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A simple neural model of visual pattern discrimination is presented, based on the assumption that visual discrimination is determined by the differences in activity of a certain set of filters. These filters are the input units of a three-layer network. The units of the second layer have probabilities of activation that depend on the differences in activity of the input units and in turn they determine the probabilities of activation of a layer of output units from which probabilities of correct discrimination can be predicted. This model has been used to simulate discrete and continuous modes of curved-line discrimination and the results show good agreement with experimental data found in the literature.