Harnessing omnipresent oscillator networks as computational resource

2025-02-07

AI Summary

This study addresses the potential of oscillator networks as intrinsic information-processing resources by proposing a novel reservoir computing paradigm based on Kuramoto oscillators. Methodologically, it establishes, for the first time, a general mapping between oscillator networks and reservoir computing: nonlinear driving replaces conventional input encoding, while a trainable feedback loop enables target-system emulation; order-parameter analysis further provides a physical interpretation of learning capability. Key contributions include: (1) high-accuracy long-term dynamical system emulation; (2) synchronization computational complexity reduced to O(N); and (3) support for fully connected topologies, linear-time synchronization, and flexible multi-parameter control. Experimental results demonstrate the feasibility and superiority of oscillator-based reservoirs for low-power, robust, brain-inspired computing.

Technology Category

• Machine Learning: Bio-inspired Learning
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Search and Optimization: Learning to Search

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Social Networks and Social Media: Computational social science
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

Nature is pervaded with oscillatory behavior. 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 reservoir 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. Secondly, the learning capabilities of the oscillator network can be explained using Kuramoto model's order parameter. This work provides the foundation for utilizing nature's oscillator networks as a new class of information processing systems.

Problem

• Harnessing oscillator networks for computation
• Emulating target-systems using trained feedback-loops
• Explaining learning capabilities via Kuramoto model

Innovation

• Utilizes Kuramoto model synchronization
• Employs trained feedback-loop emulation
• Explains learning via order parameter