Contour Detection with the Push Pull CORF model

Contour detection by a computational model of the push-pull inhibition of simple cells

Contour detection with the Push-Pull CORF model of simple cells in visual cortex. A CORF (Combination Of Receptive Fields) model, which was originally proposed in [1], 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 Push-Pull CORF model simple cell 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. The novel 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.
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Azzopardi G, Rodríguez-Sánchez A, Piater J, Petkov N (2014) A Push-Pull CORF Model of a Simple Cell with Antiphase Inhibition Improves SNR and Contour Detection. PLoS ONE 9(7): e98424. doi:10.1371/journal.pone.0098424

Reference
[1] Azzopardi G, Petkov N (2012) A CORF computational model of a simple cell that relies on LGN input outperforms the gabor function model. Biological cybernetics 1–13. doi: 10.1007/s00422-012-0486-6