Short-term memory for figure-ground organization in the visual cortex

Grouping cells in primate visual cortex

Our perception of how objects are laid out in visual scenes is remarkably stable, despite rapid shifts in the patterns of light that fall on the retina with each saccade. One mechanism that may help establish perceptual stability is border ownership assignment. Studies in macaque area V2 have identified border ownership neurons that signal which side of a border belongs to a foreground surface. This signal persists for hundreds of milliseconds after border ownership has been rendered ambiguous by deleting the stimulus features that distinguish foreground from background. Remarkably, this signal survives eye movements: border ownership neurons also exhibit border ownership signals de novo when an eye movement places the newly ambiguous border within their receptive field. The grouping cell hypothesis proposes the existence of hypothetical grouping cells in a downstream brain area. These cells would compute persistent proto-object representations and therefore have the properties to endow cells in upstream brain areas with selectivity for border ownership. Such grouping cells have been predicted to show a centripetal and persistent pattern of preferred side of ownership for a border placed parallel to the perimeter of their classical receptive field, and such a centripetal ownership preference pattern should also occur de novo in these same cells if an ambiguous border lands in their receptive field after a saccade. It is unknown if grouping cells exist. Here we used laminar multielectrodes in area V4 - the main source of feedback to V2 - of behaving macaques to determine whether such grouping cells exist. Consistent with the model prediction we find a substantial population of neurons with these properties, in all laminar compartments, and they exhibit a response latency that is short enough to act as the source that endows neurons in V2 with selectivity for border ownership. While grouping cell activity provides information about the location of foreground surfaces, these neurons are, counterintuitively, not as strongly tuned for luminance contrast polarity, a feature of those surfaces, as are border ownership cells. Our data suggest a division of labor in which these newly discovered grouping cells provide spatiotemporal continuity of segmented surfaces whereas border ownership cells link this location information with surface features such as luminance contrast.

Solving the binding problem: Assemblies form when neurons enhance their firing rate-they don't need to oscillate or synchronize

When we look at an image, its features are represented in our visual system in a highly distributed manner, calling for a mechanism that binds them into coherent object representations. There have been different proposals for the neuronal mechanisms that can mediate binding. One hypothesis is that binding is achieved by oscillations that synchronize neurons representing features of the same perceptual object. This view allows separate communication channels between different brain areas. Another hypothesis is that binding of features that are represented in different brain regions occurs when the neurons in these areas that respond to the same object simultaneously enhance their firing rate, which would correspond to directing object-based attention to these features. This review summarizes evidence in favor of and against these two hypotheses, examining the neuronal correlates of binding and assessing the time course of perceptual grouping. I conclude that enhanced neuronal firing rates bind features into coherent object representations, whereas oscillations and synchrony are unrelated to binding.

Disinhibitory circuit mediated by connections from vasoactive intestinal polypeptide to somatostatin interneurons underlies the paradoxical decrease in spike synchrony with increased border ownership selective neuron firing rate

The activity of border ownership selective (BOS) neurons in intermediate-level visual areas indicates which side of a contour owns a border relative to its classical receptive field and provides a fundamental component of figure-ground segregation. A physiological study reported that selective attention facilitates the activity of BOS neurons with a consistent border ownership preference, defined as two neurons tuned to respond to the same visual object. However, spike synchrony between this pair is significantly suppressed by selective attention. These neurophysiological findings are derived from a biologically-plausible microcircuit model consisting of spiking neurons including two subtypes of inhibitory interneurons, somatostatin (SOM) and vasoactive intestinal polypeptide (VIP) interneurons, and excitatory BOS model neurons. In our proposed model, BOS neurons and SOM interneurons cooperate and interact with each other. VIP interneurons not only suppress SOM interneuron responses but also are activated by feedback signals mediating selective attention, which leads to disinhibition of BOS neurons when they are directing selective attention toward an object. Our results suggest that disinhibition arising from the synaptic connections from VIP to SOM interneurons plays a critical role in attentional modulation of neurons in intermediate-level visual areas.

Columnar processing of border ownership in primate visual cortex

To understand a visual scene, the brain segregates figures from background by assigning borders to foreground objects. Neurons in primate visual cortex encode which object owns a border (border ownership), but the underlying circuitry is not understood. Here, we used multielectrode probes to record from border ownership-selective units in different layers in macaque visual area V4 to study the laminar organization and timing of border ownership selectivity. We find that border ownership selectivity occurs first in deep layer units, in contrast to spike latency for small stimuli in the classical receptive field. Units on the same penetration typically share the preferred side of border ownership, also across layers, similar to orientation preference. Units are often border ownership-selective for a range of border orientations, where the preferred sides of border ownership are systematically organized in visual space. Together our data reveal a columnar organization of border ownership in V4 where the earliest border ownership signals are not simply inherited from upstream areas, but computed by neurons in deep layers, and may thus be part of signals fed back to upstream cortical areas or the oculomotor system early after stimulus onset. The finding that preferred border ownership is clustered and can cover a wide range of spatially contiguous locations suggests that the asymmetric context integrated by these neurons is provided in a systematically clustered manner, possibly through corticocortical feedback and horizontal connections.

