Kohonen´s self-organizing maps: exploring their computational capabilities

The authors demonstrate that the computational capabilities of Kohonen´s algorithm provide an unified approach to such diverse fields as sensory mappings, combinatorial optimization, and learning in motor control. For a discrete probability distribution of the training inputs, the formation of the mapping can be described as a probabilistic descent in a potential. In view of their wide applicability, the principles of the algorithm might also be inherent to the maturation of biological brains and could help to achieve a better understanding of these processes from a more unified point of view.<>