A Method for Evaluating Chimeric Synchronization of Coupled Oscillators and Its Application for Creating a Neural Network Information Converter

TL;DR

This paper introduces a novel method for evaluating chimeric synchronization in coupled oscillators and demonstrates its application in creating a neural network-based information converter capable of transforming and processing digital signals into analog states.

Contribution

The paper proposes a new metric family for assessing chimeric synchronization and applies it to develop a neural network converter using pulsed oscillators, expanding neuromorphic device capabilities.

Findings

Demonstrated neural network converter using VO2 oscillators

Showed control of synchronization through coupling and power parameters

Applied converter to image filtering tasks

Abstract

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.