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New Model of Human Vision Impacts Future of Neuroscience

By IGI Global on Jan 5, 2011
New research from Duke University, USA shows that the standard model of vision, called feature detection, is incomplete. (www.dukenews.duke.edu/2010/12/egner_vision.html) Feature detection, which was discovered in 1981, is "a process by which specialized nerve cells in the brain respond to specific features of a visual stimulus, such as lines, edges, angle, or movement. The nerve cells fire selectively in response to stimuli that have specific characteristics, e.g. shape, angle, or movement." (http://en.wikipedia.org/wiki/Feature_detection)

The new model of vision, called predictive coding, is based on brain imaging data from a study led by Tobias Egner. Predictive coding is far more complex than feature detection. This model shows that "the brain's visual neurons continually develop predictions of what they will perceive and then correct erroneous assumptions as they take in additional external information." The team of researchers at Duke University monitored the brains of 16 study participants who were looking at pictures of faces and houses. The team used functional Magnetic Resonance Imaging (fMRI) brain scans of the fusiform face area (FFA), which is a region of the brain that recognizes faces. These findings may impact future studies of the brain by neuroscientists. (www.dukenews.duke.edu/2010/12/egner_vision.html)

Recognizing the impact of human vision models on neuroscience and other disciplines, IGI Global, an information science and technology publisher, is proud to present one of its newest releases, Biological and Quantum Computing for Human Vision: Holonomic Models and Applications, authored by Mitja Peruš, University of Ljubljana, Slovenia; and Chu Kiong Loo, Multimedia University, Malaysia. Many-body interactions have been successfully described through models based on classical or quantum physics. More recently, some of the models have been related to cognitive science by researchers who are interested in describing brain activity through the use of artificial neural networks (ANNs). This reference presents an integrated model of human image processing up to conscious visual experience, based mainly on the Holonomic Brain Theory by Karl Pribram. This work researches possibilities for complementing neural models of early vision with the new preliminary quantum models of consciousness in order to construct a model of human image processing.Biological and Quantum Computing for Human Vision: Holonomic Models and Applications


Regarding a variety of scientific models, the authors note that "in science, especially in science of complex systems and phenomena, we have to make approximate models which are never perfect. It is often very hard or even impossible to falsify them, but they are anyway indispensable. We believe that a model is good if there is no better model available in present circumstances. However, we can always only guess how much it fits reality." For more information on this title, please visit www.igi-global.com/Bookstore/TitleDetails.aspx?TitleId=41912.

To complement this reference, IGI Global recommends the International Journal of Computational Models and Algorithms in Medicine (IJCMAM) (Editor-in-Chief Aryya Gangopadhyay, University of Maryland Baltimore County (UMBC), USA). This journal provides comprehensive coverage of computational capabilities, prototypes, and algorithms for medical applications. IJCMAM covers methods for organizing, retrieving, managing, and discovering data found in medicine, as well as includes articles that deal with different types of data such as numeric, text, temporal, spatial, and multimedia. This journal offers state-of-the-art research on unique characteristics of issues in medicine and innovative techniques to solve them. For more information on IJCMAM, including subscription details and a sample copy, please visit www.igi-global.com/ijcmam.

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