This method of parallel-hierarchical Q-transformation offers new approach to the creation of computing
medium - of parallel -hierarchical (PH) networks, being investigated in the form of model of neurolike scheme of data
processing [1-5]. The approach has a number of advantages as compared with other methods of formation of neurolike
media (for example, already known methods of formation of artificial neural networks). The main advantage of the
approach is the usage of multilevel parallel interaction dynamics of information signals at different hierarchy levels of
computer networks, that enables to use such known natural features of computations organization as: topographic nature
of mapping, simultaneity (parallelism) of signals operation, inlaid cortex, structure, rough hierarchy of the cortex,
spatially correlated in time mechanism of perception and training .
The information revolution, which was held in XIX centuries, considerably lifts intelligence of the man due to
storming development and accumulation of base of knowledge of mankind. Time comes and volumes of knowledge and
information grow in a geometrical progression, which requires of mankind of the increasing improvement of the
knowledge, which in turn will improve the technologies, developed by them, for maintenance of comfortable and
productive ability to live. During the certain time the mankind subjectively separated concepts of energy and
information. But the objective development provides by itself knowledge installed as single unit, where all is
interdependent and interconnected. Such understanding installed deduces mankind in a new plane of intelligence, which
derivates by it new tasks and opens new prospects.
Multistage integration of visual information in the brain allows people to respond quickly to most significant stimuli while preserving the ability to recognize small details in the image. Implementation of this principle in technical systems can lead to more efficient processing procedures. The multistage approach to image processing, described in this paper, comprises both main types of cortical multistage convergence. One of these types occurs within each visual pathway and the other between the pathways. This approach maps input images into a flexible hierarchy which reflects the complexity of the image data. The procedures of temporal image decomposition and hierarchy formation are described in mathematical terms. The multistage system highlights spatial regularities, which are passed through a number of transformational levels to generate a coded representation of the image which encapsulates, in a computer manner, structure on different hierarchical levels in the image. At each processing stage a single output result is computed to allow a very quick response from the system. The result is represented as an activity pattern, which can be compared with previously computed patterns on the basis of the closest match.