Figure 6. Extraction of junction signals. The figure depicts how local activity from model cells is combined to obtain specific junction maps. To extract a T-junction signal orientation specific V2 bipole cell activities are combined pairwisely with orientation specific V1 end-stop cell activities. Pairs of cells that correspond to same orientations are not considered (diagonal marked with ‘‘X’’). The combination of cell activities is done multiplicatively such that both cells have to be active in order to produce a response. This can be represented in a map where each entry corresponds to a specific T-junction configuration. X-, and L- junction maps are extracted similarly except that pairwise combination is based on V2 bipole cells (X-junctions) and V1 end-stop cells (L junctions). To summarize, the maps represent information about local image structure with respect to different junction types. Note, that end- stop cells are only indicated for one direction in the figure. However, end-stop cell responses of both directions that correspond to one orientation are additively combined for the extraction of junction maps. doi:10.1371/journal.pone.0005909.g006 Surface-Related Features extracted by integrating over all combinations. Corners are important cues for shape perception as they mark keypoints of the boundary contour. Importantly, an L-junction can also result from two occluding surfaces, assuming that one of the surfaces is partly formed by illusory contours [1]. Under such circumstances, an L-junction feature would as well suggest for occlusions. We shall show that our model initially detects an L-junction in this case, but over time when groupings could be established, then these types turn into perceptual T-junctions (irrespectively whether the boundaries are formed by physical luminance contrasts or by illusory contours). The perceptual representation of T-junctions in turn signals the presence of an occluding surface which is consistent with the impression that human observers report when they are confronted with illusory figures, such as Kanizsa squares [40]. X-junctions. X-junctions configurations appear at positions where two contours intersect. In scenes with overlapping surfaces, this pattern is created when an occluding surface has transparent material properties leading to a visibility of the occluded surface region through the surface at the occluded surface contour. Therefore, in the transparent occlusion situation, a T-junction turns into an X-junction. In our model, X-junctions are read-out by multiplicatively combining activities of pairs of differently oriented V2 bipole cells (Figure 6). The saliency map is obtained by summing over the orientation domain. Again, the multiplicative connection acts like an AND-gate which extracts only those V2 bipole responses that have a bimodal activity distribution in the orientation domain. Y- and W-junctions. Y- and W-junctions are strong cues for 3D-corners induced by surface intersections of 3D objects which cut in a single location. For instance, a cube produces a Y-junction at the position where the corners of three visible surfaces meet. The same corner observed from another viewpoint turns into a W- junction. Notably, in rare cases, such junctions can be also produced by occluding 2D surfaces when a contour of an occluded surface meets a shape-based L-junction of an occluding surface. However, this can be seen as an ‘accidental’ and rather unstable configuration since even small changes in viewpoint would lead to vanishing of such occlusion-based junctions. Since the perception of 3D objects is not our primary focus in this contribution, we do not take Y- and W-junctions into further consideration. Competition between junction signals. In order to suppress ambiguous activations for more than one junction type at the same place, junction signals compete with each other through lateral inhibition (Figure 7). If one junction type is activated the other junction signals in a local neighborhood are weakened. Finally, all junction activities are passed through a non- linear saturation function in order to have the same range for all activity signals. Note, that although we use a similar inhibition scheme than for the model activities we do not claim that this kind of competition has a biological counterpart. This is just a necessary operation to disambiguate feature signals and has no biological relevance. Results In this section we present results of the model simulations in order to demonstrate the computational capabilities of the proposed model. We begin by presenting model results from the various neural cell pools that are simulated in the model. We also show how recurrent feedforward-feedback interaction helps to enhance and stabilize the initial responses of cells. Then we show that various feature maps can be extracted from the distributed cell activities suggesting that this representation is capable of PLoS ONE | www.plosone.org 8 June 2009 | Volume 4 | Issue 6 | e5909