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A concept of the live unit as an automatic regulation system with a few admissible states areas in the space of states is considered. Energetic profit of oscillatory behavior consisting in the consecutive transitions of system from one admissible states area to another is shown. It is stated, that external disturbances cause the energy consumption of oscillatory system to decrease. On the basis of this concept and some neurophysiological data, the "live" energy-consuming nonlinear three-state neuron model is proposed and the existence of energy optimal generation frequency v(opt) is proved. For the realization of tendency to v(opt) the optimal learning rule is proposed, which provides unsupervised learning and interlinked short-term and long-term memories with forgetting. The model proposed explains the genesis of neural network, is promising in the sense of network self-organization and allows to solve the problem of internal activity in the researches on artificial intelligence.

The "live" neural network model is proposed on the basis of "live" neuron model and optimal learning rule. By means of numerical simulation the initial stages of neural network self-organization have been shown: (1) the formation of two activity forms, which are identified with sleep and awaking, and (2) the self-organization of hierarchical associative memory when feeding a receptor excitation to the neural network. The energetic profit of self-organization is demonstrated. The formation of neural ensembles, playing the role of generalized neurons, is obtained.