averaging. Differences to our model are that they do no incorporate feedback mechanisms and that they do not consider illusory contour extraction as they do not model V2 neurons. Interestingly, only few computational models of contour grouping address the computation and representation of corners and junctions. The model proposed by Heitger et al. [8] is closely related to our model because they use several elements that we incorporated into our model (e.g. complex cells, end-stop and bipole operators). A key element of their model is the concept of ortho- and para-grouping to generate illusory contour representa- tions. Ortho grouping applies to terminations of the background, which tend to be orthogonal to the occluding contour. Para grouping applies to discontinuities of the foreground and is used to interpolate the contour in the direction of termination. However, a major shortcoming of their model is that it relies on a purely feedforward scheme which would presumably produce erroneous results when given a degraded input image (e.g. by noisy or low contrast). This also contrasts with the findings of several authors [1,12,15,37,53] that feedback and recurrent interactions play an important role in visual processing for figure-ground segregation. The model of Hansen and Neumann [54] is also closely related to our model since it is based on the same biological principles such as modulatory feedback and long-range interactions for the extraction of corners and junctions. However, the model is restricted to interlaminar interactions in V1 to explain contrast detection and subsequent enhancement effects. Our model, on the other hand, incorporates several extensions. First, our model takes illusory contours into account by additionally modeling area V2. Second, we show that our neuronal representation is further processed to extract different junction types such as L, T- and X- junctions which are perceptually important features that provide basic cues for global scene interpretations. Recently, a recurrent model for surface-based depth processing was proposed by Thielscher and Neumann [30]. In their proposed model, depth information derived from monocular cues is propagated along surface contours using local recurrent interac- tions to obtain a globally consistent depth sorting of overlapping surfaces. The model differs in several aspects from our model. In contrast to our model, they use additional recurrent interactions in V2 to propagate border-ownership information derived from detected T-junctions along contours. This propagated information enables them to obtain a globally consistent interpretation of depth relations between surfaces. Unlike this approach to monocular depth segregation, we focused on the extraction and perceptual interpretation of junction configurations. In summary, variations of mechanisms employed in our model can be found in several other models of visual processing. But only few of them have concerned for combined boundary extraction and junction extraction as well as their distinction. Unlike previous proposals which treat localized junction configurations as 2D image features [4,5], we link them to mechanisms of apparent surface segregation. As a consequence, we demonstrate how junctions can change their perceptual representation depending on the scene context and the spatial configuration of boundary fragments. Biological Plausibility Our model architecture is inspired by biological mechanisms and is based on neural representations of early visual cortex. We now put individual model components into a physiological or psychophysical context and discuss for their plausibility. Biological plausibility of model components. In the initial stages of our model we simulate V1 simple and complex cells [7]. Model V1 bipole cells are inspired by horizontal long- range connections that link patches of neurons of similar Surface-Related Features orientation preference [23,25]. Consistently, model V1 bipole cells pool activities of appropriately aligned complex cells from the lower part of model area V1 (Figure 3) which resembles intracortical layer 4 of area V1. Evidence for nonlocal integration also comes from psychophysical experiments for contrast detection [55] and contour integration [56]. Additionally, we model end-stop cells that selectively respond to contour terminations. The existence of V1 neurons reacting to end-stop configurations has been confirmed by several electrophysiological studies [26,57,58]. As a consequence, end- stop cells were also modeled by several authors in the context of contour integration [30,51,59]. In model area V2, we employ modified bipole cells with nonlinear response properties. As V2 neurons have larger receptive fields than V1 neurons, our bipole filters employed in model area V2 have a larger extent that those used in the upper part of model area V1 (Figure 3). Evidence for contour selective cells in V2 comes from von der Heydt [9] where the authors probed V2 neurons with illusory-bar stimuli. They selectively respond to coherent arrangements having both fragments of an illusory bar intact. If one fragment is missing, the cell response drops to spontaneous activity [51]. To be consistent with these findings we modeled V2 bipole cells with multiplicatively connected sub-fields which leads to similar effects than those reported by von der Heydt. Evidence for representation of junctions and corners in visual cortex. Although it seems obvious that junctions play a crucial role in several perceptual processes [1] only little evidence was found that specific cells in the visual cortex are particularly responsive to junction features. Several studies suggest the presence of a neural organization in V1 that may represent a mechanism for detecting local orientation discontinuity [60,61,62]. Their results indicate a facilitory interaction between elements of V1 circuitry representing markedly different orientations in contradiction to the common believe that functional connectivity is only seen between cells of like orientation [63]. However, it is still unclear to what extent this selectivity is used for junction processing. In a study by Kobatake and Tanaka [64] critical features for the activation of cells reaching from V2, V4 up to posterior inferotemporal cortex (IT) were determined. V2 cells were found to react to stimuli such as concentric rings or tapered bars. Cells that respond selectively to junction-like features like crosses were only found in V4 and posterior IT. More recently, two studies [32,65] report on cells in monkey visual area V2 that seem to explicitly encode combinations of orientations as represented by junctions or corners. Thus, such V2 neurons may provide important underpinnings for the analysis of surfaces [2]. In a straightforward model approach it was shown that these V2 neurons may simply sum the responses from orientation selective V1 neurons [66]. However, the fact that only little evidence exists for junction selective cells in V2 could also motivate the hypothesis that junctions are not explicitly encoded by specific cells in V2 but higher visual areas such as V4 or IT link responses from cell types selective for lower-level features, such as complex, end-stop, and bipole cells. Thus, the extraction of junction signals from combinations of model cell responses, described as read-out process in our model, follows the idea mentioned above, that, e.g., V4 cells could pool signals from several cortical areas, particularly from V1 and V2. Notably, we do not claim that junction signals are encoded by V4 or IT neurons, but we demonstrate that our model performs well assuming that junctions are processed from distributed activities of neurons at early cortical stages. PLoS ONE | www.plosone.org 18 June 2009 | Volume 4 | Issue 6 | e5909