Oscillatory binocular system and temporal segmentation of stereoscopic depth surfaces

Synchronization and desynchronization in a network of locally coupled Wilson-Cowan oscillators

A network of Wilson-Cowan (WC) oscillators is constructed, and its emergent properties of synchronization and desynchronization are investigated by both computer simulation and formal analysis. The network is a 2D matrix, where each oscillator is coupled only to its neighbors. We show analytically that a chain of locally coupled oscillators (the piecewise linear approximation to the WC oscillator) synchronizes, and we present a technique to rapidly entrain finite numbers of oscillators. The coupling strengths change on a fast time scale based on a Hebbian rule. A global separator is introduced which receives input from and sends feedback to each oscillator in the matrix. The global separator is used to desynchronize different oscillator groups. Unlike many other models, the properties of this network emerge from local connections that preserve spatial relationships among components and are critical for encoding Gestalt principles of feature grouping. The ability to synchronize and desynchronize oscillator groups within this network offers a promising approach for pattern segmentation and figure/ground segregation based on oscillatory correlation.

A neural model of contour integration in the primary visual cortex

Experimental observations suggest that contour integration may take place in V1. However, there has yet to be a model of contour integration that uses only known V1 elements, operations, and connection patterns. This article introduces such a model, using orientation selective cells, local cortical circuits, and horizontal intracortical connections. The model is composed of recurrently connected excitatory neurons and inhibitory interneurons, receiving visual input via oriented receptive fields resembling those found in primary visual cortex. Intracortical interactions modify initial activity patterns from input, selectively amplifying the activities of edges that form smooth contours in the image. The neural activities produced by such interactions are oscillatory and edge segments within a contour oscillate in synchrony. It is shown analytically and empirically that the extent of contour enhancement and neural synchrony increases with the smoothness, length, and closure of contours, as observed in experiments on some of these phenomena. In addition, the model incorporates a feedback mechanism that allows higher visual centers selectively to enhance or suppress sensitivities to given contours, effectively segmenting one from another. The model makes the testable prediction that the horizontal cortical connections are more likely to target excitatory (or inhibitory) cells when the two linked cells have their preferred orientation aligned with (or orthogonal to) their relative receptive field center displacements.

Image segmentation based on oscillatory correlation

We study the image segmentation on the basis of locally excitatory, globally inhibitory oscillator networks (LEGION), whereby the phases of oscillators encode the binding of pixels. We introduce a lateral potential for each oscillators so that only oscillators with strong connections from their neighborhood can develop high potentials. Based on the concept of the lateral potential, a solution to remove noisy regions in an image is proposed for LEGION, so that it suppresses the oscillators corresponding to noisy regions but without affecting those corresponding to major regions. We show that the resulting oscillator network separates an image into several major regions, plus a background consisting of all noisy regions, and we illustrate network properties by computer stimulation. The network exhibits a natural capacity in segmenting images. The oscillatory dynamics leads to a computer algorithm, which is applied successfully to segmenting real gray-level images. A number of issues regarding biological plausibility and perceptual organization are discussed. We argue that LEGION provides a novel and effective framework for image segmentation and figure-ground segregation.

The Role of Neuronal Synchronization in Response Selection: A Biologically Plausible Theory of Structured Representations in the Visual Cortex

Recent experimental results in the visual cortex of cats and monkeys have suggested an important role for synchronization of neuronal activity on a millisecond time scale. Synchronization has been found to occur selectively between neuronal responses to related image components. This suggests that not only the firing rates of neurons but also the relative timing of their action potentials is used as a coding dimension. Thus, a powerful relational code would be available, in addition to the rate code, for the representation of perceptual objects. This could alleviate difficulties in the simultaneous representation of multiple objects. In this article we present a set of theoretical arguments and predictions concerning the mechanisms that could group neurons responding to related image components into coherently active aggregates. Synchrony is likely to be mediated by synchronizing connections; we introduce the concept of an interaction skeleton to refer to the subset of synchronizing connections that are rendered effective by a particular stimulus configuration. If the image is segmented into objects, these objects can typically be segmented further into their constituent parts. The synchronization behavior of neurons that represent the various image components may accurately reflect this hierarchical clustering. We propose that the range of synchronizing interactions is a dynamic parameter of the cortical network, so that the grain of the resultant grouping process may be adapted to the actual behavioral requirements.

It can be argued that different aspects of purposeful behavior rely on separable processes by which sensory input is transformed into adjustments of motor activity. Indeed, neurophysiological evidence has suggested separate processing streams originating in the primary visual cortex for object identification and sensorimotor coordination. However, such a separation calls for a mechanism that avoids interference effects in the presence of multiple objects, or when multiple motor programs are simultaneously prepared. In this article we suggest that synchronization between responses of neurons in both the visual cortex and in areas that are involved in response selection and execution might allow for a selective routing of sensory information to the appropriate motor program.

Binding in models of perception and brain function

The development of the concept of feature binding as fundamental to neural dynamics has made possible recent advances in the modeling of difficult problems of perception and brain function. Major weaknesses of past neural modeling (most prominently its inability to work with natural stimuli and its 'learning-time' barrier) have been traced back to improper treatment of the binding issue. Signal synchrony is now seen as playing a major role in binding. Inclusion of temporal binding in neural models has led to recent breakthroughs in solving important perceptual problems. Among them is perceptual segmentation, invariant object recognition and natural language parsing, as well as overcoming the 'learning-time' barrier.

Stereo vision by self-organization

We propose a new algorithm for stereoscopic depth perception, where the depth map is the momentary state of a dynamic process. To each image point we assign a set of possible disparity values. In a dynamic process with competition and cooperation, the correct disparity value is selected for each image point. Therefore, we solve the correspondence problem by a dynamic, self-organizing process, the structure of which shows analogies to the human visual system. The algorithm can be implemented in a massive parallel manner and yields good results for either artificial or natural images.