Two types of auto-associative networks are well known, one based upon the correlation matrix and the other based upon the orthogonal projection matrix, both of which are calculated from the pattern vectors to be memorized. Although the latter type of networks have a desirable associative property compared to the former ones, they require, in conventional models, nonlocal calculation of the pattern vectors (i.e., pseudoinverse of a matrix) or some learning procedure based on the error correction paradigm. This paper proposes a new model of auto-associative networks in which the orthogonal projection is implemented in a special relation between the connections linking neuron-like elements. The connection weights can be determined by a Hebbian local learning, requiring no pseudoinverse calculation nor the error correction learning.
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