A fuzzy-like phenomenon in a dynamic neural network is demonstrated and analyzed. The network operates as a dynamic associative memory. Each neuron of the dynamic neural network has an all-or-none output and exponentially decaying refractoriness. When several related patterns are stored in the dynamic neural network and an external stimulus with a property shared by two of the stored patterns is applied to the neural network, the output of the neural network dynamically transits between the two stored patterns. The frequency ratio that the network visits the two stored patterns is dependent on the ratio of the Hamming distances between the external pattern and the two stored patterns. This phenomenon is similar to the human decision-making process, some of which characteristics can be described by fuzzy set theory. A framework for the analysis of this phenomenon is proposed, and is used to derive sufficient conditions which ensure the dynamical transition between the two stored patterns. The properties of the transition in the network are also discussed.
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