In recent years, interest in artificial intelligence based on neural network approaches has grown significantly. A number of significant scientific results have been obtained that have found wide application in practice. Generative adversarial neural network models, neural network transformers, and other solutions have attracted much attention. Obvious progress has been achieved in neural network recognition and image generation, text and speech processing, event forecasting, and control of processes that are difficult to formalize. However, it has not yet been possible to endow neural network machines with thinking. All results obtained using neural network machines can be attributed to solutions based on various types of signal binding without full control of their processing processes. Typical representatives of such machines are ChatGPT. The capabilities for intelligently operating various signals in known neural network machines are very limited. Among the main reasons for such limitations, one should highlight the imperfection of the basic principles of neural network information processing used. The properties of neurons have long been considered in a simplified manner. This was due to both gaps in the field of biological research and the lack of opportunities to build large neural networks on complex neuron models. In recent years the situation has changed. New ways to implement large neural networks have emerged. It has also been established that even individual neurons can have extensive internal memory and implement various functions. However, many mechanisms of neuron functioning and their interactions still remain unclear. The issues of controlled associative access to the internal memory of neurons have been little studied. These shortcomings significantly hinder the creation of thinking neural network machines. The object of research in the article is the process of intelligent neural network information processing. The subject of research: principles, models, and methods of such processing. The goal is to expand the functionality of neural network machines to solve difficult-to-formalize creative problems through the development of new principles, models, and methods of intelligent information processing. In the interests of achieving this goal, the operating principles of intelligent neural network machines are clarified, and new models and methods of neural network information processing are proposed. A new model of a pulse neuron is revealed as a basic element of such machines. It is recommended to form the artificial brain of neural network machines in the form of multilayer neural networks endowed with logical structures with neurons of different parameters. A new method of multi-level intelligent information processing in neural network machines based on smart impulse neurons is proposed. The mechanisms of thinking of neural network machines, and the underlying functions of intellectual operation of images and concepts in neural network memory are explained. Simulation results are presented that confirm the validity of the proposed solutions.
