Surface-Related Features Figure 3. Our model simulates cells of two areas in the visual cortex, visual areas V1 and V2. Each model (sub-) area is designed with respect to a basic building block scheme (bottom, left). The scheme consists of three subsequent steps, namely filtering, modulation and centre- surround inhibition. This scheme is applied three times in our model architecture (left), corresponding to upper and lower area V1 and area V2. In this model, modulatory input (provided by feedback from area V2) is only used in lower area V1. Otherwise the default modulatory input is set to 1 (which leaves the signal unchanged). The lower part of area V1 is modelled by simple and complex cells for initial contrast extraction. Note, that each cell pool consists of 12 oriented filters equally distributed between 0u and 180u. The upper part of V1 is modelled by end-stop and bipole cells which both receive input from lower V1. The additively combined signals are further passed to area V2 where long-range lateral connections are modelled by V2 bipole cells. Note, that ‘‘N’’ stands for a multiplicative connection of filter subfields as employed in V2 whereas ‘‘#’’ stands for an additive connection as employed in V1. Finally output of area V2 is used as feedback signal which closes the recurrent loop between areas V1 and V2. doi:10.1371/journal.pone.0005909.g003 contour. Such a mechanism of ortho-grouping has been proposed earlier by Heitger and colleagues [8]. Recurrent V1-V2 feedforward-feedback interaction. In our model, lower area V1 and area V2 interact in two directions, that is feedforward and feedback. Feedforward interaction is realized by feeding bottom-up input activation from model V1 to V2 and was described in detail in the last sub-section. On the other hand, feedback interaction is realized by top-down modulatory feedback connections that deliver signals from model V2 to V1. The recurrent loop is closed at the feedback re-entry point in V1 where initial feedforward complex cell responses and V2 bipole cell responses are multiplicatively combined (Figure 3). In order to allow feeding input signals to be propagated, even in the case that no feedback signals exist, the feedback signal is biased by a constant unit value. This bias introduces an asymmetry for the roles of forward signals and feedback processing. Feedforward signals act as drivers of the hierarchical processing scheme, whereas feedback signals generate an enhancing gain factor which cannot on its own generate any activity at positions where no initial feeding input responses are present. This realizes a variant of the no-strong loop hypothesis [34] to avoid uncontrolled behavior of the overall model dynamics and to limit the amount of inhibition necessary to achieve a stable network performance. Several physiological studies support the view that, e.g., feedback from higher visual areas is not capable of driving cells in lower areas, but modulates their activity [12,15]. It is important to mention that modulatory feedback in a recurrent loop only works correctly in combination with a suitable inhibition mechanism. Otherwise, the feedback signal would lead to uncontrolled growth of model cell activities. Thus, both mechanisms, the modulatory V2 R V1 feedback interaction and the subsequent shunting lateral inhibition work in combination in order to enhance distributed contour and junction representations in model V1 and V2 which mutually support each other considering a larger spatial context. At the same time, mainly through the action of the divisive inhibition mechanism, the overall activity in a pool of cells is kept within a maximum bound which stabilizes the network behavior and prevents the energy from getting too excited. In addition, those feeding activities that receive no amplification via feedback signals will be less energetic in the subsequent competition stage. Consequently, their activities will be finally reduced, which realizes the function of biased competition which has been proposed in the context of modulation in attention effects [35]. Read-out and interpretation of model activities From the distributed representation of cell responses in both model areas V1 and V2 several retinotopic maps can be extracted that signal perceptually relevant contour configurations. If not mentioned otherwise, these maps are extracted by computing at each position the mean activity of all orientation responses. An alternative method for reading out salience values was suggested by Li [36], who choose to extract at each position the maximum activity over all orientations. In the following, we describe in detail how saliency maps for specific image structures, namely corners and junctions can be extracted by combining activities from different model cells pools. In this paper, we define saliency maps as 2d maps that encode at each position the likelihood that a specific structure is present. A more broad discussion on the concept of salience and salience maps can be found in [37]. In Figure 4 the structural configurations are sketched to present an overview of the output as signaled by the different orientation sensitive mechanisms of the proposed model. This summary indicates how the different visual structures of surface shape outlines and their ordinal depth structure might be selectively encoded neurally through the concert of responses generated by different (model) cell types. The conclusions are two-fold. First, it is indicated that the presence of, e.g., a T-junction (which most often coheres with an opaque surface occlusion [1]) is uniquely indicated by the response pattern of V1 and V2 cells at one spatial location. The T-junction is represented by an end-stop cell response at the end of the T-stem, V1 bipole cell responses in the orientations of both the T-stem (signaled by one active sub-field) and the roof, and finally a V2 bipole cell response in the orientation of the roof of the T (representing the occluding boundary). Second, we argue in favor that no explicit detectors are needed to represent those local 2D structures. Figure 4 indicates that the explicit representation of different junction types necessitates a rich catalogue of cells with rather specific wiring patterns. Below we propose specific read-out mechanisms in order to visualize the information we suggest is important for surface-related analysis of the input structure. Contours/Illusory contours. Contours are basic image structures which are important for the segmentation of surfaces by generating a likelihood representation of the locations and orientations of the shape outline boundaries. Furthermore, contours mark the border between two adjacent surfaces and play a major role in the process of figure-ground segregation and border-ownership [37,38,39]. In our model, contour saliency is encoded in the response of V1 bipole cells. The contour saliency map can be extracted by summing activity of orientation selective bipole cells pools in V1. Illusory contours are a form of visual illusion where contours are perceived without a luminance change across the contour. Classical examples are the Kanizsa figure [40] where an illusory square is induced by four flanking pac-man symbols or the Varin figure [41] where linearly arranged line-ends mark the borders of the illusory square (Figure 5). There is evidence that illusory contours are represented by V2 neurons [31]. In our model, illusory contours are signaled by activity of V2 bipole cells. T-junctions. A T-junction is formed when a contour terminates at a differently oriented continuous contour. T- junctions most often provide local evidence for occlusions as they frequently occur when a surface contour is occluded by another opaque surface in front. At the point where the bounding contour of the occluded surface intersects with the bounding contour of the occluding surface a T-junctions is formed in the image which is dependent on the position of the viewpoint. It has been suggested by Rubin [1] that T-junctions play a central role in monocular depth perception, surface completion, and contour PLoS ONE | www.plosone.org 6 June 2009 | Volume 4 | Issue 6 | e5909