Abstract
Summary form only given. Competitive Hebbian learning, a modified Hebbian-learning rule, is introduced. In competitive Hebbian learning the change in each connection weight is made proportional to the product of node and input activities multiplied by a factor which decreases with increasing activity on the other nodes. The individual nodes learn to respond to different components of the input activity while collectively developing maximal response. Several applications of competitive Hebbian learning were presented to show examples of the power and versatility of this learning algorithm. >
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