Abstract
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.
Original language | English (US) |
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Title of host publication | IEEE Int Conf on Neural Networks |
Publisher | Publ by IEEE |
Pages | 109-116 |
Number of pages | 8 |
State | Published - 1988 |
ASJC Scopus subject areas
- Engineering(all)