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Full-Text Articles in Computational Neuroscience
Model Of Visual Contrast Gain Control And Pattern And Noise Masking, Joshua A. Solomon
Model Of Visual Contrast Gain Control And Pattern And Noise Masking, Joshua A. Solomon
MODVIS Workshop
The first stage of the model can be subdivided into a global contrast sensitivity function (a 2-D log-parabolic filter of spatial frequency), followed by an array of sensors having Gabor-pattern receptive fields. The second stage is contrast gain control. At this stage, sensor outputs are subjected to an expansive transformation. Then the outputs are pooled and used to inhibit (or “normalize”) each other. Inhibition is strongest between sensors with similar preferences for orientation, spatial frequency and spatial location. In the final stage of the model, the nomalized sensor outputs for each image are subjected to Minkowski pooling. Two-alternative, forced-choice detection …