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Freezing is an effective defense strategy for some prey, because their predators rely on visual motion to distinguish objects from their surroundings. An object moving over a background progressively covers (deletes) and uncovers (accretes) background texture while simultaneously producing discontinuities in the optic flow field. These events unambiguously specify kinetic occlusion and can produce a crisp edge, depth perception, and figure-ground segmentation between identically textured surfaces - percepts which all disappear without motion. Given two abutting regions of uniform random texture with different motion velocities, one region appears to be situated farther away and behind the other (i.e., the ground) if its texture is accreted or deleted at the boundary between the regions, irrespective of region and boundary velocities. Consequently, a region with moving texture appears farther away than a stationary region if the boundary is stationary, but it appears closer (i.e., the figure) if the boundary is moving coherently with the moving texture. A computational model of visual areas V1 and V2 shows how interactions between orientation- and direction-selective cells first create a motion-defined boundary and then signal kinetic occlusion at that boundary. Activation of model occlusion detectors tuned to a particular velocity results in the model assigning the adjacent surface with a matching velocity to the far depth. A weak speed-depth bias brings faster-moving texture regions forward in depth in the absence of occlusion (shearing motion). These processes together reproduce human psychophysical reports of depth ordering for key cases of kinetic occlusion displays.

How are spatial and object attention coordinated to achieve rapid object learning and recognition during eye movement search? How do prefrontal priming and parietal spatial mechanisms interact to determine the reaction time costs of intra-object attention shifts, inter-object attention shifts, and shifts between visible objects and covertly cued locations? What factors underlie individual differences in the timing and frequency of such attentional shifts? How do transient and sustained spatial attentional mechanisms work and interact? How can volition, mediated via the basal ganglia, influence the span of spatial attention? A neural model is developed of how spatial attention in the where cortical stream coordinates view-invariant object category learning in the what cortical stream under free viewing conditions. The model simulates psychological data about the dynamics of covert attention priming and switching requiring multifocal attention without eye movements. The model predicts how “attentional shrouds” are formed when surface representations in cortical area V4 resonate with spatial attention in posterior parietal cortex (PPC) and prefrontal cortex (PFC), while shrouds compete among themselves for dominance. Winning shrouds support invariant object category learning, and active surface-shroud resonances support conscious surface perception and recognition. Attentive competition between multiple objects and cues simulates reaction-time data from the two-object cueing paradigm. The relative strength of sustained surface-driven and fast-transient motion-driven spatial attention controls individual differences in reaction time for invalid cues. Competition between surface-driven attentional shrouds controls individual differences in detection rate of peripheral targets in useful-field-of-view tasks. The model proposes how the strength of competition can be mediated, though learning or momentary changes in volition, by the basal ganglia. A new explanation of crowding shows how the cortical magnification factor, among other variables, can cause multiple object surfaces to share a single surface-shroud resonance, thereby preventing recognition of the individual objects.

Information from the vestibular, sensorimotor, or visual systems can affect the firing of grid cells recorded in entorhinal cortex of rats. Optic flow provides information about the rat's linear and rotational velocity and, thus, could influence the firing pattern of grid cells. To investigate this possible link, we model parts of the rat's visual system and analyze their capability in estimating linear and rotational velocity. In our model a rat is simulated to move along trajectories recorded from rat's foraging on a circular ground platform. Thus, we preserve the intrinsic statistics of real rats' movements. Visual image motion is analytically computed for a spherical camera model and superimposed with noise in order to model the optic flow that would be available to the rat. This optic flow is fed into a template model to estimate the rat's linear and rotational velocities, which in turn are fed into an oscillatory interference model of grid cell firing. Grid scores are reported while altering the flow noise, tilt angle of the optical axis with respect to the ground, the number of flow templates, and the frequency used in the oscillatory interference model. Activity patterns are compatible with those of grid cells, suggesting that optic flow can contribute to their firing.

In a synchronous digital platform for building large cognitive models, memristive nanodevices form dense, resistive memories that can be placed close to conventional processing circuitry. Through adaptive transformations, the devices can interact with the world in real time.