To understand a visual scene, the brain needs to identify objects and distinguish them from background. A border marks the transition from object to background, but to differentiate which side of the border belongs to the object and which to background, the brain must integrate information across space. An early signature of this computation is that brain cells signal which side of a border is ‘owned’ by an object, also known as border ownership. But how the brain computes border ownership remains unknown.

The optic nerve is a cable-like group of nerve cells that transmits information from the eye to the brain’s visual processing areas and into the visual cortex. This flow of information is often described as traveling in a feedforward direction, away from the eyes to progressively more specialized areas in the visual cortex. However, there are also numerous feedback connections in the brain, running backward from more specialized to less specialized cortical areas.

To better understand the role of these feedforward and feedback circuits in the visual processing of object borders, Franken and Reynolds made use of their stereotyped projection patterns across the cortex layers. Feedforward connections terminate in the middle layers of a cortical area, whereas feedback connections terminate in upper and lower layers. Since time is required for information to traverse the cortical layers, dissecting the timing of border ownership signals may reveal if border ownership is computed in a feedforward or feedback manner. To find out more, electrodes were used to record neural activity in the upper, middle and lower layers of the visual cortex of two rhesus monkeys as they were presented with a set of abstract scenes composed of simple shapes on a background.

This revealed that cells signaling border ownership in deep layers of the cortex did so before the signals appeared in the middle layer. This suggests that feedback rather than feedforward is required to compute border ownership. Moreover, Franken and Reynolds found evidence that cells that prefer the same side of border ownership are clustered in columns, showing how these neural circuits are organized within the visual cortex.

In summary, Franken and Reynolds found that the circuits of the primate brain that compute border ownership occur as columns, in which cells in deep layers signal border ownership first, suggesting that border ownership relies on feedback from more specialized areas. A better understanding of how feedback in the brain works to process visual information helps us appreciate what happens when these systems are impaired.

Analysis of spiking synchrony in visual cortex reveals distinct types of top-down modulation signals for spatial and object-based attention

The activity of a border ownership selective (BOS) neuron indicates where a foreground object is located relative to its (classical) receptive field (RF). A population of BOS neurons thus provides an important component of perceptual grouping, the organization of the visual scene into objects. In previous theoretical work, it has been suggested that this grouping mechanism is implemented by a population of dedicated grouping (“G”) cells that integrate the activity of the distributed feature cells representing an object and, by feedback, modulate the same cells, thus making them border ownership selective. The feedback modulation by G cells is thought to also provide the mechanism for object-based attention. A recent modeling study showed that modulatory common feedback, implemented by synapses with N-methyl-D-aspartate (NMDA)-type glutamate receptors, accounts for the experimentally observed synchrony in spike trains of BOS neurons and the shape of cross-correlations between them, including its dependence on the attentional state. However, that study was limited to pairs of BOS neurons with consistent border ownership preferences, defined as two neurons tuned to respond to the same visual object, in which attention decreases synchrony. But attention has also been shown to increase synchrony in neurons with inconsistent border ownership selectivity. Here we extend the computational model from the previous study to fully understand these effects of attention. We postulate the existence of a second type of G-cell that represents spatial attention by modulating the activity of all BOS cells in a spatially defined area. Simulations of this model show that a combination of spatial and object-based mechanisms fully accounts for the observed pattern of synchrony between BOS neurons. Our results suggest that modulatory feedback from G-cells may underlie both spatial and object-based attention.

Vision allows us to make sense out of a very complex signal, the patterns of light rays reaching our eyes. Two mechanisms are essential for this: perceptual organization which structures the input into meaningful visual objects, and attention which selects only the most important parts in the input. Prior work suggests that both of these mechanisms are implemented by neurons called grouping cells. These organize the object features into coherent entities (perceptual grouping) and access them as needed (selective attention). For technical reasons it is difficult to observe grouping cells but their effect can be seen in the influence they have on responses of other classes of cells. These responses have been measured experimentally and it was found that they depend in unexpected ways on where the subject was attending. Using a computational model, we here demonstrate that the responses can be understood in terms of the interaction between two kinds of selective attention, both of which are known to occur in primate perception. One is attention to a specific area in the environment, the other is to specific objects. A model including both of these attentional mechanisms generates neuronal responses in agreement with the observed patterns of neural activity.

