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A novel model of autoassociative memory and its self-organization

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1 Author(s)
K. Matsuoka ; Div. of Control Eng., Kyushu Inst. of Technol., Japan

A network is presented which embodies the orthogonal type of association. A remarkable point of the network is that the weights of the connections between neurons can be determined directly from the correlation matrix derived from the prototype patterns, requiring no pseudoinverse calculation. As a result, the connection weights can also be obtained by an unsupervised, local learning procedure based on the conventional Hebbian principle

Published in:

IEEE Transactions on Systems, Man, and Cybernetics  (Volume:21 ,  Issue: 3 )