Computational modeling of the human brain has long been an important goal of scientific research. The visual system is of particular interest because it is one of the primary modalities by which we understand the world. One integral aspect of vision is object representation, which plays an important role in machine perception as well. In the human brain, object recognition is a part of the functionality of the ventral pathway. In this work, we have developed a computational and statistical techniques to characterize object representation among this pathway. The understanding of how the brain represents objects is essential to developing models of computer vision that are truer to how humans perceive the world.
In the ventral pathway, the lateral occipital complex (LOC) is known to respond to images of objects. Neural recording studies in monkeys have shown that the homologue for LOC represents objects as configurations of medial axis and surface components. In this work, we designed and implemented novel experiment paradigms and developed algorithms to test whether the human LOC represents medial axis structure as in the monkey models. We developed a data-driven iterative sparse regression model guided by neuroscience principles in order to estimate the response pattern of LOC voxels. For each voxel, we modeled the response pattern as a linear combination of partial medial axis configurations that appeared as fragments across multiple stimuli. We used this model to demonstrate evidence of structural object coding in the LOC. Finally, we developed an algorithm to reconstruct images of stimuli being viewed by subjects based on their brain images. As a whole, we apply computational techniques to present the first significant evidence that the LOC carries information about the medial axis structure of objects, and further characterize its response properties.
Haluk Tokgozoglu received the Bachelors of Engineering in Computer Science and Engineering from Bilkent University in 2009, and the Masters of Science in Computer Science from Johns Hopkins University in 2012. He enrolled in the Computer Science Ph.D. program at Johns Hopkins University in 2010. His research focuses on Machine Learning, Computer Vision and Visual Neuroscience.