Multiple decisions about one object involve parallel sensory acquisition but time-multiplexed evidence incorporation

The brain is capable of processing several streams of information that bear on different aspects of the same problem. Here, we address the problem of making two decisions about one object, by studying difficult perceptual decisions about the color and motion of a dynamic random dot display. We find that the accuracy of one decision is unaffected by the difficulty of the other decision. However, the response times reveal that the two decisions do not form simultaneously. We show that both stimulus dimensions are acquired in parallel for the initial ∼0.1 s but are then incorporated serially in time-multiplexed bouts. Thus, there is a bottleneck that precludes updating more than one decision at a time, and a buffer that stores samples of evidence while access to the decision is blocked. We suggest that this bottleneck is responsible for the long timescales of many cognitive operations framed as decisions.

Normative data for an expanded set of stimuli for testing high-level influences on object perception: OMEFA-II

We present normative data for an expanded set of stimuli designed to investigate past experience effects on object detection. The stimuli are vertically-elongated “bipartite” displays comprising two equal-area regions meeting at an articulated central border. When the central border is assigned to one side, a shaped figure (i.e., an object) is detected on that side. Participants viewing brief masked exposures typically detect figures more often on the critical side of Intact displays where a common (“familiar”) object is depicted than on a matched critical side of Part-Rearranged (PR) displays comprising the same parts arranged in novel configurations. This pattern of results showed that past experience in the form of familiar configuration rather than familiar parts is a prior for figure assignment. Spurred by research implicating a network involving the perirhinal cortex of the medial temporal lobe in these familiar configuration effects, we enlarged the stimulus set from 24 to 48 base stimuli to increase its usefulness for behavioral, neuropsychological, and neuroimaging experiments. We measured the percentage of participants who agreed on a single interpretation for each side of Intact, Upright PR, and Inverted PR displays (144 displays; 288 sides) under long exposure conditions. High inter-subject agreement is taken to operationally define a familiar configuration. This new stimulus set is well-suited to investigate questions concerning how parts and wholes are integrated and how high- and low-level brain areas interact in object detection. This set also allows tests of predictions regarding cross-border competition in figure assignment and assessments of individual differences. The displays, their image statistics, and normative data are available online (https://osf.io/j9kz2/).

Searching for object pointers in the visual cortex

We perceive objects as permanent and stable despite frequent occlusions and eye movements, but their representation in the visual cortex is neither permanent nor stable. Feature selective cells respond only as long as objects are visible, and their responses depend on eye position. We explored the hypothesis that the system maintains object pointers that provide permanence and stability. Pointers should send facilitatory signals to the feature cells of an object, and these signals should persist across temporary occlusions and remap to compensate for image displacements caused by saccades. Here, we searched for such signals in monkey areas V2 and V4 (Macaca mulatta). We developed a new paradigm in which a monkey freely inspects an array of objects in search for reward while some of the objects are being occluded temporarily by opaque drifting strips. Two types of objects were used to manipulate attention. The results were as follows. 1) Eye movements indicated a robust representation of location and type of the occluded objects; 2) in neurons of V4, but not V2, occluded objects produced elevated activity relative to blank condition; 3) the elevation of activity was reduced for objects that had been fixated immediately before the current fixation ('inhibition of return'); and 4) when attended, or when the target of a saccade, visible objects produced enhanced responses in V4, but occluded objects produced no modulation. Although results 1-3 confirm the hypothesis, the absence of modulation under occlusion is not consistent. Further experiments are needed to resolve this discrepancy.NEW & NOTEWORTHY The way we perceive objects as permanent contrasts with the short-lived responses of visual cortical neurons. A theory postulates pointers that give objects continuity, predicting a class of neurons that respond not only to visual objects but also when an occluded object moves into their receptive field. Here, we tested this theory with a novel paradigm in which a monkey freely scans an array of objects while some of them are transiently occluded.

