# Detection of Generalized Synchronization using Echo State Networks

arXiv: https://arxiv.org/abs/1710.08286 · HTML: https://mlsanity.com/paper/1710.08286/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2017.

## Built on by (most cited first, 3 of 3)

- [Optimized ensemble deep learning framework for scalable forecasting of dynamics containing extreme events](https://mlsanity.com/paper/2106.08968/) (2021)
- [Modeling Biological Multifunctionality with Echo State Networks](https://mlsanity.com/paper/2510.23940/) (2025)
- [Beyond the Edge of Chaos: Stability-Expressivity Transfer in Reservoir Forecasting](https://mlsanity.com/paper/2607.17909/) (2026)

## Abstract (arXiv)

Generalized synchronization between coupled dynamical systems is a phenomenon of relevance in applications that range from secure communications to physiological modelling. Here we test the capabilities of reservoir computing and, in particular, echo state networks for the detection of generalized synchronization. A nonlinear dynamical system consisting of two coupled R"ossler chaotic attractors is used to generate temporal series consisting of time-locked generalized synchronized sequences interleaved by unsynchronized ones. Correctly tuned, echo state networks are able to efficiently discriminate between unsynchronized and synchronized sequences. Compared to other state-of-the-art techniques of synchronization detection, the online capabilities of the proposed ESN based methodology make it a promising choice for real-time applications aiming to monitor dynamical synchronization changes in continuous signals.