Neuromorphic Encoding/Decoding of Data Streams in Next–Generation Information Systems Inspired by Marr’s Computer Vision Paradigm

Interpreting the human visual system as a cascading sequence of an information system blocks, we, relying on the experience of developing information technologies (data encoding/decoding algorithms) and using specialized neurophysiological knowledge, have developed a model of an neuromorphic system, which in turn maximally imitates the perceptual mechanisms of the visual system. Essentially, we develop a neuromorphic system for processing data streams (registered counts) by extending Marr’s primal sketch paradigm with encoder blocks and implementing it at the algorithmic level. This article presents the results of our previous work in a more detailed and substantiated manner, as well as some new results. The results include a number of data representations internal to the neuromorphic system and algorithmic procedures designed to process them. Most of the obtained theoretical results are illustrated by the results of computer simulation.