Studies on the formation of intellectual concepts with neural network models.

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In the cerebral cortex, it is assumed that information is represented in the activity pattern of an assembly of neurons. In this study, in order to investigate the formation of intellectual concepts, such a distributed representation was incorporated in the output layr of a neural network with the error-back-propagation algorithm. The network recognized the learned data with complete accuracy. Several units of the hidden layr responded to a series of related data. The network showed high tolerance for the inactivation of the hidden layr units. In addition, it recognized the newly constructed data with high accuracy. These results showed some advantages of the formation of intellectual concepts with a distributed representation. Furthermore, according to the current knowledge of cognitive neuroscience, a recurrent neural network model for controlling multiple intelligence was proposed. The model had multiple neural networks for multiple intelligence. Information from multiple inputs was integrated in the higher association layr through secondary sensory layrs. The output of the higher association layr was transmitted to the filter layr, and the filter provided each secondary sensory layr with each momentary bias, which directed the successive expression of intelligence. The recurrent neural network learned the learning data set with the recurrent back-propagation algorithm. After learning, it was found that this network processed input data according to the previous command. The information representation in the higher association layr may be regarded as the intellectual concepts and will be analyzed in further studies.