The resonant brain: How attentive conscious seeing regulates action sequences that interact with attentive cognitive learning, recognition, and prediction

This article describes mechanistic links that exist in advanced brains between processes that regulate conscious attention, seeing, and knowing, and those that regulate looking and reaching. These mechanistic links arise from basic properties of brain design principles such as complementary computing, hierarchical resolution of uncertainty, and adaptive resonance. These principles require conscious states to mark perceptual and cognitive representations that are complete, context sensitive, and stable enough to control effective actions. Surface-shroud resonances support conscious seeing and action, whereas feature-category resonances support learning, recognition, and prediction of invariant object categories. Feedback interactions between cortical areas such as peristriate visual cortical areas V2, V3A, and V4, and the lateral intraparietal area (LIP) and inferior parietal sulcus (IPS) of the posterior parietal cortex (PPC) control sequences of saccadic eye movements that foveate salient features of attended objects and thereby drive invariant object category learning. Learned categories can, in turn, prime the objects and features that are attended and searched. These interactions coordinate processes of spatial and object attention, figure-ground separation, predictive remapping, invariant object category learning, and visual search. They create a foundation for learning to control motor-equivalent arm movement sequences, and for storing these sequences in cognitive working memories that can trigger the learning of cognitive plans with which to read out skilled movement sequences. Cognitive-emotional interactions that are regulated by reinforcement learning can then help to select the plans that control actions most likely to acquire valued goal objects in different situations. Many interdisciplinary psychological and neurobiological data about conscious and unconscious behaviors in normal individuals and clinical patients have been explained in terms of these concepts and mechanisms.

Interactions Elicited by the Contradiction Between Figure Direction Discrimination and Figure-Ground Segregation

Figure-ground (FG) segregation that separates an object from the rest of the image is a fundamental problem in vision science. A majority of neurons in monkey V2 showed the selectivity to border ownership (BO) that indicates which side of a contour owns the border. Although BO could be a precursor of FG segregation, the contribution of BO to FG segregation has not been clarified. Because FG segregation is the perception of the global region that belongs to an object, whereas BO determination provides the local direction of figure (DOF) along a contour, a spatial integration of BO might be expected for the generation of FG. To understand the mechanisms underlying the perception of figural regions, we investigated the interaction between the local BO determination and the global FG segregation through the quantitative analysis of the visual perception and the spatiotemporal characteristics of eye movements. We generated a set of novel stimuli in which translucency induces local DOF along the contour and global FG independently so that DOF and FG could be either consistent or contradictory. The perceptual responses showed better performance in DOF discrimination than FG segregation, supporting distinct mechanisms for the DOF discrimination and the FG segregation. We examined whether the contradiction between DOF and FG modulates the eye movement while participants judged DOF and FG. The duration of the first eye fixation was modulated by the contradiction during FG segregation but not DOF discrimination, suggesting a sequential processing from the BO determination to the FG segregation. These results of human perception and eye fixation provide important clues for understanding the visual processing for FG segregation.

Figure and ground: how the visual cortex integrates local cues for global organization

Inferring figure-ground organization in two-dimensional images may require different complementary strategies. For isolated objects, it has been shown that mechanisms in visual cortex exploit the overall distribution of contours, but in images of cluttered scenes where the grouping of contours is not obvious, that strategy would fail. However, natural scenes contain local features, specifically contour junctions, that may contribute to the definition of object regions. To study the role of local features in the assignment of border ownership, we recorded single-cell activity from visual cortex in awake behaving Macaca mulatta. We tested configurations perceived as two overlapping figures in which T- and L-junctions depend on the direction of overlap, whereas the overall distribution of contours provides no valid information. While recording responses to the occluding contour, we varied direction of overlap and variably masked some of the critical contour features to determine their influences and their interactions. On average, most features influenced the responses consistently, producing either enhancement or suppression depending on border ownership. Different feature types could have opposite effects even at the same location. Features far from the receptive field produced effects as strong as near features and with the same short latency. Summation was highly nonlinear: any single feature produced more than two-thirds of the effect of all features together. These findings reveal fast and highly specific organization mechanisms, supporting a previously proposed model in which "grouping cells" integrate widely distributed edge signals with specific end-stopped signals to modulate the original edge signals by feedback. NEW & NOTEWORTHY Seeing objects seems effortless, but defining objects in a scene requires sophisticated neural mechanisms. For isolated objects, the visual cortex groups contours based on overall distribution, but this strategy does not work for cluttered scenes. Here, we demonstrate mechanisms that integrate local contour features like T- and L-junctions to resolve clutter. The process is fast, evaluates widely distributed features, and gives any single feature a decisive influence on figure-ground representation.

