Computational and Applied Math Proseminar

Department of Mathematics and Statistics, Arizona State University

Friday, January 30, 2004, 12:15 p.m. in SCOB 150

D. Granquist-Fraser

Lockheed Martin Corporation, Goodyear, AZ

Modeling the Physical Limits of Optical Recording and the Resultant Distortion of Functional Maps of Visual Cortex

(also Bioengineering Seminar)
Abstract During the past two decades, functional optical imaging (optical recording) has been established as the primary research tool for investigating the computational topography of cerebral cortex. Studying the spatial functional organization of the brain is crucial for understanding the precise computational strategies used for sensory signal processing. Prior to this work, no detailed error analysis of functional optical imaging had been undertaken. This analysis was needed to determine not only the magnitude of the error, but also its form. Most importantly, it was imperative to model the level and nature of the distortion induced in the cortical topographic maps by the methodological error.

This work provides an interesting example of the interplay between computational biology and biological computation. The methodological error was modeled in two parts. First, the diffusion of the signal due to the turbid nature of the tissue optics of cortex was calculated using a three-dimensional Monte Carlo model. Second, diffraction contributions to the error were calculated by using Gaussian beam decomposition and propagation of a point source wavefront through a model of the instrumental imaging system. The respective results were then convolved to produce the predicted total error.

The effect of the resulting predicted error on the orientation maps of primary visual cortex (V1) was then analyzed. A central debate in the study of V1 organization involves the centration of orientation pinwheels (singularities) in ocular dominance columns. This work's analyses of the effects of methodological error on the distortion of the cortical topography greatly illuminate the underlying misapprehensions that drive this debate.

Brief Biosketch
Dr. Granquist-Fraser holds the position of Senior Staff Research Engineer with the Lockheed Martin Corporation in Goodyear, Arizona. His current work there involves the development and application of biologically inspired machine intelligence algorithms. Other recent academic research has included hierarchical Bayesian modeling of planetary nebulae shells with colleagues at NASA Ames Research Center. Following graduate studies in medical neuroscience at the Louisiana State University Medical Center in New Orleans, Dr. Granquist-Fraser received a doctorate in computational neuroscience from the Cognitive and Neural Systems Department of Boston University. He holds a bachelor's degree in Engineering and Physics from the University of the State of New York, Albany. Prior to his graduate studies, Dr. Granquist-Fraser worked as an optical engineer at the University of Arizona for nearly a decade.

For further information please contact: mittelmann@asu.edu