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We describe a decirculation process which marks perturbations of network structure and neural updating that are necessary for evolutionary neural networks to proceed from one circulating state to another. Two aspects of control parameters, screen updating and flow diagrams, are developed to quantify such perturbations, and hence to manage the dynamics of evolutionary neural networks. A dynamic state-shifting algorithm is derived from the decirculation process. This algorithm is used to build models of evolutionary content-addressable memory (ECAM) networks endowed with many dynamic relaxation processes. By the training of ECAM networks based on the dynamic state-shifting algorithm, we obtain the classification of training samples and the construction of recognition mappings, both of which perform adaptive computations essential to CAM.