Sidney R. Lehky

An astable multivibrator model of binocular rivalry.

The behavior of a neural network model for binocular rivalry is explored through the development of an analogy between it and an electronic astable multivibrator circuit. The model incorporates reciprocal feedback inhibition between signals from the left and the right eyes prior to binocular convergence. The strength of inhibitory coupling determines whether the system undergoes rivalrous oscillations or remains in stable fusion: strong coupling leads to oscillations, weak coupling to fusion. This implies that correlation between spatial patterns presented to the two eyes can affect the strength of binocular inhibition. Finally, computer simulations are presented which show that a reciprocal inhibition model can reproduce the stochastic behavior of rivalry. The model described is a counterexample to claims that reciprocal inhibition models as a class cannot exhibit many of the experimentally observed properties of rivalry.

Neural network model of visual cortex for determining surface curvature from images of shaded surfaces

The visual system can extract information about shape from the pattern of light and dark surface shading on an object. Very little is known about how this is accomplished. We have used a learning algorithm to construct a neural network model that computes the principal curvatures and orientation of elliptic paraboloids independently of the illumination direction. Our chief finding is that receptive fields developed by units of such model network are surprisingly similar to some found in the visual cortex. It appears that neurons that can make use of the continuous gradations of shading have receptive fields similar to those previously interpreted as dealing with contours (i. e. ‘bar’ detectors or ‘edge’ detectors). This study illustrates the difficulty of deducing neuronal function within a network solely from receptive fields. It is also important to consider the pattern of connections a neuron makes with subsequent stages, which we call the ‘projective field’.

Selectivity and sparseness in the responses of striate complex cells

Probability distributions of macaque complex cell responses to a large set of images were determined. Measures of selectivity were based on the overall shape of the response probability distribution, as quantified by either kurtosis or entropy. We call this non-parametric selectivity, in contrast to parametric selectivity, which measures tuning curve bandwidths. To examine how receptive field properties affected non-parametric selectivity, two models of complex cells were created. One was a standard Gabor energy model, and the other a slight variant constructed from a Gabor function and its Hilbert transform. Functionally, these models differed primarily in the size of their DC responses. The Hilbert model produced higher selectivities than the Gabor model, with the two models bracketing the data from above and below. Thus we see that tiny changes in the receptive field profiles can lead to major changes in selectivity. While selectivity looks at the response distribution of a single neuron across a set of stimuli, sparseness looks at the response distribution of a population of neurons to a single stimulus. In the model, we found that on average the sparseness of a population was equal to the selectivity of cells comprising that population, a property we call ergodicity. We raise the possibility that high sparseness is the result of distortions in the shape of response distributions caused by non-linear, information-losing transforms, unrelated to information theoretic issues of efficient coding.

Shape Selectivity in Primate Frontal Eye Field

Previous neurophysiological studies of the frontal eye field (FEF) in monkeys have focused on its role in saccade target selection and gaze shift control. It has been argued that FEF neurons indicate the locations of behaviorally significant visual stimuli and are not inherently sensitive to specific features of the visual stimuli per se. Here, for the first time, we directly examined single cell responses to simple, two-dimensional shapes and found that shape selectivity exists in a substantial number of FEF cells during a passive fixation task or during the sample, delay (memory), and eye movement periods in a delayed match to sample (DMTS) task. Our data demonstrate that FEF neurons show sensory and mnemonic selectivity for stimulus shape features whether or not they are behaviorally significant for the task at hand. We also investigated the extent and localization of activation in the FEF using a variety of shape stimuli defined by static or dynamic cues employing functional magentic resonance imaging (fMRI) in anesthetized and paralyzed monkeys. Our fMRI results support the electrophysiological findings by showing significant FEF activation for a variety of shape stimuli and cues in the absence of attentional and motor processing. This shape selectivity in FEF is comparable to previous reports in the ventral pathway, inviting a reconsideration of the functional organization of the visual system.

Organization of binocular pathways: Modeling and data related to rivalry

It is proposed that inputs to binocular cells are gated by reciprocal inhibition between neurons located either in the lateral geniculate nucleus or in layer 4 of striate cortex. The strength of inhibitory coupling in the gating circuitry is modulated by layer 6 neurons, which are the outputs of binocular matching circuitry. If binocular inputs are matched, the inhibition is modulated to be weak, leading to fused vision, whereas if the binocular inputs are unmatched, inhibition is modulated to be strong, leading to rivalrous oscillations. These proposals are buttressed by psychophysical experiments measuring the strength of adaptational aftereffects following exposure to an adapting stimulus visible only intermittently during binocular rivalry.