CONFIGR (CONtour FIgure GRound) is a computational model based on principles of biological vision that completes sparse and noisy image figures. Within an integrated vision/recognition system, CONFIGR posits an initial recognition stage which identifies figure pixels from spatially local input information. The resulting, and typically incomplete, figure is fed back to the ''early vision'' stage for long-range completion via filling-in. The reconstructed image is then re-presented to the recognition system for global functions such as object recognition. In the CONFIGR algorithm, the smallest independent image unit is the visible pixel, whose size defines a computational spatial scale. Once the pixel size is fixed, the entire algorithm is fully determined, with no additional parameter choices. Multi-scale simulations illustrate the vision/recognition system. Open-source CONFIGR code is available online, but all examples can be derived analytically, and the design principles applied at each step are transparent. The model balances filling-in as figure against complementary filling-in as ground, which blocks spurious figure completions. Lobe computations occur on a subpixel spatial scale. Originally designed to fill-in missing contours in an incomplete image such as a dashed line, the same CONFIGR system connects and segments sparse dots, and unifies occluded objects from pieces locally identified as figure in the initial recognition stage. The model self-scales its completion distances, filling-in across gaps of any length, where unimpeded, while limiting connections among dense image-figure pixel groups that already have intrinsic form. Long-range image completion promises to play an important role in adaptive processors that reconstruct images from highly compressed video and still camera images.

Surfaces of environmental objects are the key to understanding the visual experience of primates. Surfaces create structure in patterns of light available for sampling by visual systems, and delineate potential interactions that an animal can have with its environment, such as approaching goals, avoiding obstacles, grasping an object, or identifying members of a social group. Recent progress in modeling the perception of visual surfaces highlights the importance of feedforward and feedback connections in visual neural networks that segregate and group visual input into coherent regions related to corresponding surfaces in the visual world. Rich non-linear network dynamics in the brain underlie surface perception, including the detection, regularization, and grouping of visual boundaries between surfaces, the determination of “ownership” of a boundary by a closer surface that partially occludes a background, and the apprehension of a surface's visual quality, such as color or texture. Recent modeling efforts on these fronts are reviewed.

A neural model is presented of how cortical areas V1, V2, and V4 interact to convert a textured 2D image into a representation of curved 3D shape. Two basic problems are solved to achieve this: (1) Patterns of spatially discrete 2D texture elements are transformed into a spatially smooth surface representation of 3D shape. (2) Changes in the statistical properties of texture elements across space induce the perceived 3D shape of this surface representation. This is achieved in the model through multiple-scale filtering of a 2D image, followed by a cooperative-competitive grouping network that coherently binds texture elements into boundary webs at the appropriate depths using a scale-to-depth map and a subsequent depth competition stage. These boundary webs then gate filling-in of surface lightness signals in order to form a smooth 3D surface percept. The model quantitatively simulates challenging psychophysical data about perception of prolate ellipsoids [Todd, J., & Akerstrom, R. (1987). Perception of three-dimensional form from patterns of optical texture. Journal of Experimental Psychology: Human Perception and Performance, 13(2), 242–255]. In particular, the model represents a high degree of 3D curvature for a certain class of images, all of whose texture elements have the same degree of optical compression, in accordance with percepts of human observers. Simulations of 3D percepts of an elliptical cylinder, a slanted plane, and a photo of a golf ball are also presented.

Heavily occluded objects are more difficult for classification algorithms to identify correctly than unoccluded objects. This effect is rare and thus hard to measure with datasets like ImageNet and PASCAL VOC, however, owing to biases in human-generated image pose selection. We introduce a dataset that emphasizes occlusion and additions to a standard convolutional neural network aimed at increasing invariance to occlusion. An unmodified convolutional neural network trained and tested on the new dataset rapidly degrades to chance-level accuracy as occlusion increases. Training with occluded data slows this decline but still yields poor performance with high occlusion. Integrating novel preprocessing stages to segment the input and inpaint occlusions is an effective mitigation. A convolutional network so modified is nearly as effective with more than 81% of pixels occluded as it is with no occlusion. Such a network is also more accurate on unoccluded images than an otherwise identical network that has been trained with only unoccluded images. These results depend on successful segmentation. The occlusions in our dataset are deliberately easy to segment from the figure and background. Achieving similar results on a more challenging dataset would require finding a method to split figure, background, and occluding pixels in the input.

