Modified neocognitron for improved 2-D pattern recognition

Some modifications to an existing neural network, the neocognitron, are proposed in order to overcome some of its limitations and to achieve an improved recognition of patterns (for instance, characters). Motivation for the present work arose from the results of extensive simulation experiments on the neocognitron. Inhibition during training is dispensed with, including it only during the testing phase of the neocognitron. Even during testing, inhibition is totally discarded in the initial layer because it leads, otherwise, to some undesirable results. However, inhibition, which is feature-based, is incorporated in the later stages. The number of network parameters which are to be set manually during training is reduced. The training is made simple without involving multiple training patterns of the same nature. A new layer has been introduced after the C-layer (of the neocognitron) to scale down the network size. Finally, the response of the S-cell has been simplified, and the blurring operation between the S- and the C-layers has been changed. The new architecture, which is robust with respect to small variations in the value of the network parameters, and the associated training are believed to be simpler and more efficient than those of the neocognitron