Percognitron: Neocognitron coupled with perceptron

Proposes two neural network models, Percognitron I and II, for position- and deformation-invariant visual pattern-recognition systems. The number of synapses between the Us4 and Uc4 levels of the Neocognitron are increased to achieve full interconnection among the nodes of the two levels. Then, Percognitron I is used to adapt the excitatory synapses between the Us4 and Uc4 levels using a single-layer perceptron-type adaptation. Percognitron II is then used to adapt the excitatory synapses between the Us4 and Uc4 levels and between the Us4 and Uc3 levels using a backpropagation-type adaptation. The rate of adaptation is controlled with a user-supplied gain factor for each level that is adapted. The autonomy of the Percognitrons having a fully interconnected fourth layer is briefly illustrated in comparison to D.H. Hubel and T.N. Wiesel´s (1962, 1965) hierarchical model. The Percognitron is shown to effectively recognize handwritten Arabic numerals. The proposed approach can successfully recognize a greater variety of patterns, including distorted or shifted patterns, than the Neocognitron