Эта разработка Токийского университета представляет собой самоорганизующиеся сети пороговых элементов, способные запоминать сложные последовательности данных. Модель ИИ имитирует естественные процессы памяти, превращая внешние стимулы в устойчивые состояния нейронной сети.
Various information-processing capabilities of self-organizing nets of threshold elements are studied. A self-organizing net, learning from patterns or pattern sequences given from outside as stimuli, "remembers" some of them as stable equilibrium states or state-transition sequences of the net. A condition where many patterns and pattern sequences are remembered in a net at the same time is shown. The stability degree of their remembrance and recalling under noise disturbances is investigated theoretically. For this purpose, the stability of state transition in an autonomous logical net of threshold elements is studied by the use of characteristics of threshold elements.