Short-term depression and transient memory in sensory cortex

Persistent neuronal activity is usually studied in the context of short-term memory localized in central cortical areas. Recent studies show that early sensory areas also can have persistent representations of stimuli which emerge quickly (over tens of milliseconds) and decay slowly (over seconds). Traditional positive feedback models cannot explain sensory persistence for at least two reasons: (i) They show attractor dynamics, with transient perturbations resulting in a quasi-permanent change of system state, whereas sensory systems return to the original state after a transient. (ii) As we show, those positive feedback models which decay to baseline lose their persistence when their recurrent connections are subject to short-term depression, a common property of excitatory connections in early sensory areas. Dual time constant network behavior has also been implemented by nonlinear afferents producing a large transient input followed by much smaller steady state input. We show that such networks require unphysiologically large onset transients to produce the rise and decay observed in sensory areas. Our study explores how memory and persistence can be implemented in another model class, derivative feedback networks. We show that these networks can operate with two vastly different time courses, changing their state quickly when new information is coming in but retaining it for a long time, and that these capabilities are robust to short-term depression. Specifically, derivative feedback networks with short-term depression that acts differentially on positive and negative feedback projections are capable of dynamically changing their time constant, thus allowing fast onset and slow decay of responses without requiring unrealistically large input transients.

Modeling the Time-Course of Responses for the Border Ownership Selectivity Based on the Integration of Feedforward Signals and Visual Cortical Interactions

Border ownership (BO) indicates which side of a contour owns a border, and it plays a fundamental role in figure-ground segregation. The majority of neurons in V2 and V4 areas of monkeys exhibit BO selectivity. A physiological work reported that the responses of BO-selective cells show a rapid transition when a presented square is flipped along its classical receptive field (CRF) so that the opposite BO is presented, whereas the transition is significantly slower when a square with a clear BO is replaced by an ambiguous edge, e.g., when the square is enlarged greatly. The rapid transition seemed to reflect the influence of feedforward processing on BO selectivity. Herein, we investigated the role of feedforward signals and cortical interactions for time-courses in BO-selective cells by modeling a visual cortical network comprising V1, V2, and posterior parietal (PP) modules. In our computational model, the recurrent pathways among these modules gradually established the visual progress and the BO assignments. Feedforward inputs mainly determined the activities of these modules. Surrounding suppression/facilitation of early-level areas modulates the activities of V2 cells to provide BO signals. Weak feedback signals from the PP module enhanced the contrast gain extracted in V1, which underlies the attentional modulation of BO signals. Model simulations exhibited time-courses depending on the BO ambiguity, which were caused by the integration delay of V1 and V2 cells and the local inhibition therein given the difference in input stimulus. However, our model did not fully explain the characteristics of crucially slow transition: the responses of BO-selective physiological cells indicated the persistent activation several times longer than that of our model after the replacement with the ambiguous edge. Furthermore, the time-course of BO-selective model cells replicated the attentional modulation of response time in human psychophysical experiments. These attentional modulations for time-courses were induced by selective enhancement of early-level features due to interactions between V1 and PP. Our proposed model suggests fundamental roles of surrounding suppression/facilitation based on feedforward inputs as well as the interactions between early and parietal visual areas with respect to the ambiguity dependence of the neural dynamics in intermediate-level vision.

Towards solving the hard problem of consciousness: The varieties of brain resonances and the conscious experiences that they support

The hard problem of consciousness is the problem of explaining how we experience qualia or phenomenal experiences, such as seeing, hearing, and feeling, and knowing what they are. To solve this problem, a theory of consciousness needs to link brain to mind by modeling how emergent properties of several brain mechanisms interacting together embody detailed properties of individual conscious psychological experiences. This article summarizes evidence that Adaptive Resonance Theory, or ART, accomplishes this goal. ART is a cognitive and neural theory of how advanced brains autonomously learn to attend, recognize, and predict objects and events in a changing world. ART has predicted that "all conscious states are resonant states" as part of its specification of mechanistic links between processes of consciousness, learning, expectation, attention, resonance, and synchrony. It hereby provides functional and mechanistic explanations of data ranging from individual spikes and their synchronization to the dynamics of conscious perceptual, cognitive, and cognitive-emotional experiences. ART has reached sufficient maturity to begin classifying the brain resonances that support conscious experiences of seeing, hearing, feeling, and knowing. Psychological and neurobiological data in both normal individuals and clinical patients are clarified by this classification. This analysis also explains why not all resonances become conscious, and why not all brain dynamics are resonant. The global organization of the brain into computationally complementary cortical processing streams (complementary computing), and the organization of the cerebral cortex into characteristic layers of cells (laminar computing), figure prominently in these explanations of conscious and unconscious processes. Alternative models of consciousness are also discussed.

