Evidence suggests that the brain uses an operational set of canonical computations like normalization, input filtering, and response gain enhancement via reentrant feedback. Here, we propose a three-stage columnar architecture of cascaded model neurons to describe a core circuit combining signal pathways of feedforward and feedback processing and the inhibitory pooling of neurons to normalize the activity. We present an analytical investigation of such a circuit by first reducing its detail through the lumping of initial feedforward response filtering and reentrant modulating signal amplification. The resulting excitatory-inhibitory pair of neurons is analyzed in a 2D phase-space. The inhibitory pool activation is treated as a separate mechanism exhibiting different effects. We analyze subtractive as well as divisive (shunting) interaction to implement center-surround mechanisms that include normalization effects in the characteristics of real neurons. Different variants of a core model architecture are derived and analyzed—in particular, individual excitatory neurons (without pool inhibition), the interaction with an inhibitory subtractive or divisive (i.e., shunting) pool, and the dynamics of recurrent self-excitation combined with divisive inhibition. The stability and existence properties of these model instances are characterized, which serve as guidelines to adjust these properties through proper model parameterization. The significance of the derived results is demonstrated by theoretical predictions of response behaviors in the case of multiple interacting hypercolumns in a single and in multiple feature dimensions. In numerical simulations, we confirm these predictions and provide some explanations for different neural computational properties. Among those, we consider orientation contrast-dependent response behavior, different forms of attentional modulation, contrast element grouping, and the dynamic adaptation of the silent surround in extraclassical receptive field configurations, using only slight variations of the same core reference model.

Visual navigation requires the estimation of self-motion as well as the segmentation of objects from the background. We suggest a definition of local velocity gradients to compute types of self-motion, segment objects, and compute local properties of optical flow fields, such as divergence, curl, and shear. Such velocity gradients are computed as velocity differences measured locally tangent and normal to the direction of flow. Then these differences are rotated according to the local direction of flow to achieve independence of that direction. We propose a bio-inspired model for the computation of these velocity gradients for video sequences. Simulation results show that local gradients encode ordinal surface depth, assuming self-motion in a rigid scene or object motions in a nonrigid scene. For translational self-motion velocity, gradients can be used to distinguish between static and moving objects. The information about ordinal surface depth and self-motion can help steering control for visual navigation.

Motion transparency occurs when multiple coherent motions are perceived in one spatial location. Imagine, for instance, looking out of the window of a bus on a bright day, where the world outside the window is passing by and movements of passengers inside the bus are reflected in the window. The overlay of both motions at the window leads to motion transparency, which is challenging to process. Noisy and ambiguous motion signals can be reduced using a competition mechanism for all encoded motions in one spatial location. Such a competition, however, leads to the suppression of multiple peak responses that encode different motions, as only the strongest response tends to survive. As a solution, we suggest a local center-surround competition for population-encoded motion directions and speeds. Similar motions are supported, and dissimilar ones are separated, by representing them as multiple activations, which occurs in the case of motion transparency. Psychophysical findings, such as motion attraction and repulsion for motion transparency displays, can be explained by this local competition. Besides this local competition mechanism, we show that feedback signals improve the processing of motion transparency. A discrimination task for transparent versus opaque motion is simulated, where motion transparency is generated by superimposing large field motion patterns of either varying size or varying coherence of motion. The model’s perceptual thresholds with and without feedback are calculated. We demonstrate that initially weak peak responses can be enhanced and stabilized through modulatory feedback signals from higher stages of processing.

Motion of an extended boundary can be measured locally by neurons only orthogonal to its orientation (aperture problem) while this ambiguity is resolved for localized image features, such as corners or nonocclusion junctions. The integration of local motion signals sampled along the outline of a moving form reveals the object velocity. We propose a new model of V1-MT feedforward and feedback processing in which localized V1 motion signals are integrated along the feedforward path by model MT cells. Top-down feedback from MT cells in turn emphasizes model V1 motion activities of matching velocity by excitatory modulation and thus realizes an attentional gating mechanism. The model dynamics implement a guided filling-in process to disambiguate motion signals through biased on-center, off-surround competition. Our model makes predictions concerning the time course of cells in area MT and V1 and the disambiguation process of activity patterns in these areas and serves as a means to link physiological mechanisms with perceptual behavior. We further demonstrate that our model also successfully processes natural image sequences.

Junctions provide important cues in various perceptual tasks, such as the determination of occlusion relationships for figure-ground separation, transparency perception, and object recognition, among others. In computer vision, junctions are used in a number of tasks, like point matching for image tracking or correspondence analysis. We propose a biologically motivated approach to junction representation in which junctions are implicitly characterized by high activity for multiple orientations within a cortical hypercolumn. A local measure of circular variance is suggested to extract junction points from this distributed representation. Initial orientation measurements are often fragmented and noisy. A coherent contour representation can be generated by a model of V1 utilizing mechanisms of collinear long-range integration and recurrent interaction. In the model, local oriented contrast estimates that are consistent within a more global context are enhanced while inconsistent activities are suppressed. In a series of computational experiments, we compare junction detection based on the new recurrent model with a feedforward model of complex cells. We show that localization accuracy and positive correctness in the detection of generic junction configurations such as L- and T-junctions is improved by the recurrent long-range interaction. Further, receiver operating characteristics analysis is used to evaluate the detection performance on both synthetic and camera images, showing the superior performance of the new approach. Overall, we propose that nonlocal interactions implemented by known mechanisms within V1 play an important role in detecting higher-order features such as corners and junctions.