Fine Discrimination of Faces can be Performed Rapidly

Here we measure the smallest change in a face that can be discriminated. A morphing algorithm mixed two faces in variable proportions to create a series of synthetic faces that each differed by a tiny amount. By selecting from this series, a test face could be chosen so as to reach a just noticeable difference from a sample face. Face-discrimination thresholds were about 7 of the average difference between two faces, as quantified by coefficients of a principal components decomposition. This threshold remained constant as the duration of the test face was reduced from 1,000 to 100 msec, and rose quickly for shorter stimulus durations. The behavioral evidence presented here indicates that complex visual processing can be completed within the first 100 msec of the signal, suggesting involvement of feedforward neural mechanisms, and placing constraints on possible computational algorithms employed within the ventral visual pathways.

Population Coding of Visual Space: Comparison of Spatial Representations in Dorsal and Ventral Pathways

Although the representation of space is as fundamental to visual processing as the representation of shape, it has received relatively little attention from neurophysiological investigations. In this study we characterize representations of space within visual cortex, and examine how they differ in a first direct comparison between dorsal and ventral subdivisions of the visual pathways. Neural activities were recorded in anterior inferotemporal cortex (AIT) and lateral intraparietal cortex (LIP) of awake behaving monkeys, structures associated with the ventral and dorsal visual pathways respectively, as a stimulus was presented at different locations within the visual field. In spatially selective cells, we find greater modulation of cell responses in LIP with changes in stimulus position. Further, using a novel population-based statistical approach (namely, multidimensional scaling), we recover the spatial map implicit within activities of neural populations, allowing us to quantitatively compare the geometry of neural space with physical space. We show that a population of spatially selective LIP neurons, despite having large receptive fields, is able to almost perfectly reconstruct stimulus locations within a low-dimensional representation. In contrast, a population of AIT neurons, despite each cell being spatially selective, provide less accurate low-dimensional reconstructions of stimulus locations. They produce instead only a topologically (categorically) correct rendition of space, which nevertheless might be critical for object and scene recognition. Furthermore, we found that the spatial representation recovered from population activity shows greater translation invariance in LIP than in AIT. We suggest that LIP spatial representations may be dimensionally isomorphic with 3D physical space, while in AIT spatial representations may reflect a more categorical representation of space (e.g., “next to” or “above”).

Neural Models of Binocular Depth Perception

We have developed a model for the representation of stereo disparity by using a population of neurons that is based on tuning curves similar in shape to those measured physiologically (Lehky and Sejnowski 1990). The model predicts depth discrimination thresholds that agree with human psychophysical data only when the population size representing disparity in a small patch of visual field was in the range of about 20-200 units. This population model of disparity coding at a single spatial location was extended to include lateral interactions, as suggested by psychophysical data on stereo interpolation (Westheimer 1986a). Disparity is a measure of depth relative to the plane of fixation. Additional sources of information are needed to estimate the distance to the point of fixation, such as that provided by eye vergence, vertical disparity, and accommodation. We have developed a simple model that combines disparity information in a distributed representation and vergence information to compute the absolute depth of objects from the observer (Pouget and Sejnowski 1990). Although these are models of binocular vision, a number of the ideas presented here generalize to the representations of other sensory cues.

Neural representation for object recognition in inferotemporal cortex.

We suggest that population representation of objects in inferotemporal cortex lie on a continuum between a purely structural, parts-based description and a purely holistic description. The intrinsic dimensionality of object representation is estimated to be around 100, perhaps with lower dimensionalities for object representations more toward the holistic end of the spectrum. Cognitive knowledge in the form of semantic information and task information feed back to inferotemporal cortex from perirhinal and prefrontal cortex respectively, providing high-level multimodal-based expectations that assist in the interpretation of object stimuli. Integration of object information across eye movements may also contribute to object recognition through a process of active vision.

Spatial Modulation of Primate Inferotemporal Responses by Eye Position

BACKGROUND A key aspect of representations for object recognition and scene analysis in the ventral visual stream is the spatial frame of reference, be it a viewer-centered, object-centered, or scene-based coordinate system. Coordinate transforms from retinocentric space to other reference frames involve combining neural visual responses with extraretinal postural information. METHODOLOGY/PRINCIPAL FINDINGS We examined whether such spatial information is available to anterior inferotemporal (AIT) neurons in the macaque monkey by measuring the effect of eye position on responses to a set of simple 2D shapes. We report, for the first time, a significant eye position effect in over 40% of recorded neurons with small gaze angle shifts from central fixation. Although eye position modulates responses, it does not change shape selectivity. CONCLUSIONS/SIGNIFICANCE These data demonstrate that spatial information is available in AIT for the representation of objects and scenes within a non-retinocentric frame of reference. More generally, the availability of spatial information in AIT calls into questions the classic dichotomy in visual processing that associates object shape processing with ventral structures such as AIT but places spatial processing in a separate anatomical stream projecting to dorsal structures.