Visual perceptual grouping is a process of identifying constituents that together form a group. In this project, a self-organizing map of spiking neurons was developed to understand the neural mechanisms of perceptual grouping. Grouping events were represented by the degree of synchrony among neural populations in the model, and the connection weights were self-organized to capture statistical regularities in the input. The resulting connection structure was consistent with experimental findings, and the functional performance of the model matched human performance in contour integration tasks. Furthermore, the same model was able to account for segmentation of multiple contours and contour filling-in. Altered input distribution caused the structural properties to differ among different areas in the model cortex, and as a result, functional performance also differed. Such a result provides a possible developmental explanation for similar psychophysical results.