The primate brain intelligently processes visual information from the world as the eyes move constantly. The brain must take into account visual motion induced by eye movements, so that visual information about the outside world can be recovered. Certain neurons in the dorsal part of monkey medial superior temporal area (MSTd) play an important role in integrating information about eye movements and visual motion. When a monkey tracks a moving target with its eyes, these neurons respond to visual motion as well as to smooth pursuit eye movements. Furthermore, the responses of some MSTd neurons to the motion of objects in the world are very similar during pursuit and during fixation, even though the visual information on the retina is altered by the pursuit eye movement. We call these neurons compensatory pursuit neurons. In this study we develop a computational model of MSTd compensatory pursuit neurons based on physiological data from single unit studies. Our model MSTd neurons can simulate the velocity tuning of monkey MSTd neurons. The model MSTd neurons also show the pursuit compensation property. We find that pursuit compensation can be achieved by divisive interaction between signals coding eye movements and signals coding visual motion. The model generates two implications that can be tested in future experiments: (1) compensatory pursuit neurons in MSTd should have the same direction preference for pursuit and retinal visual motion; (2) there should be non-compensatory pursuit neurons that show opposite preferred directions of pursuit and retinal visual motion.

The perception of a glossy surface in a static monochromatic image can occur when a bright highlight is embedded in a compatible context of shading and a bounding contour. Some images naturally give rise to the impression that a surface has a uniform reflectance, characteristic of a shiny object, even though the highlight may only cover a small portion of the surface. Nonetheless, an observer's impression of gloss may be partial and nonuniform at image regions outside of a highlight. A rating scale and small probe points indicating image locations were used to investigate the differential perception of gloss within a single object. Gloss ratings given by observers were not uniform across a surface, but decreased as a function of distance from a highlight. When, by design, the distance from a highlight was uncoupled from the luminance value at corresponding probe points, the decrease in rated gloss correlated more with distance than with luminance change. Experiments also indicated that gloss ratings may change as a function of estimated surface distance, rather than as a function of image distance. Surface continuity affected gloss ratings, suggesting that surface and gloss processing are closely related.

Visible surfaces in a natural environment often have multiple components of reflectance, including a diffuse component, by which light is scattered isotropically in all possible directions, and a specular component, by which light is reflected anisotropically within a limited range of directions. The research described in the present article was designed to investigate how these different components of reflectance influence the perception of lightness. Human observers were presented with shaded images of smoothly curved surfaces and asked to compare the relative lightness of different surface regions whose diffuse and specular components of luminance were independently manipulated. The results revealed that observers are able to discount the presence of specular highlights so that the relative lightness among different regions is determined almost entirely by the diffuse component of surface reflectance.

We examined the allocation of attention in depth using a multi-element tracking paradigm. Observers were required to track a predefined subset of from two to eight elements in displays containing up to sixteen identical moving elements. We first show that depth cues, such as binocular disparity and occlusion through T-junctions, improve performance in a multi-element tracking task in the case where element boundaries are allowed to intersect in the depiction of motion in a single frontoparallel plane. We also show that the allocation of attention across two perceptually distinguishable planar surfaces, either frontoparallel or receding at a slanting angle and defined by coplanar elements, is easier than allocation of attention within a single surface. The same result was not found when attention was required to be deployed across items of two-color populations rather than across items of a single color. Our results suggest that, when surface information does not suffice to distinguish between targets and distractors that are embedded in these surfaces, division of attention across two surfaces aids in tracking moving targets. A final experiment with populations of elements moving within distinct volumes produced similar results, suggesting that spatial separation in three dimensions, rather than confinement to surfaces as such, may explain the improved performance for the two-surface case.

