Introduction

The function of the brain is a far cry from that of existing computer systems. Our knowledge of the information processing mechanism of the brain is rather limited, but we know that the brain consists of a large number of neurons and that the individual neuron can be regarded as a sort of threshold element. So, we can find some similarities between the neuron systems and the existing computer systems.

The similarity we are especially interested in is that both of them can be considered as aggregates of digital circuits. Any function of the digitial circuit which is independent of the previous state can be easily implemented by a two-level AND-to-OR gate network. In addition the threshold element which substitutes for a neuron performs the function of an AND gate or an OR gate according to its threshold value. Those facts inspired us with some ideas about our AND–OR analog of neuron networks. Our neuron network also has two levels. The first level of the network acts as an analyzer of the input patterns, and the second level acts as a generator of the output patterns. The operations assigned to the two levels correspond to those of the AND plane and the OR plane, respectively.

The function of a standard AND-to-OR gate network is determined by its inherent wiring. However, that of our neuron network is to be formed by the interaction with the given environment. The learning process of the network we assume is based on the biological hypothesis that the creatures which have neuron systems tend to avoid continuous and invariable stimuli, in other words, they are fond of moderate changes of the environment. For instance, the reflex including the avoidant behavior from danger should be explained by this hypothesis.