National Institute for Physiological Sciences Takemura Lab Sensory & Cognitive Brain Mapping
National Institutes of Natural Sciences National Institute for Physiological SciencesNational Institutes of Natural Sciences National Institute for Physiological Sciences



CiNet Friday Lunch Seminar: Noah Benson (University of Washington)

Date and Time

Oct 15th, 2021 (Friday)
12:15-13:00 (Japan Standard Time)


Zoom Online

Registration(deadline: noon, Oct 14 JST)


Noah Benson
Senior Data Scientist
eScience Institute
University of Washington

Title & Abstract

Title: Modeling the Visual Cortex: from Populations of Neurons to Populations of Humans.

Abstract: Across individuals, the size and shape of the brain is relatively conserved, varying in size by a factor of approximately 1.5 across the population. Primary visual cortex (V1), the earliest cortical area responsible for processing visual inputs, however, can vary by a factor of at least 3.5 between individuals who nonetheless have normal vision. In fact, although brain areas in the visual cortex have highly consistent topological organizations across individuals, variation in the folding patterns of the cortex itself is large, making comparison and description of these brain areas difficult both in early sensory cortex as well as higher cortical regions. In this talk I will demonstrate how my research into the anatomical structure and visual function of the three largest visual areas (V1, V2, and V3) can be used to quantify the relationship between the organization of functionally-defined brain areas and brain anatomy. I will discuss various high-dimensional optimization and CNN tools designed to characterize the brain's functional organization and demonstrate how these tools can be used to quantify the degree to which differences between the visual cortices of individuals correspond to differences in their brains' anatomical structures. Finally I will discuss the Human Connectome Project and how large public neuroscience datasets like it can lead to new insights by employing these methods at large scales. Together these datasets and tools can provide a rich source of information about the brain's anatomical structure and can be leveraged to better understand the brain's function.