capable of generating new activity at positions with zero initial activity which could lead to an uncontrolled behavior of the overall system’s functionality. Feedback can only modulate activity that is already present at V1 [12]. We shall demonstrate in our results that multiple iterations of feedforward-feedback processing between model areas V1 and V2 lead to clearly more consistent and stable results compared to purely feedforward processing schemes. The third component of the model comprises the extraction or ‘‘read-out’’ of scene relevant information that is provided by different pools of cells within the two model areas V1 and V2. Figure 2 presents an overview of the different types of mid-level features that can be extracted from the distributed representation of cell responses. This includes the extraction of several maps that signal contours, illusory contours, and keypoints characterized by different junction configurations. It has been stressed by several authors that specific junction configurations like T- or X-junctions provide important cues for the discovery of occlusions or transparency [1] in the context of surface segmentation. Therefore, we suggest that the visual system uses specialized mechanisms to read out separate maps for such configurations. It is important to note that our model is not only a simple ‘‘keypoint detector’’. In fact, our model additionally provides structural information about keypoints represented by activities of model cell pools located at the keypoint. Detailed description of model components In this section, we explain the individual model parts in more detail. For a precise mathematical description of the model and its different processing stages the reader is referred to Appendix S1. The detailed model architecture is illustrated in Figure 3. Model area V1. The initial feedforward stage represents early visual mechanisms in area V1 and V2 of the primate visual cortex. We do not simulate processing at earlier stages of the visual pathway such as LGN or the retina since this would not considerably influence our results. In our model, a first step is to simulate pools of cells that encode at each position of the input image oriented luminance contrasts which are represented by V1 simple cells in the primary visual cortex [7]. Each model cell represents the average responses (or firing-rate) of groups of neurons with similar response properties. We simulate two different types of simple cells with even and odd symmetrical receptive field (RF) properties. These orientation selective cells respond best to oriented line segments or edges, respectively. Simple cells are also selective to contrast polarity, such that they signal light and dark bars as well as light-dark and dark-light transitions. We do not, however, keep this information separate but combine this information to yield a representation of the local contrast energy, which is invariant against the sign of contrast. This is motivated by the fact that the model tries to explain computational stages to form unsigned boundaries, and thus should be invariant to contrast polarity [17]. In addition, such a convergence of activity coheres with current models of hierarchical feedforward processing of binocular input of disparity sensitive cells in primary visual cortex [18]. V1 complex cells pool activity of two equally oriented V1 simple cells of opposite polarity. Thus, complex cell activity is invariant to contrast polarity which resembles response properties of real complex cells. The output signals of model complex cells subsequently undergo a centre-surround inhibition that is realized by a lateral divisive inhibition mechanism. In the literature, this divisive type of lateral inhibition is also termed shunting inhibition [19]. This mechanism leads to a competition of cell activities within a neighborhood that is defined over the spatial and orientation domain. High activity of multiple orientations leads to a suppression of overall cell activity whereas activity in a single Surface-Related Features orientation channel leaves cell activity relatively unchanged. As a consequence, responses in areas with undirected structure such as textured or noisy areas are weakened by this operation. On the other hand, responses in areas with directed structures, such as edges and lines are strengthened by this operation. Such a stage of divisive inhibition has been previously proposed to account for non- linear effects in contrast and motion responses of V1 cells [20,21,22]. In the next step of the hierarchical processing scheme two different populations of cells receive forward projections from V1 complex cells. The first population of model cells resembles long-range lateral connections found in V1 [23,24]. These long- range connections are modeled by V1 bipole cells which consist of two additively connected elongated Gaussian subfields. The spatial layout of the filter is similar to the bipole filter as first proposed by [17]. The spatial weighting function is narrowly tuned to the preferred orientation, reflecting the highly significant anisotropies of long-range fibers in visual cortex [25]. Here, we parameterize the length of a V1 bipole cell about 2 times the size of the RF of a complex cell. The second population receiving input from complex cells are V1 end-stop cells. End-stop cells respond to edges or lines that terminate within their RF. This includes also corners or junctions where more than one contour ends at the same place. However, at positions along contours or at X-junction configurations, end-stop cells do not respond. Such types of cells have been first observed in cat striate cortex [26]. More recently, evidence for end-stop cell properties of V1 neurons was found in several physiological studies [27,28,29]. In our model, end-stop cells are modeled by an elongated excitatory subfield and an inhibitory isotropic counterpart [30]. Our model end-stop cells are direction sensitive and are therefore modeled for a set of directions between 0 and 360 degrees. Activities of end-stop cells corresponding to opposite directions are additively combined in order to achieve direction invariance. Finally, at the output of model area V1 activities from V1 bipole and V1 end-stop cells are merged and normalized by a centre-surround inhibition stage before they are forwarded to model area V2. Model area V2. Visual area V2 is the next stage in the hierarchy of processing stages along the ventral stream. Several physiological studies on macaque monkeys have shown that cells in V2 respond to luminance contrasts as well as to illusory contours [8,31]. In contrast to complex cells in V1 they respond much stronger if the luminance contrast is continuous, and less if gaps are between inducers, thus resembling functional properties of contour neurons in V2. Moreover, they respond to moderately complex patterns such as angle stimuli [32]. However, the precise functional role of area V2 remains unclear. In our model, we employ V2 bipole cells with elongated sub-fields which are collinearly arranged and centered at the reference position. The sub-fields sample the input activations generated by V1 bipole cells and V1 end-stop cells. Responses of the individual sub-fields are multiplicatively combined [33] such that the net effect of the contrast feature integration leads to an AND-gate of the individual sub-field activations. Thus, activity from both sub-fields of an integration cell is necessary to generate cell activity. This non- linear connection has the effect that activity can emerge between two or more like-oriented contour-fragments or line ends at positions where no initial luminance contrast is present which is indicative for the presence of an illusory contour. At the same time, activity of a V2 bipole cell at an isolated contour termination would be zero as one subfield does not receive any input. V2 bipole cells are additively combined with perpendicular oriented V1 end-stop cells. This has the effect that V2 bipole cells can integrate activity of end-stop cells along line terminations that are linearly arranged, which leads to the impression of an illusory PLoS ONE | www.plosone.org 4 June 2009 | Volume 4 | Issue 6 | e5909