Many companies and institutions in their attempts construct decision-making system, face a bottleneck in performance of their systems. Training neural networks can take from several days to several weeks. The traditional approach suggests modification of modern systems and microcircuits as long as their performance reaches a permissible limit. A different approach, unconventional, looks for opportunities in computing inspired by the human brain, neuromorphic computing. The idea was proposed by the engineer Carver Mead in the 80s and suggests combining artificial neural networks with specialized microcircuits. The architecture of the microchip needs to reproduce the mechanisms of the human brain and to be a kind of hardware support for neural networks. Last decade is characterized by a sharp growth of interest in neuromorphic computing, human brain modeling and peculiarities of how it works during making decisions. This is evidenced by the launch of a large-scale research programs like DARPA SyNAPSE (USA) and the Human Brain Project (EU), the purpose of which is to build a microprocessor system, which resembles the human brain in functionality, size and energy consumption. Existing models of the brain even on powerful supercomputers require significant computation time and are not yet able to solve problems in real time. Since the human brain consists of two parts with different functions and different data processing principles, there is a very promising approach which suggests combining digital and analog systems into single one. In current collaboration we incorporate some results of study of activity of human brain as a base of building of hybrid computational system and foundation to the approach of running it.