Perceptual grouping is the process of identifying the constituents in the visual scene that together form a coherent object. The goal of this thesis is to understand the neural mechanisms of perceptual grouping. The hypotheses are that (1) perceptual grouping is carried out through synchronized firing of neurons representing the same object, and that (2) self-organized lateral connections encoding statistical regularities of the visual environment mediate such a synchronization. A self-organizing neural network of spiking neurons was developed to test these hypotheses in the perceptual grouping task of contour integration. The network self-organized orientation maps and patchy lateral connections similar to those found in the visual cortex, and the contour integration, segmentation, and completion performance measured by the degree of synchrony in neural populations accurately predicted human performance. Such results suggest that synchronized activity can represent perceptual events, and statistical properties of the input can shape the structure of the cortex and the perceptual performance. By providing a computational framework where perceptual performance and neural structure can be compared, the model helps us understand the neural mechanisms of perceptual grouping.