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A new learning model is proposed, which is quite different from the usual ones in both function and structure. In the usual models only the classification function of input patterns is learned. On the other hand, the proposed learning machine can memorize input patterns themselves by a learning process. That is, an input pattern appears on the output plane of the machine if it has been presented to the machine often enough and does not appear if it has been presented infrequently. Since a man can remember or actually sketch patterns, he not only classifies but probably also memorizes patterns themselves by learning. This machine can therefore be said to simulate a phase of brain functioning. As for structure, the new machine consists of an iteration of nonlinear and spatially local processing instead of the usual single processing with linear discriminant functions. Both a general analysis and experimental results are given, and the machine is shown to have the expected behavior.