CAREER: The Tuning and Topography of the Ventral Visual Stream

人类可以识别成千上万的物体，如杯子，汽车，鞋子和建筑物，所有这些都是通过进入我们眼睛的光线模式。一个主要的科学前沿是了解大脑如何完成这一壮举。大脑是如何将传入的光波转换成我们周围世界的物体的表征的？这个项目的目标是描述物体信息在视觉系统中的组织特征--在视觉处理的不同阶段，什么样的视觉信息被表现出来，以及它是如何沿着大脑表面映射的？通过使用多种方法的组合，包括人类大脑的测量，再加上计算建模方法，这个项目将促进我们对视觉大脑组织的理解。该项目还将促进妇女在科学领域的进步，重点关注计算机领域的妇女，目标是培养年轻妇女的科学事业，使其成为学术教授和以研究为重点的行业职位，第一个目标将侧重于大脑对物体反应的调节性质，沿着视觉处理层次。具体来说，该项目将研究与通用纹理和形状信息相关的中级特征与对象的高级属性（如它们的生命力和真实世界的大小）紧密相关的假设。第二个目标将通过使用行为和深度神经网络测量视觉和认知相似性空间，然后将这些与功能性神经成像测量的神经反应相关联，来表征腹侧流中反应的调谐。第三个目标将介绍一个计算框架，涉及调整地形使用Kohonen自组织地图。具体来说，PI将首先关注早期视觉皮层视网膜病变的大规模组织作为概念证明，然后将此框架扩展到对象响应皮层。因此，PI的目标是将空间组织的联合收割机数学模型与视觉特征的深度神经网络表示相结合，以产生与人类大脑皮层组织直接可比的人工皮层地图。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。

Humans can recognize thousands of objects, like cups, cars, shoes, and buildings, all from patterns of light entering our eyes. A major scientific frontier is to understand how the brain accomplishes this feat. How are incoming light waves converted by the brain into representations of objects in the world around us? The goal of this project is to characterize the organization of object information across the visual system--what kind of visual information is being represented at different stages of the visual processing, and how is it mapped along the brain’s surface? By using a combination of methods, including measurements of human brains, coupled with computational modeling approaches, this project will advance our understanding of visual brain organization. The project will also promote women's advancement in science, highlighting women in computational fields, with the goal to foster young women's scientific careers into academic professorships and research-focused industry positions.The first aim will focus on the nature of the tuning of brain responses to objects along the visual processing hierarchy. Specifically, the project will examine the hypothesis that mid-level features related to generic texture and shape information are tightly linked to high-level properties of objects like their animacy and real-world size. The second aim will characterize the tuning of responses in the ventral stream, by measuring visual and cognitive similarity spaces using both behavior and deep neural networks, and then relating these with neural responses measured with functional neuroimaging. The third aim will introduce a computational framework to relate tuning to topography using Kohonen self-organizing maps. Specifically, the PI will first focus on the large-scale organization of early visual cortex retinotopy as a proof of concept, and then expand this framework to object-responsive cortex. Thus, the PI aims to combine mathematical models of spatial organization with deep neural network representations of visual features, to produce artificial cortical maps that are directly comparable with the cortical organization of the human brain.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.