Nonlinear interaction of ON and OFF data streams for the detection of visual structure

Visual stimuli lead to neural activity in the retina that is propagated in separate ON and OFF pathways to the cortex. Most models of biological early vision recombine these activity streams by a linear integration at the simple cell level. Based on empirical as well as theoretical investigations we propose a nonlinear recombination circuit that is selectively responsive to contrast magnitude as well as to the sharpness of luminance transition. Simulations with artificial and camera images show a higher positional selectivity for local contrasts than an equivalent linear device. In a multiscale hierarchy the nonlinear circuit produces a unique maximum response in scale-space where scale directly relates to the width of the luminance transition. In order to investigate the biological relevance of the proposed neural circuit, we measured the model sensitivity to luminance gradient reversal in bar stimuli. Our simulations show strong similarity to simple cell recordings in the feline striate cortex. This result further supports the evidence for nonlinear interaction at the simple cell level