Surface-Related Features Figure 7. Competition between junction signals. Junction signals can locally compete with each other to avoid ambiguous signals. In addition, centre-surround inhibition helps to suppress multiple junctions in a small neighbourhood which could be induced by fine texture or noise. doi:10.1371/journal.pone.0005909.g007 providing cues that are perceptually relevant for fundamental visual processes such as occlusion detection. Robustness to noise In a first simulation an artificially created image of a noisy square is employed to demonstrate the robustness of the model against noise perturbations. The image was created such that the standard deviation of the additive Gaussian noise equals the luminance difference of the square against the background (so-called 100% noise). Figure 8 shows that initial complex cell responses are strongly disrupted resulting from the additive noise pattern. However, recurrent feedforward-feedback iterations lead to a significant reduction of noise responses and at the same time to a strengthening of contour and contour-termination signals corresponding to V1 bipole and V1 end-stop cell activities, respectively. Note that the long-range interaction stage does not lead to activities beyond contour terminations at the corners of the square which would mistakenly lead to turn L-junctions into X-junction. Extraction of junction configurations In a second simulation, we used a noise-free image of four occluding transparent and opaque squares as input for the model (Figure 9a). The stimulus includes all three types of junction configurations and is therefore a good example to demonstrate the capabilities of the model at a glance. Initial feedforward responses from simple and complex cells shown in Figure 9b and Figure 9c demonstrate that these responses are not invariant to luminance contrast. Furthermore, they are not robust against noise (Figure 8). The results described in the following correspond to model activities after four recurrent feedforward-feedback cycles where model activities tended to converge to a stable state. The upper sub-area of model V1 is represented by bipole cells and end-stop cells. End-stop cells respond at positions where contours meet (e.g. T-, and L- junctions) or at positions where a contour ends (Figure 4). However, they do not respond at X-junction configurations as can be observed in Figure 9e. Model V1 bipole cells are responsive for contours. They connect short like-oriented fragments and equalize contrast changes along the contour. At contour endings their activity is reduced (not shown) since they have additively connected subfield (one subfield is still activated, see Figure 4). The additive combination of V1 bipole and perpendicular oriented end-stop cells compensates for the reduction effect at corners or T-junctions. Figure 9d show that contour activity is not reduced at corners or T-junctions. The combined bipole end end- stop activities from model area V1 are further processed by V2 bipole cells which are as well responsive for contours. However, as a result of their multiplicatively connected subfield these cells do not respond at contour endings. Consequently, V2 cell responses are zero at T- and L-junctions, but not at X-junctions (Figure 9D, Figure 4). In Figure 10, T-,L-, and X-junction maps were extracted and visualized based on converged model activities. In each map, the presence of the specific feature is signaled by patches of high PLoS ONE | www.plosone.org 9 June 2009 | Volume 4 | Issue 6 | e5909