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As a prelude to the study of the adaptive-behavior and data-handling characteristics of natural and artificial neural networks, an analog model of a single neuron was developed. An analog computer was employed to reproduce the known electrical characteristics of a neuron in a qualitative simulation study of neuronal behavior. A self-imposed criterion of a minimum number of analog-computer components was adopted for the single-neuron simulation in anticipation of network studies. This criterion led to the development of a simple analog circuit which has qualitatively reproduced some of the known electrical characteristics of a neuron, such as: initiation of an action potential in the presence of suprathreshold depolarizing (or excitatory) inputs; strength-duration relationship; accommodation; refractoriness; rate of pulse repetition as a function of the input depolarization level; and prolonged action potentials. The simulation method adopted consisted of four major analog-computer components, three operational amplifiers and a single-shot multivibrator. The circuit developed has a linear forward path with a nonlinear form of feedback. A discussion is given of the properties exhibited by the simulated neuron with and without the nonlinear feedback. A mathematical method for linearly approximating nonlinear behavior is presented. This method is known as the "describing function technique," and its application to the analysis of neuronal behavior is described, as well as the technique's application to the synthesis of simulation networks.