A method for the classification of chimera states of coupled oscillators and its application for creating a neural network information converter

by   Andrei Velichko, et al.

The paper presents a new method for the classification of chimera states, which characterizes the synchronization of two coupled oscillators more accurately. As an example of method application, a neural network information converter based on a network of pulsed oscillators is used, which can convert input information from digital to analogue type and 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 chimera 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, prethreshold 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 for the classification of chimera states helps significantly expand the capabilities of neuromorphic and logical devices based on synchronization effects.


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