# Harnessing omnipresent oscillator networks as computational resource

arXiv: https://arxiv.org/abs/2502.04818 · HTML: https://mlsanity.com/paper/2502.04818/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 59 references that are in the map.
- First posted on arXiv 7 Feb 2025.

## Builds on (most cited first, 7 of 7)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Physical reservoir computing -- An introductory perspective](https://mlsanity.com/paper/2005.00992/) (2020)
- [Learn to Synchronize, Synchronize to Learn](https://mlsanity.com/paper/2010.02860/) (2020)
- [Self-Evolutionary Reservoir Computer Based on Kuramoto Model](https://mlsanity.com/paper/2301.10654/) (2023)
- [Analytic relationship of relative synchronizability to network structure and motifs](https://mlsanity.com/paper/2305.10509/) (2023)
- [Virtual reservoir acceleration for CPU and GPU: Case study for coupled spin-torque oscillator reservoir](https://mlsanity.com/paper/2312.01121/) (2023)

## Abstract (arXiv)

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.