A Push-Pull CORF Model of a Simple Cell with Antiphase Inhibition Improves SNR and Contour Detection

We propose a computational model of a simple cell with push-pull inhibition, a property that is observed in many real
simple cells. It is based on an existing model called Combination of Receptive Fields or CORF for brevity. A CORF model uses
as afferent inputs the responses of model LGN cells with appropriately aligned center-surround receptive fields, and
combines their output with a weighted geometric mean. The output of the proposed model simple cell with push-pull
inhibition, which we call push-pull CORF, is computed as the response of a CORF model cell that is selective for a stimulus
with preferred orientation and preferred contrast minus a fraction of the response of a CORF model cell that responds to the
same stimulus but of opposite contrast. We demonstrate that the proposed push-pull CORF model improves signal-to-noise
ratio (SNR) and achieves further properties that are observed in real simple cells, namely separability of spatial frequency
and orientation as well as contrast-dependent changes in spatial frequency tuning. We also demonstrate the effectiveness
of the proposed push-pull CORF model in contour detection, which is believed to be the primary biological role of simple
cells. We use the RuG (40 images) and Berkeley (500 images) benchmark data sets of images with natural scenes and show
that the proposed model outperforms, with very high statistical significance, the basic CORF model without inhibition,
Gabor-based models with isotropic surround inhibition, and the Canny edge detector. The push-pull CORF model that we
propose is a contribution to a better understanding of how visual information is processed in the brain as it provides the
ability to reproduce a wider range of properties exhibited by real simple cells. As a result of push-pull inhibition a CORF
model exhibits an improved SNR, which is the reason for a more effective contour detection.