Harnessing omnipresent oscillator networks as computational resource
This work provides a foundational approach for leveraging ubiquitous oscillatory dynamics in nature as information processing systems, which is incremental in applying existing models to new computational contexts.
The authors tackled the problem of using natural oscillator networks as computational resources by developing a universal framework based on the Kuramoto model, achieving a system that emulates target dynamics with linear-time all-to-all coupling and abundant synchronization parameters.
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.