Neural Computation of Surface Border Ownership and Relative Surface Depth from Ambiguous Contrast Inputs

The segregation of image parts into foreground and background is an important aspect of the neural computation of 3D scene perception. To achieve such segregation, the brain needs information about border ownership; that is, the belongingness of a contour to a specific surface represented in the image. This article presents psychophysical data derived from 3D percepts of figure and ground that were generated by presenting 2D images composed of spatially disjoint shapes that pointed inward or outward relative to the continuous boundaries that they induced along their collinear edges. The shapes in some images had the same contrast (black or white) with respect to the background gray. Other images included opposite contrasts along each induced continuous boundary. Psychophysical results demonstrate conditions under which figure-ground judgment probabilities in response to these ambiguous displays are determined by the orientation of contrasts only, not by their relative contrasts, despite the fact that many border ownership cells in cortical area V2 respond to a preferred relative contrast. Studies are also reviewed in which both polarity-specific and polarity-invariant properties obtain. The FACADE and 3D LAMINART models are used to explain these data.

Cortical Dynamics of Figure-Ground Separation in Response to 2D Pictures and 3D Scenes: How V2 Combines Border Ownership, Stereoscopic Cues, and Gestalt Grouping Rules

The FACADE model, and its laminar cortical realization and extension in the 3D LAMINART model, have explained, simulated, and predicted many perceptual and neurobiological data about how the visual cortex carries out 3D vision and figure-ground perception, and how these cortical mechanisms enable 2D pictures to generate 3D percepts of occluding and occluded objects. In particular, these models have proposed how border ownership occurs, but have not yet explicitly explained the correlation between multiple properties of border ownership neurons in cortical area V2 that were reported in a remarkable series of neurophysiological experiments by von der Heydt and his colleagues; namely, border ownership, contrast preference, binocular stereoscopic information, selectivity for side-of-figure, Gestalt rules, and strength of attentional modulation, as well as the time course during which such properties arise. This article shows how, by combining 3D LAMINART properties that were discovered in two parallel streams of research, a unified explanation of these properties emerges. This explanation proposes, moreover, how these properties contribute to the generation of consciously seen 3D surfaces. The first research stream models how processes like 3D boundary grouping and surface filling-in interact in multiple stages within and between the V1 interblob—V2 interstripe—V4 cortical stream and the V1 blob—V2 thin stripe—V4 cortical stream, respectively. Of particular importance for understanding figure-ground separation is how these cortical interactions convert computationally complementary boundary and surface mechanisms into a consistent conscious percept, including the critical use of surface contour feedback signals from surface representations in V2 thin stripes to boundary representations in V2 interstripes. Remarkably, key figure-ground properties emerge from these feedback interactions. The second research stream shows how cells that compute absolute disparity in cortical area V1 are transformed into cells that compute relative disparity in cortical area V2. Relative disparity is a more invariant measure of an object's depth and 3D shape, and is sensitive to figure-ground properties.

Figure-ground organization and the emergence of proto-objects in the visual cortex

A long history of studies of perception has shown that the visual system organizes the incoming information early on, interpreting the 2D image in terms of a 3D world and producing a structure that provides perceptual continuity and enables object-based attention. Recordings from monkey visual cortex show that many neurons, especially in area V2, are selective for border ownership. These neurons are edge selective and have ordinary classical receptive fields (CRF), but in addition their responses are modulated (enhanced or suppressed) depending on the location of a 'figure' relative to the edge in their receptive field. Each neuron has a fixed preference for location on one side or the other. This selectivity is derived from the image context far beyond the CRF. This paper reviews evidence indicating that border ownership selectivity reflects the formation of early object representations ('proto-objects'). The evidence includes experiments showing (1) reversal of border ownership signals with change of perceived object structure, (2) border ownership specific enhancement of responses in object-based selective attention, (3) persistence of border ownership signals in accordance with continuity of object perception, and (4) remapping of border ownership signals across saccades and object movements. Findings 1 and 2 can be explained by hypothetical grouping circuits that sum contour feature signals in search of objectness, and, via recurrent projections, enhance the corresponding low-level feature signals. Findings 3 and 4 might be explained by assuming that the activity of grouping circuits persists and can be remapped. Grouping, persistence, and remapping are fundamental operations of vision. Finding these operations manifest in low-level visual areas challenges traditional views of visual processing. New computational models need to be developed for a comprehensive understanding of the function of the visual cortex.

