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In a previous work, the authors proposed an analog Hopfield-type neural network that identified the K largest components of a list of real numbers. In this work, we identify computable restrictions on the parameters, in order that the network can repeatedly process lists, one after the other, at a given rate. A complete mathematical analysis gives analytical bounds for the time required in terms of circuit parameters, the length of the lists, and the relative separation of list elements. This allows practical setting of circuit parameters for required clocking times. The emphasis is on high gain functioning of each neuron. Numerical investigations show the accuracy of the theoretical predictions, and study the influence of various parameters on performance.