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Neuroscience Journal
Volume 2016, Article ID 8751874, 16 pages
http://dx.doi.org/10.1155/2016/8751874
Research Article

Adaptive Neuromorphic Circuit for Stereoscopic Disparity Using Ocular Dominance Map

Dayalbagh Educational Institute, Dayalbagh, Agra 282005, India

Received 20 December 2015; Revised 27 February 2016; Accepted 13 March 2016

Academic Editor: Gianfranco Bosco

Copyright © 2016 Sheena Sharma et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Stereopsis or depth perception is a critical aspect of information processing in the brain and is computed from the positional shift or disparity between the images seen by the two eyes. Various algorithms and their hardware implementation that compute disparity in real time have been proposed; however, most of them compute disparity through complex mathematical calculations that are difficult to realize in hardware and are biologically unrealistic. The brain presumably uses simpler methods to extract depth information from the environment and hence newer methodologies that could perform stereopsis with brain like elegance need to be explored. This paper proposes an innovative aVLSI design that leverages the columnar organization of ocular dominance in the brain and uses time-staggered Winner Take All (ts-WTA) to adaptively create disparity tuned cells. Physiological findings support the presence of disparity cells in the visual cortex and show that these cells surface as a result of binocular stimulation received after birth. Therefore, creating in hardware cells that can learn different disparities with experience not only is novel but also is biologically more realistic. These disparity cells, when allowed to interact diffusively on a larger scale, can be used to adaptively create stable topological disparity maps in silicon.