This article further develops the FACADE neural model of 3-D vision and figure-ground perception to quantitatively explain properties of the McCollough effect (ME). The model proposes that many ME data result from visual system mechanisms whose primary function is to adaptively align, through learning, boundary and surface representations that are positionally shifted due to the process of binocular fusion. For example, binocular boundary representations are shifted by binocular fusion relative to monocular surface representations, yet the boundaries must become positionally aligned with the surfaces to control binocular surface capture and filling-in. The model also includes perceptual reset mechanisms that use habituative transmitters in opponent processing circuits. Thus the model shows how ME data may arise from a combination of mechanisms that have a clear functional role in biological vision. Simulation results with a single set of parameters quantitatively fit data from 13 experiments that probe the nature of achromatic/chromatic and monocular/binocular interactions during induction of the ME. The model proposes how perceptual learning, opponent processing, and habituation at both monocular and binocular surface representations are involved, including early thalamocortical sites. In particular, it explains the anomalous ME utilizing these multiple processing sites. Alternative models of the ME are also summarized and compared with the present model.

Determining whether a region belongs to the interior or exterior of a shape (figure-ground segregation) is a core competency of the primate brain, yet the underlying mechanisms are not well understood. Many models assume that figure-ground segregation occurs by assembling progressively more complex representations through feedforward connections, with feedback playing only a modulatory role. We present a dynamical model of figure-ground segregation in the primate ventral stream wherein feedback plays a crucial role in disambiguating a figure's interior and exterior. We introduce a processing strategy whereby jitter in RF center locations and variation in RF sizes is exploited to enhance and suppress neural activity inside and outside of figures, respectively. Feedforward projections emanate from units that model cells in V4 known to respond to the curvature of boundary contours (curved contour cells), and feedback projections from units predicted to exist in IT that strategically group neurons with different RF sizes and RF center locations (teardrop cells). Neurons (convex cells) that preferentially respond when centered on a figure dynamically balance feedforward (bottom-up) information and feedback from higher visual areas. The activation is enhanced when an interior portion of a figure is in the RF via feedback from units that detect closure in the boundary contours of a figure. Our model produces maximal activity along the medial axis of well-known figures with and without concavities, and inside algorithmically generated shapes. Our results suggest that the dynamic balancing of feedforward signals with the specific feedback mechanisms proposed by the model is crucial for figure-ground segregation.

The visual cortex has a laminar organization whose circuits form functional columns in cortical maps. How this laminar architecture supports visual percepts is not well understood. A neural model proposes how the laminar circuits of V1 and V2 generate perceptual groupings that maintain sensitivity to the contrasts and spatial organization of scenic cues. The model can decisively choose which groupings cohere and survive, even while balanced excitatory and inhibitory interactions preserve contrast-sensitive measures of local boundary likelihood or strength. In the model, excitatory inputs from lateral geniculate nucleus (LGN) activate layers 4 and 6 of V1. Layer 6 activates an on-center off-surround network of inputs to layer 4. Together these layer 4 inputs preserve analog sensitivity to LGN input contrasts. Layer 4 cells excite pyramidal cells in layer 2/3, which activate monosynaptic long-range horizontal excitatory connections between layer 2/3 pyramidal cells, and short-range disynaptic inhibitory connections mediated by smooth stellate cells. These interactions support inward perceptual grouping between two or more boundary inducers, but not outward grouping from a single inducer. These boundary signals feed back to layer 4 via the layer 6-to-4 on-center off-surround network. This folded feedback joins cells in different layers into functional columns while selecting winning groupings. Layer 6 in V1 also sends top-down signals to LGN using an on-center off-surround network, which suppresses LGN cells that do not receive feedback, while selecting, enhancing, and synchronizing activity of those that do. The model is used to simulate psychophysical and neurophysiological data about perceptual grouping, including various Gestalt grouping laws.

A neural network system for boundary segmentation and surface representation, inspired by a new local-circuit model of visual processing in the cerebral cortex, is used to enhance images of range data gathered by a synthetic aperture radar (SAR) sensor. Boundary segmentation is accomplished by an improved Boundary Contour System (BCS) model which completes coherent boundaries that retain their sensitivity to image contrasts and locations. A Feature Contour System (FCS) model compensates for local contrast variations and uses the compensated signals to diffusively fill-in surface regions within the BCS boundaries. Image noise pixels that are not supported by BCS boundaries are hereby eliminated. More generally, BCS/FCS processing normalizes input dynamic range, reduces noise, and enhances contrasts between surface regions. BCS/FCS processing hereby makes structures such as motor vehicles, roads, and buildings more salient to human observers than in original imagery. The new BCS model improves image enhancement with significant reductions in processing time and complexity over previous BCS applications. The new system also outperforms several established techniques for image enhancement.

