差，但它们的行为表明，老鼠的视力要好得多--它们避开捕食者，在野外捕捉蟋蟀。为了了解老鼠的视觉，研究人员将研究对新颖的、数学上易于处理的刺激的反应，这些刺激类似于老鼠在草地上移动时视网膜上的图像流。 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做出可靠的反应。 对大脑如何接受视觉世界的数学理解应该对我们如何看有真实的影响，并且应该对计算机和机器人的人工视觉有很大的好处。 将这些想法带入课堂将为新技术提供基础，并将使学生接触到真实的和人工视觉系统。大脑视觉功能的分析受到用于探测它们的刺激的限制。传统的定量方法来理解生物视觉已经基于模型的线性内核，其中只有输出可能会受到非线性，所有来自大脑中的神经元的响应光栅的空间频率的范围。 这种分析无法捕捉自然图像的相关特征，这不能被约束到线性。这个项目的目标是探索小鼠视觉系统超出线性范围，但低于任意自然图像的复杂性所构成的障碍。研究人员已经确定了一个中间刺激类别-视觉流模式-正式近似自然视觉场景的重要特征，类似于动物在草地上奔跑时所看到的。流型具有丰富的几何形状，在数学上易于处理。 该项目将开发这样的刺激，并在清醒的老鼠身上进行测试，同时记录视觉皮层中的神经活动。 研究小鼠开辟了应用整个强大的现代神经科学工具的可能性-遗传，光学和电生理学。视觉反应将使用一种新型的机器学习算法进行分析，这将使研究人员能够对可能的神经回路进行建模，然后测试这些模型回路的预测。 对大脑的这种理解将为灵长类动物的视觉和下一代人工智能算法提供信息，因此，人工智能算法应该会从更“类脑”中受益。“这个奖项反映了NSF的法定使命，并已被认为是值得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的支持。

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.