Harnessing omnipresent oscillator networks as computational resource

TL;DR

This paper introduces a universal framework using the Kuramoto oscillator model to harness natural oscillator networks for computation, demonstrating their synchronization-based learning and potential as information processing systems.

Contribution

It presents a novel method to emulate target systems with oscillator networks via feedback loops, leveraging synchronization properties for computational tasks.

Findings

Oscillator networks can emulate complex systems through trained feedback loops.

Synchronization properties enable the networks to inherit performance characteristics.

The approach offers linear time complexity for all-to-all coupling in networks.

Abstract

Nature is pervaded with oscillatory dynamics. In networks of coupled oscillators patterns can arise when the system synchronizes to an external input. Hence, these networks provide processing and memory of input. We present a universal framework for harnessing oscillator networks as computational resource. This computing framework is introduced by the ubiquitous model for phase-locking, the Kuramoto model. We force the Kuramoto model by a nonlinear target-system, then after substituting the target-system with a trained feedback-loop it emulates the target-system. Our results are two-fold. Firstly, the trained network inherits performance properties of the Kuramoto model, where all-to-all coupling is performed in linear time with respect to the number of nodes and parameters for synchronization are abundant. The latter implies that the network is generically successful since the system learns via sychronization. Secondly, the learning capabilities of the oscillator network, which describe a type of collective intelligence, can be explained using Kuramoto model's order parameter. In summary, this work provides the foundation for utilizing nature's oscillator networks as a new class of information processing systems.