Neocognitron: A neural network model for a mechanism of visual pattern recognition

A neural network model, called a “neocognitron,” for a mechanism of visual pattern recognition was proposed earlier, and the result of computer simulation for a small-scale network was shown. A neocognitron with a larger-scale network is now simulated on a digital computer and is shown to have a great capability for visual pattern recognition: The neocognitron's ability to recognize handwritten Arabic numerals, even with considerable deformations in shape, is demonstrated. The neocognitron is a multilayered network consisting of a cascaded connection of many layers of cells. The information of the stimulus pattern given to the input layer is processed step by step in each stage of the multilayered network. A cell in a deeper layer generally has a tendency to respond selectively to a more complicated feature of the stimulus patterns and, at the same time, has a larger receptive field and is less sensitive to shifts in position of the stimulus patterns. Thus each cell of the deepest layer of the network responds selectively to a specific stimulus pattern and is not affected by the distortion in shape or the shift in position of the pattern. The synapses between the cells in the network are modifiable, and the neocognitron has a function of learning. A learning-with-a-teacher process is used to reinforce these modifiable synapses in the new model, instead of the learning-without-a-teacher process which was applied to the previous small-scale model.