Subject of study. This study investigates the synthesis of data-processing and analysis procedures in the form of event streams for image encoding and decoding tasks, based on the principles of neuroiconics. Aim of study. The aims of this study are to develop a model for neuromorphic encoding and subsequent decoding of video data, synthesize procedures for image recovery, and create an algorithm for object-boundary detection in digital images. Method. A sampling representation of images is employed. A generative encoder model is constructed based on the statistics of this sampling representation, representing the joint distribution of the input and encoded data as a mixture of components. A system of receptive fields is introduced to model lateral inhibition mechanisms. Main results. The mechanisms of primary neuromorphic processing of video data in the peripheral visual system are modeled, and neuromorphic procedures for image encoding and reconstruction are synthesized. Numerical testing and optimization of the developed algorithms demonstrate the feasibility of avoiding computational difficulties associated with large-scale data processing and show the adaptability of the proposed approach to modern neural network tasks. Practical significance. The synthesized procedures can be applied to modern communication systems and related tasks such as object search, identification, and recognition in digital images. Furthermore, the proposed approach may serve as a foundation for the analysis and synthesis of other neuromorphic information systems designed to operate on data streams.
Antsiperov et al. (Wed,) studied this question.