Ken Yamane

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A key to overcoming the limitations of classical artificial intelligence and to deal well with enormous amounts of information might be brain-like computing in which distributed representations of information are processed by dynamical systems without using symbols. We present a method for such computing. We constructed an inference system using a(More)
Dynamics of traditional neural network models are generally time-invariant. For that reason, they have limitations in context-dependent processing. We present a new method, dynamic desensitiza-tion, of varying neurodynamics continuously and construct a basic model of interaction between neurodynamical systems. This model comprises two nonmonotone neural(More)
It is considered that a key to overcoming the limitations of classical artificial intelligence is to process distributed representations of information without symbolizing them. However, conventional neural networks require local or symbolic representations to perform complicated processing. Here we present a brain-like inference engine consisting of a(More)
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