The influence of monocular occlusion cues on the perceived direction of motion of barber pole patterns is examined. Unlike previous studies that have emphasized the importance of binocular disparity, we find that monocular cues strongly influence the perceived motion direction and can even override binocular depth cues. The difference in motion bias for occluders with and without disparity cues is relatively small. Additionally, although ‘T-junctions’ aligned with occluders are particularly important, they are not strictly necessary for creating a change in motion perception. Finally, the amount of motion bias differs for several stimulus configurations, suggesting that the extrinsic:intrinsic classification of terminators is not all-or-none. © 1998 Elsevier Science Ltd. All rights reserved.

The motion after-effect (MAE) can be elicited by adapting observers to global motion of randomly distributed dots before they view a display containing dots moving in random directions, but no global motion. Experiments by others have shown that if the adaptation stimulus contains two directions of motion, the MAE points opposite to the vector sum of the adapting directions. The present study investigated whether such vector addition in the MAE could also occur if the two directions of motion were presented to separate eyes. Observers were adapted to different, but not opposite, directions of motion in the two eyes. Either the left eye, the right eye, or both eyes were tested. Observers reported the direction of perceived motion during the test. When they saw the test stimulus with both eyes, observers reported seeing motion in the direction opposite that of the vector sum of the adaptation directions. In the monocular test conditions observers reported MAE directions opposite to the corresponding monocular adaptation directions. In a second experiment we verified that subjects had interocular transfer of the MAE. Together these results are consistent with a model in which (1) addition of adaptation directions occurs at a binocular site; (2) directional adaptation occurs at a monocular site; and (3) monocular adaptation is able to change the threshold for obtaining an MAE at the binocular site, thus acting like binocular adaptation in interocular transfer of the MAE.

Over the past 10 years a neural modeling approach to understanding form and color vision has driven a productive research program of psychophysical and neurophysiological modeling as well as further theoretical development. Along the way, it has led to psychophysical experiments and image-processing applications. This work has helped us to better understand how we can percieve surface colors to be constant despite environmental variablity through neural mechansims of contrast measurement and filling-in, how patterns of recurrent excitatory and inhibitory cortical connectivity can interact to segregate forms despite camouflage and occlusion, how these neural representations of forms can be dynamically reset so that we can perceive rapidly changing scenes, and how learned recognition categories can interact with preattentive form and color representations to achieve efficient visual search. This paper discusses this neural modeling approach and reviews recent progress.

This paper is about a phenomenon that combines a delicate beauty with a profoundsignificance for understanding visual perception. The neon-like glow of a color that escapesthe boundaries of a real figure and fills the surrounding area until it is halted by theboundaries of an illusory figure has an ethereal quality unlike any other brightness and coloreffect. It also has substantial implications regarding the way in which our visual system usesseemingly incomplete stimuli to generate meaningful percepts, segregate objects from theirbackgrounds, and provide them with color and depth.As it so often happens in science, the effect was discovered and then independentlyrediscovered in the span of a few years. In 1971, Dario Varin of the University of Milanpublished a monograph on Ochromatic contrast and diffusionO whose front cover isreproduced in figure 1. A circular transparent veil can be seen to extend over four sets ofconcentric rings partly composed of blue arcs. It has a subtle bluish tinge and is luminescent,as if it emitted a weak light, or as if it were a colored spot of light cast by some off-screenlamp. This veil is illusory, in the sense that a point-by-point description of the reflectancespectrum of the white background would not reveal any changes corresponding to it.

How the brain generates visual percepts is a central problem in neuroscience. We propose a detailed neural model of how lateral geniculate nuclei and the interblob cortical stream through V1 and V2 generate context-sensitive perceptual groupings from visual inputs. The model suggests a functional role for cortical layers, columns, maps and networks, and proposes homologous circuits for V1 and V2 with larger-scale processing in V2. An integrated treatment of interlaminar, horizontal, orientational and endstopping cortical interactions and a role for corticogeniculate feedback in grouping are proposed. Modeled circuits simulate parametric psychophysical data about boundary grouping and illusory contour formation.

