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Self-Organization with Lateral Connections

A self-organizing neural network model for the development of afferent and lateral input connections in cortical feature maps is presented. The weight adaptation process is purely activity-dependent, unsupervised, and local. The afferent input weights self-organize into a topological map of the input space. At the same time, the lateral interaction weights develop a smooth "Mexican hat" shaped distribution. Weak lateral connections die off, leaving a pattern of connections that represents the significant long-term correlations of activity on the feature map. The model demonstrates how self-organization can bootstrap itself based on input information only, without global supervision or predetermined lateral interaction. The model can potentially account for experimental observation such as critical periods for self-organization in cortical maps and development of horizontal connections in the primary visual cortex.