Neuroscientists and computer vision scientists say a new dataset of unprecedented size comprising brain scans of four volunteers who each viewed 5,000 images will help researchers better understand how the brain processes images. Researchers at Carnegie Mellon University and Fordham University, reporting today in the journal Scientific Data, said acquiring functional magnetic resonance imaging (fMRI) scans at this scale presented unique challenges.
Brain processes images
Each volunteer participated in 20 or more hours of MRI scanning, challenging both their perseverance and the experimenters’ ability to coordinate across scanning sessions. The extreme design decision to run the same individuals over so many sessions was necessary for disentangling the neural responses associated with individual images.
The resulting dataset; dubbed BOLD5000, allows cognitive neuroscientists to better leverage the deep learning models that have dramatically improved artificial vision systems. Originally inspired by the architecture of the human visual system; deep learning may be further improved by pursuing new insights; into how human vision works and by having studies of human vision better reflect modern computer vision methods.
Intertwining of brain science
To that end, BOLD5000 measured neural activity arising from viewing images taken from two popular computer vision datasets: Image Net and COCO. “The intertwining of brain science and computer science means that scientific discoveries can flow in both directions;” said co-author Michael J. Tarr, the Kavčic-Moura Professor of Cognitive and Brain Science and head of CMU’s Department of Psychology.
“Future studies of vision that employ the BOLD5000 dataset should help neuroscientists better understand the organization of knowledge in the human brain. As we learn more about the neural basis of visual recognition; we will also be better positioned to contribute to advances in artificial vision.”
Lead author Nadine Chang, a Ph.D. student in CMU’s Robotics Institute who specializes in computer vision; suggested that computer vision scientists are looking to neuroscience to help innovate in the rapidly advancing area of artificial vision reinforcing the two-way nature of this research.
Looking to neuroscience
“Computer-vision scientists and visual neuroscientists essentially have the same end goal: to understand how to process and interpret visual information;” Chang said. Improving computer vision was an important part of the BOLD5000 project from its onset. Senior author Elissa Amin off; then a post-doctoral fellow in CMU’s Psychology Department and now an assistant professor of psychology at Fordham; because initiated this research direction with co-author Abhinav Gupta; an associate professor in the Robotics Institute.
Among the challenges faced in connecting biological and computer vision is that the majority of human neuroimaging studies include very few stimulus images often 100 or less which typically are simplified to depict only single objects against a neutral background. In contrast; BOLD5000 includes more than 5,000 real-world; therefore complex images of scenes, single objects and interacting objects.