DocumentCode :
3498390
Title :
The role of orientation diversity in binocular vergence control
Author :
Qu, Chao ; Shi, Bertram E.
Author_Institution :
Dept. of Electron. & Comput. Eng., Hong Kong Univ. of Sci. & Technol., Kowloon, China
fYear :
2011
fDate :
July 31 2011-Aug. 5 2011
Firstpage :
2266
Lastpage :
2272
Abstract :
Neurons tuned to binocular disparity in area V1 are hypothesized to be responsible for short latency binocular vergence movements, which align the two eyes on the same object as it moves in depth. Disparity selective neurons in V1 are not only selective to disparity, but also to other visual stimulus dimensions, in particular orientation. In this work, we explore the role of neurons tuned to different orientations in binocular vergence control. We trained an artificial binocular vision system to execute corrective vergence movements based on the outputs of disparity selective neurons tuned to different orientations and scales. As might be expected, we find that neurons tuned to vertical orientations have the strongest effect on the vergence eye movements. The effect of neurons tuned to other orientations decreases as the tuned orientation approaches horizontal. Although adding neurons tuned to non-vertical orientations does not appear to improve vergence tracking accuracy, we find that neurons tuned to non-vertical orientations still play critical roles in binocular vergence control. First, they decrease the time required to learn the vergence control strategy. Second, they also increase the effective range of vergence control.
Keywords :
biology; eye; neural nets; artificial binocular vision system; binocular disparity; binocular vergence control; corrective vergence movement; disparity selective neurons; nonvertical orientation; orientation diversity; short latency binocular vergence movement; vergence eye movement; vergence tracking accuracy; visual stimulus dimension; Biological neural networks; Cameras; Histograms; Neurons; Training; Trajectory;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks (IJCNN), The 2011 International Joint Conference on
Conference_Location :
San Jose, CA
ISSN :
2161-4393
Print_ISBN :
978-1-4244-9635-8
Type :
conf
DOI :
10.1109/IJCNN.2011.6033511
Filename :
6033511
Link To Document :
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