This paper presents a new method for evaluating the synchronization of
quasi-periodic oscillations of two oscillators, termed "chimeric
synchronization". The family of metrics is proposed to create a neural network
information converter based on a network of pulsed oscillators. In addition to
transforming input information from digital to analogue, the converter can
perform information processing after training the network by selecting control
parameters. In the proposed neural network scheme, the data arrives at the
input layer in the form of current levels of the oscillators and is converted
into a set of non-repeating states of the chimeric synchronization of the
output oscillator. By modelling a thermally coupled VO2-oscillator circuit, the
network setup is demonstrated through the selection of coupling strength, power
supply levels, and the synchronization efficiency parameter. The distribution
of solutions depending on the operating mode of the oscillators, sub-threshold
mode, or generation mode are revealed. Technological approaches for the
implementation of a neural network information converter are proposed, and
examples of its application for image filtering are demonstrated. The proposed
method helps to significantly expand the capabilities of neuromorphic and
logical devices based on synchronization effects.Comment: 25 pages, 20 figure
