Abstract|We present an object recognition system based

This article describes an integrated theory of analogical access and mapping, instantiated in a

We study 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 potential for each oscillator so that only those oscillators with strong connections from their neighborhood can develop high potentials. Based on the concept of potential, a solution to remove noisy regions in an image is proposed for LEGION, so that it suppresses the oscillators corresponding to noisy regions, without affecting those corresponding to major regions. We show analytically that the resulting oscillator network separates an image into several major regions, plus a background consisting of all noisy regions, and illustrate network properties by computer simulation. The network exhibits a natural capacity in segmenting images. The oscillatory dynamics leads to a computer algorithm, which is applied successfully to segmenting real graylevel 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. DeLiang Wang and David Terman Image Segmentation 1.

Present and permanent address: Physik-Department der TU Munchen Exploiting local stability we show what neuronal characteristics are essential to ensure that coherent oscillations are asymptotically stable in a spatially homogeneous network of spiking neurons. Under standard conditions, a necessary and in the limit of a large number of interacting neighbors also sufficient condition is that the postsynaptic potential is increasing in time as the neurons fire. If the postsynaptic potential is decreasing, oscillations are bound to be unstable. This is a kind of locking theorem and boils down to a subtle interplay of axonal delays, postsynaptic potentials, and refractory behavior. The theorem also allows for mixtures of excitatory and inhibitory interactions. On the basis of the locking theorem we present a simple geometric method to verify existence and local stability of a coherent oscillation. 2 1

We present a cortical-based model for computing the perceptual salience of contours embedded in noisy images. It has been suggested (Gilbert, 1992; Field, Hayes & Hess, 1993) that horizontal intra-cortical connections in primary visual cortex may modulate contrast detection thresholds and pre-attentive "popout ". In our model, horizontal connections mediate context-dependent facilitatory and inhibitory interactions among oriented cells. Strongly facilitated cells undergo temporal synchronization; and perceptual salience is determined by the level of synchronized activity. The model accounts for a range of reported psychophysical and physiological effects of contour salience (Polat & Sagi, 1993, 1994; Kapadia, Ito, Gilbert & Westheimer, 1995; Field et al., 1993; Kovács, Polat & Norcia, 1996; Pettet, McKee & Grzywacz, 1996). In particular, the model proposes that intrinsic properties of synchronization account for the increased salience of smooth, closed contours (Kovács & Julesz, 1993, ...

The RF-SLISSOM model integrates two separate lines of research on computational modeling of the visual cortex. Laterally connected self-organizing maps have been used to model how afferent structures such as orientation columns and patterned lateral connections can simultaneously self-organize through input-driven Hebbian adaptation. Spiking neurons with leaky integrator synapses have been used to model image segmentation and binding by synchronization and desynchronization of neuronal group activity. Although these approaches differ in how they model the neuron and what they explain, they share the same overall layout of a laterally connected two-dimensional network. This paper shows how both self-organization and segmentation can be achieved in such an integrated network, thus presenting a unified model of development and functional dynamics in the primary visual cortex. 1 Introduction Several models of the visual cortex that take into account lateral interactions between neurons hav...

Based on a Hebbian adaptation process, the afferent and lateral connections in the RF-LISSOM model organize simultaneously and cooperatively, and form structures such as those observed in the primary visual cortex. The neurons in the model develop local receptive fields that are organized into orientation, ocular dominance, and size selectivity columns. At the same time, patterned lateral connections form between neurons that follow the receptive field organization. This structure is in a continuously-adapting dynamic equilibrium with the external and intrinsic input, and can account for reorganization of the adult cortex following retinal and cortical lesions. The same learning processes may be responsible for a number of low-level functional phenomena such as tilt aftereffects, and combined with the leaky integrator model of the spiking neuron, for segmentation and binding. The model can also be used to verify quantitatively the hypothesis that the visual cortex forms a sparse, redun...

A network of Wilson-Cowan oscillators is constructed, and its emergent properties of synchronization and desynchronization are investigated by both computer simulation and formal analysis. The network is a two-dimensional matrix, where each oscillator is coupled only to its neighbors. We show analytically that a chain of locally coupled oscillators (the piece-wise linear approximation to the Wilson-Cowan oscillator) synchronizes, and 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 sy...

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