Receptive field sizes of neurons in early primate visual areas increase with eccentricity, as does temporal processing speed. The fovea is evidently specialized for slow, fine movements while the periphery is suited for fast, coarse movements. In either the fovea or periphery discrete flashes can produce motion percepts. Grossberg and Rudd (1989) used traveling Gaussian activity profiles to model long-range apparent motion percepts. We propose a neural model constrained by physiological data to explain how signals from retinal ganglion cells to V1 affect the perception of motion as a function of eccentricity. Our model incorporates cortical magnification, receptive field overlap and scatter, and spatial and temporal response characteristics of retinal ganglion cells for cortical processing of motion. Consistent with the finding of Baker and Braddick (1985), in our model the maximum flash distance that is perceived as an apparent motion (Dmax) increases linearly as a function of eccentricity. Baker and Braddick (1985) made qualitative predictions about the functional significance of both stimulus and visual system parameters that constrain motion perception, such as an increase in the range of detectable motions as a function of eccentricity and the likely role of higher visual processes in determining Dmax. We generate corresponding quantitative predictions for those functional dependencies for individual aspects of motion processing. Simulation results indicate that the early visual pathway can explain the qualitative linear increase of Dmax data without reliance on extrastriate areas, but that those higher visual areas may serve as a modulatory influence on the exact Dmax increase.

Humans are capable of rapidly determining whether regions in a visual scene appear as figures in the foreground or as background, yet how figure-ground segregation occurs in the primate visual system is unknown. Figures in the environment are perceived to own their borders, and recent neurophysiology has demonstrated that certain cells in primate visual area V2 have border-ownership selectivity. We present a dynamic model based on physiological data that indicates areas V1, V2, and V4 act as an interareal network to determine border-ownership. Our model predicts that competition between curvature- sensitive cells in V4 that have on-surround receptive fields of different sizes can determine likely figure locations and rapidly propagate the information interareally to V2 border-ownership cells that receive contrast information from V1. In the model border-ownership is an emergent property produced by the dynamic interactions between V1, V2, and V4, one which could not be determined by any single cortical area alone.

A neural network model of brightness perception is developed to account for a wide variety of data, including the classical phenomenon of Mach bands, low- and high-contrast missing fundamental, luminance staircases, and non-linear contrast effects associated with sinusoidal waveforms. The model builds upon previous work on filling-in models that produce brightness profiles through the interaction of boundary and feature signals. Boundary computations that are sensitive to luminance steps and to continuous luminance gradients are presented. A new interpretation of feature signals through the explicit representation of contrast-driven and luminance-driven information is provided and directly addresses the issue of brightness "anchoring". Computer simulations illustrate the model's competencies.

A neural network model of boundary segmentation and surface representation is developed to process images containing range data gathered by a synthetic aperture radar (SAR) sensor. The boundary and surface processing are accomplished by an improved Boundary Countour System (BCS) and Feature Countour System (FCS), respectively, that have been derived from analyses of perceptual and neurobiological data. BCS/FCS processing makes structures such as motor vehicles, roads, and buildings more salient and interpretable to human observers than they are in the original imagery. Early processing by ON cells and OFF cells embedded in shunting center-surround network models preprocessing by lateral geniculate nucleus (LGN). Such preprocessing compensates for illumination gradients, normalizes input dynamic range, and extracts local ratio contrasts. ON cell and OFF cell outputs are combined in the BCS to define oriental filters that model corticla simple cells. Pooling ON and OFF outputs at simple cells overcomes complementary processing deficiencies of each cell type along concave and convex contours, and enhances simpl;e cell sensitivity to image edges. Oriented filter outputs are rectified and outputs sensitive to opposite contrast polarities are pooled to define complex cells. The complex cells output to stages of short-range spatial competition (or endstopping) and orientational competition among hypercomplex cells. Hypercomplex cells activate long-range cooperative bipole cells that begin to group image boundaries. Nonlinear feedback between bipole cells and hypercomplex cell segments image regions by cooperatively completing and regularizing the most favored boundaries while suppressing image noise and weaker boundary groupings. Boundary segmentation is performed by three copies of the BCS at small, medium, and large filter scales, whose subsequent interaction distances covary with the size of the filter. Filling-in of multiple surface representations occurs within the FCS at each scale via a boundary-gated diffusion process. Diffusion is activated by the normalized LGN ON and OFF outputs within ON and OFFfilling-in domains. Diffusion is restricted to the regions defined by gating signals from the corresponding BCS boundary segmentation. The filled-in opponent ON and OFFsignals are subtracted to form double opponent surface representations. These surface representations are shown by any of three methods to be sensitive to both image ratio contrasts and background luminance. The three scales of surface representation are then added to yield a final multiple-scale output. The BCS and FCS are shown to perform favorably in comparison to several other techniques for speckle removal.

