We introduce a computational model of a simple cell, which combines the responses of model LGN cells with center-surround receptive fields (RF). We call it Combination of RFs (CORF) model. We use simulated reverse correlation to demonstrate that the RF map of the CORF model can be divided into elongated inhibitory and excitatory regions, typical of simple cells. Besides orientation selectivity, the CORF model exhibits contrast invariant orientation tuning, cross orientation suppression and response saturation, which are observed in simple cells. These three properties are, however, not possessed by the Gabor function (GF) model, which has gained particular popularity as a computational model of a simple cell. We use two public data sets of images of natural scenes with associated ground truth to compare the CORF and the GF models in a contour detection task, which is assumed to be the primary biological role of simple cells. In this task, the CORF model outperforms the GF model (RuG dataset: t(39) = 4.39, p< 10-4, Berkeley dataset: t(299)= 3.88, p< 10-4). The proposed CORF model is more realistic than the GF model as it shares more properties with simple cells and it is more effective in contour detection.