Self-Organization in Artificial Intelligence and the Brain

Self-organization is one of the few theories that can explain significant aspects of developmental neuroscience. Within the brain itself, various spatially organized regions, or maps, exist that emerge dynamically. Theories and models that use self-organization have been successful at explaining such phenomena, and while these are not conclusive proof, they provide strong evidence in favor of self-organized mechanisms in the brain. Artificial Neural Networks have been developed that make use of these models to produce pattern recognition and classification mechanisms that have been used in widely diverse fields. This paper describes some of the models used to explain the emergence of various patterns and maps in the brain and their counterparts in the Neural Network domain. Widely used Neural Network algorithms include the Self-Organized Map and Adaptive Resonance Theory, that are discussed herein.