An observer traversing an environment actively relocates gaze to fixate objects. Evidence suggests that gaze is frequently directed toward the center of an object considered as target but more likely toward the edges of an object that appears as an obstacle. We suggest that this difference in gaze might be motivated by specific patterns of optic flow that are generated by either fixating the center or edge of an object. To support our suggestion we derive an analytical model that shows: Tangentially fixating the outer surface of an obstacle leads to strong flow discontinuities that can be used for flow-based segmentation. Fixation of the target center while gaze and heading are locked without head-, body-, or eye-rotations gives rise to a symmetric expansion flow with its center at the point being approached, which facilitates steering toward a target. We conclude that gaze control incorporates ecological constraints to improve the robustness of steering and collision avoidance by actively generating flows appropriate to solve the task.

The researchers at Boston University (BU)'s Neuromorphics Laboratory, part of the National Science Foundation (NSF)-sponsored Center of Excellence for Learning in Education, Science, and Technology (CELEST), are working in collaboration with the engineers and scientists at Hewlett-Packard (HP) to implement neural models of intelligent processes for the next generation of dense, low-power, computer hardware that will use memristive technology to bring data closer to the processor where computation occurs. The HP and BU teams are jointly designing an optimal infrastructure, simulation, and software platform to build an artificial brain. The resulting Cog Ex Machina (Cog) software platform has been successfully used to implement a large-scale, multicomponent brain system that is able to simulate some key rat behavioral results in a virtual environment and has been applied to control robotic platforms as they learn to interact with their environment.

Humans accurately judge their direction of heading when translating in a rigid environment, unless independently moving objects (IMOs) cross the observer's focus of expansion (FoE). Studies show that an IMO on a laterally moving path that maintains a fixed distance with respect to the observer (non-approaching; C. S. Royden & E. C. Hildreth, 1996) biases human heading estimates differently from an IMO on a lateral path that gets closer to the observer (approaching; W. H. Warren & J. A. Saunders, 1995). C. S. Royden (2002) argued that differential motion operators in primate brain area MT explained both data sets, concluding that differential motion was critical to human heading estimation. However, neurophysiological studies show that motion pooling cells, but not differential motion cells, in MT project to heading-sensitive cells in MST (V. K. Berezovskii & R. T. Born, 2000). It is difficult to reconcile differential motion heading models with these neurophysiological data. We generate motion sequences that mimic those viewed by human subjects. Model MT pools over V1; units in model MST perform distance-weighted template matching and compete in a recurrent heading representation layer. Our model produces heading biases of the same direction and magnitude as humans through a peak shift in model MSTd without using differential motion operators, maintaining consistency with known primate neurophysiology.

Convergent advances in neural modeling, neuroinformatics, neuromorphic engineering, materials science, and computer science will soon enable the development and manufacture of novel computer architectures, including those based on memristive technologies that seek to emulate biological brain structures. A new computational platform, Cog Ex Machina, is a flexible modeling tool that enables a variety of biological-scale neuromorphic algorithms to be implemented on heterogeneous processors, including both conventional and neuromorphic hardware. Cog Ex Machina is specifically designed to leverage the upcoming introduction of dense memristive memories close to computing cores. The MoNETA (Modular Neural Exploring Traveling Agent) model is comprised of such algorithms to generate complex behaviors based on functionalities that include perception, motivation, decision-making, and navigation. MoNETA is being developed with Cog Ex Machina to exploit new hardware devices and their capabilities as well as to demonstrate intelligent, autonomous behaviors in both virtual animats and robots. These innovations in hardware, software, and brain modeling will not only advance our understanding of how to build adaptive, simulated, or robotic agents, but will also create innovative technological applications with major impacts on general-purpose and high-performance computing.