Spike synchrony reveals emergence of proto-objects in visual cortex

Neurons at early stages of the visual cortex signal elemental features, such as pieces of contour, but how these signals are organized into perceptual objects is unclear. Theories have proposed that spiking synchrony between these neurons encodes how features are grouped (binding-by-synchrony), but recent studies did not find the predicted increase in synchrony with binding. Here we propose that features are grouped to "proto-objects" by intrinsic feedback circuits that enhance the responses of the participating feature neurons. This hypothesis predicts synchrony exclusively between feature neurons that receive feedback from the same grouping circuit. We recorded from neurons in macaque visual cortex and used border-ownership selectivity, an intrinsic property of the neurons, to infer whether or not two neurons are part of the same grouping circuit. We found that binding produced synchrony between same-circuit neurons, but not between other pairs of neurons, as predicted by the grouping hypothesis. In a selective attention task, synchrony emerged with ignored as well as attended objects, and higher synchrony was associated with faster behavioral responses, as would be expected from early grouping mechanisms that provide the structure for object-based processing. Thus, synchrony could be produced by automatic activation of intrinsic grouping circuits. However, the binding-related elevation of synchrony was weak compared with its random fluctuations, arguing against synchrony as a code for binding. In contrast, feedback grouping circuits encode binding by modulating the response strength of related feature neurons. Thus, our results suggest a novel coding mechanism that might underlie the proto-objects of perception.

A neural model of border-ownership from kinetic occlusion

Camouflaged animals that have very similar textures to their surroundings are difficult to detect when stationary. However, when an animal moves, humans readily see a figure at a different depth than the background. How do humans perceive a figure breaking camouflage, even though the texture of the figure and its background may be statistically identical in luminance? We present a model that demonstrates how the primate visual system performs figure-ground segregation in extreme cases of breaking camouflage based on motion alone. Border-ownership signals develop as an emergent property in model V2 units whose receptive fields are nearby kinetically defined borders that separate the figure and background. Model simulations support border-ownership as a general mechanism by which the visual system performs figure-ground segregation, despite whether figure-ground boundaries are defined by luminance or motion contrast. The gradient of motion- and luminance-related border-ownership signals explains the perceived depth ordering of the foreground and background surfaces. Our model predicts that V2 neurons, which are sensitive to kinetic edges, are selective to border-ownership (magnocellular B cells). A distinct population of model V2 neurons is selective to border-ownership in figures defined by luminance contrast (parvocellular B cells). B cells in model V2 receive feedback from neurons in V4 and MT with larger receptive fields to bias border-ownership signals toward the figure. We predict that neurons in V4 and MT sensitive to kinetically defined figures play a crucial role in determining whether the foreground surface accretes, deletes, or produces a shearing motion with respect to the background.

Remapping of border ownership in the visual cortex

We see objects as having continuity although the retinal image changes frequently. How such continuity is achieved is hard to understand, because neurons in the visual cortex have small receptive fields that are fixed on the retina, which means that a different set of neurons is activated every time the eyes move. Neurons in areas V1 and V2 of the visual cortex signal the local features that are currently in their receptive fields and do not show "remapping" when the image moves. However, subsets of neurons in these areas also carry information about global aspects, such as figure-ground organization. Here we performed experiments to find out whether figure-ground organization is remapped. We recorded single neurons in macaque V1 and V2 in which figure-ground organization is represented by assignment of contours to regions (border ownership). We found previously that border-ownership signals persist when a figure edge is switched to an ambiguous edge by removing the context. We now used this paradigm to see whether border ownership transfers when the ambiguous edge is moved across the retina. In the new position, the edge activated a different set of neurons at a different location in cortex. We found that border ownership was transferred to the newly activated neurons. The transfer occurred whether the edge was moved by a saccade or by moving the visual display. Thus, although the contours are coded in retinal coordinates, their assignment to objects is maintained across movements of the retinal image.

Figure–Ground Representation and Its Decay in Primary Visual Cortex

We used fMRI to study figure–ground representation and its decay in primary visual cortex (V1). Human observers viewed a motion-defined figure that gradually became camouflaged by a cluttered background after it stopped moving. V1 showed positive fMRI responses corresponding to the moving figure and negative fMRI responses corresponding to the static background. This positive–negative delineation of V1 “figure” and “background” fMRI responses defined a retinotopically organized figure–ground representation that persisted after the figure stopped moving but eventually decayed. The temporal dynamics of V1 “figure” and “background” fMRI responses differed substantially. Positive “figure” responses continued to increase for several seconds after the figure stopped moving and remained elevated after the figure had disappeared. We propose that the sustained positive V1 “figure” fMRI responses reflected both persistent figure–ground representation and sustained attention to the location of the figure after its disappearance, as did subjects' reports of persistence. The decreasing “background” fMRI responses were relatively shorter-lived and less biased by spatial attention. Our results show that the transition from a vivid figure–ground percept to its disappearance corresponds to the concurrent decay of figure enhancement and background suppression in V1, both of which play a role in form-based perceptual memory.

