A neural network model of the primary visual cortex.

Many problems in modern computing require a visual component. That is to say, it is fairly common for applications to have a need to see their environments. These applications will typically employ techniques designed specifically to solve the particular task needed for the application, and have little or no relation to the human visual system. Humans generally do not have difficulty interpreting the world around us. When traveling through known environments, we can easily recognize particular walls, doors and other objects in our view. We are not confused by the huge number factors that can complicate an image. The generalization and robustness of the human system would provide a huge benefit to any system that requires more advanced vision than is capable with the ad-hoc methods developed previously. If the underlying principles that make the human visual system so powerful can be identified and implemented programmatically, then a machine could reap the benefits obtained by humans. The purpose of this thesis is to demonstrate that a visual system modeled after the human visual system will be robust and accurate enough to solve real world problems - and to be useful in a non-trivial application. By developing neural networks that directly model the most primitive image processing cells of the human visual system, a platform can be built on which advanced vision systems can be developed.The original print copy of this thesis may be available here: http://wizard.unbc.ca/record=b135329

Tracking the emergence of visual recognition through multivariate approaches

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 121-130).The visual system is a dynamic entity whose response properties depend on context and experience. In this thesis, I examine how the brain changes as we learn to see - what changes occur during the onset of recognition, in the mature visual system on the one hand, and in a developmentally nascent one, on the other? Working with normal adults, I focus on the processes that underlie the interpretation of images as meaningful entities. This interpretation is greatly facilitated by prior information about a stimulus. What are the neural sites that exhibit experience dependent changes? Using multivariate decoding techniques, I find pervasive evidence of such changes throughout the visual system. Critically, cortical regions previously implicated in such learning are not the same loci as sites of increased information. Examining the temporal mechanisms of recognition, I identify the perceptual state transitions corresponding to the onset of meaning in an observed image. Furthermore, decoding techniques reveal the flow of information during this 'eureka moment.' I find feedback processing when a degraded image is first meaningfully interpreted, and then a rapid transition into feed-forward processing for more coherent images. Complementing the studies with mature subjects, my work with developmentally nascent observers explores the genesis of visual interpretation. What neural changes accompany the earliest stages of visual learning? I show that children treated for congenital blindness exhibit significant cortical re-organization after sight onset, in contrast to the classical notion of a critical period for visual plasticity. The specific kind of reorganization suggests that visual experience enhances information coding efficiency in visual cortex. Additionally, I present evidence of rapid development of functionally specialized cortical regions. Overall, the thesis presents two complementary perspectives on the genesis of visual meaning. The results help advance our understanding of how short-term experience, as well as developmental history, shapes our interpretation of the complex visual world.by Scott Gorlin.Ph.D