Substantial physiological evidence indicates that neuron thresholds and synaptic weights in living creatures are adjusted by mechanisms quite different from those that have ordinarily been proposed in neural net investigations. This paper presents a theoretical model of the plastic neuron in which threshold and synaptic weights are adjusted solely on the basis of the time history of afferent and efferent activity of the neuron. Physiological, psychological and mathematical evidence is presented which supports the postulate that each neuron in living creatures is an autonomous, dynamically self-adjusting unit which is advised (not directed) by higher centers during the adjustnent process. The model duplicates much of the behavior of neurons in experimental preparations, and simulations of small nets have yielded learning behavior apparently similar in some respects to that of living creatures.