An analysis of the reset of visual cortical circuits responsible for the binding or segmentation of visual features into coherent visual forms yields a model that explains properties of visual persistence. The reset mechanisms prevent massive smearing of visual percepts in response to rapidly moving images. The model simulates relationships among psychophysical data showing inverse relations of persistence to flash luminance and duration, greater persistence of illusory contours than real contours, a U-shaped temporal function for persistence of illusory contours, a reduction of persistence due to adaptation with a stimulus of like orientation, an increase of persistence with spatial separation of a masking stimulus. The model suggests that a combination of habituative, opponent, and endstopping mechanisms prevent smearing and limit persistence. Earlier work with the model has analyzed data about boundary formation, texture segregation, shape-from-shading, and figure-ground separation. Thus, several types of data support each model mechanism and new predictions are made.

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.

Realizing adaptive brain functions subserving perception, cognition, and motor behavior on biological temporal and spatial scales remains out of reach for even the fastest computers. Newly introduced memristive hardware approaches open the opportunity to implement dense, low-power synaptic memories of up to 1015 bits per square centimeter. Memristors have the unique property of “remembering” the past history of their stimulation in their resistive state and do not require power to maintain their memory, making them ideal candidates to implement large arrays of plastic synapses supporting learning in neural models. Over the past decades, many learning rules have been proposed in the literature to explain how neural activity shapes synaptic connections to support adaptive behavior. To ensure an optimal implementation of a large variety of learning rules in hardware, some general and easily parameterized form of learning rule must be designed. This general form learning equation would allow instantiation of multiple learning rules through different parameterizations, without rewiring the hardware. This paper characterizes a subset of local learning rules amenable to implementation in memristive hardware. The analyzed rules belong to four broad classes: Hebb rule derivatives with various methods for gating learning and decay, Threshold rule variations including the covariance and BCM families, Input reconstruction-based learning rules, and Explicit temporal trace-based rules.

A neural modeling platform known as Cog ex Machina1 (Cog) developed in the context of the DARPA SyNAPSE2 program offers a computational environment that promises, in a foreseeable future, the creation of adaptive whole-brain systems subserving complex behavioral functions in virtual and robotic agents. Cog is designed to operate on low-powered, extremely storage-dense memristive hardware3 that would support massively-parallel, scalable computations. We report an adaptive robotic agent, ViGuAR4, that we developed as a neural model implemented on the Cog platform. The neuromorphic architecture of the ViGuAR brain is designed to support visually-guided navigation and learning, which in combination with the path-planning, memory-driven navigation agent - MoNETA5 - also developed at the Neuromorphics Lab at Boston University, should effectively account for a wide range of key features in rodents' navigational behavior.

The watercolor effect (WCE) is a filling-in phenomenon in a region demarcated by two thin abutting lines. The perceived chromaticity of the region is similar to that of the interior line. We develop a series of achromatic WCE stimuli to induce lightness changes analogous to the induced chromaticity in the chromatic version of the WCE. We use a variation of the paired-comparison paradigm to quantify the induced lightness of the filled-in regions to regions with real luminance variations. The luminance of the inner line is fixed, while the luminance of the outer line varies across stimuli. Data from seven subjects (five naive) confirm that an achromatic WCE exists. Moreover, outer lines with both high and low luminances can generate a WCE with an inner line of a moderate luminance. All subjects show a single peak of the effect strength for both polarity conditions, which is never at the extreme luminance levels. Most subjects show an inverted U curve for effect strength as a function of the contrast of the outer lines against the background. Results suggest that the contrast difference between the outer line and the inner line affects the existence and the strength of the achromatic WCE in a nonlinear way.