Dendritic slow dynamics enables localized cortical activity to switch between mobile and immobile modes with noisy background input

Mounting lines of evidence suggest the significant computational ability of a single neuron empowered by active dendritic dynamics. This motivates us to study what functionality can be acquired by a network of such neurons. The present paper studies how such rich single-neuron dendritic dynamics affects the network dynamics, a question which has scarcely been specifically studied to date. We simulate neurons with active dendrites networked locally like cortical pyramidal neurons, and find that naturally arising localized activity--called a bump--can be in two distinct modes, mobile or immobile. The mode can be switched back and forth by transient input to the cortical network. Interestingly, this functionality arises only if each neuron is equipped with the observed slow dendritic dynamics and with in vivo-like noisy background input. If the bump activity is considered to indicate a point of attention in the sensory areas or to indicate a representation of memory in the storage areas of the cortex, this would imply that the flexible mode switching would be of great potential use for the brain as an information processing device. We derive these conclusions using a natural extension of the conventional field model, which is defined by combining two distinct fields, one representing the somatic population and the other representing the dendritic population. With this tool, we analyze the spatial distribution of the degree of after-spike adaptation and explain how we can understand the presence of the two distinct modes and switching between the modes. We also discuss the possible functional impact of this mode-switching ability.

What can medical education learn from the neurobiology of learning?

The last several decades have seen a large increase in knowledge of the underlying biological mechanisms that serve learning and memory. The insights gleaned from neurobiological and cognitive neuroscientific experimentation in humans and in animal models have identified many of the processes at the molecular, cellular, and systems levels that occur during learning and the formation, storage, and recall of memories. Moreover, with the advent of noninvasive technologies to monitor patterns of neural activity during various forms of human cognition, the efficacy of different strategies for effective teaching can be compared. Considerable insight has also been developed as to how to most effectively engage these processes to facilitate learning, retention, recall, and effective use and application of the learned information. However, this knowledge has not systematically found its way into the medical education process. Thus, there are considerable opportunities for the integration of current knowledge about the biology of learning with educational strategies and curricular design. By teaching medical students in ways that use this knowledge, there is an opportunity to make medical education easier and more effective. The authors present 10 key aspects of learning that they believe can be incorporated into effective teaching paradigms in multiple ways. They also present recommendations for applying the current knowledge of the neurobiology of learning throughout the medical education continuum.

Analysis of the context integration mechanisms underlying figure-ground organization in the visual cortex

Most neurons in visual cortex respond to contrast borders and are orientation selective, and some are also selective for which side of a border is figure and which side is ground ("border ownership coding"). These neurons are influenced by the image context far beyond the classical receptive field (CRF) and as early as 25 ms after the onset of activity in the cortex. The nature of the fast context integration mechanism is not well understood. What parts of a figure contribute to the context effect? What is the structure of the "extraclassical surround"? Is the context information propagated through horizontal fibers within cortex or through reciprocal connections via higher-level areas? To address these questions, we studied border ownership modulation with fragmented figures. Neurons were recorded in areas V1 and V2 of Macaca mulatta under behaviorally induced fixation. Test figures were fragmented rectangles. While one edge was centered on the CRF, the presence of the fragments outside the CRF was varied. The surround fragments produced facilitation on the preferred border ownership side as well as suppression on the nonpreferred side, with approximately 80% of the locations contributing on average. Fragments far from the CRF influenced the responses even in the absence of fragments closer to the CRF, and without the extra delay that would incur from propagation through horizontal fibers. Three principally different models are discussed. The results support a model in which the antagonistic surround influences are produced by reentrant signals from a higher-level area.

Pre-exposure to moving form enhances static form sensitivity

Background

Motion-defined form can seem to persist briefly after motion ceases, before seeming to gradually disappear into the background. Here we investigate if this subjective persistence reflects a signal capable of improving objective measures of sensitivity to static form.

Methodology/Principal Findings

We presented a sinusoidal modulation of luminance, masked by a background noise pattern. The sinusoidal luminance modulation was usually subjectively invisible when static, but visible when moving. We found that drifting then stopping the waveform resulted in a transient subjective persistence of the waveform in the static display. Observers' objective sensitivity to the position of the static waveform was also improved after viewing moving waveforms, compared to viewing static waveforms for a matched duration. This facilitation did not occur simply because movement provided more perspectives of the waveform, since performance following pre-exposure to scrambled animations did not match that following pre-exposure to smooth motion. Observers did not simply remember waveform positions at motion offset, since removing the waveform before testing reduced performance.

Conclusions/Significance

Motion processing therefore interacts with subsequent static visual inputs in a way that can improve performance in objective sensitivity measures. We suggest that the brief subjective persistence of motion-defined forms that can occur after motion offsets is a consequence of the decay of a static form signal that has been transiently enhanced by motion processing.