CAREER: The Tuning and Topography of the Ventral Visual Stream

点击翻译按钮获取中文摘要

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.