# Dimension of Reservoir Computers

arXiv: https://arxiv.org/abs/1912.06472 · HTML: https://mlsanity.com/paper/1912.06472/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2019.

## Built on by (most cited first, 6 of 6)

- [Attractor reconstruction with reservoir computers: The effect of the reservoir's conditional Lyapunov exponents on faithful attractor reconstruction](https://mlsanity.com/paper/2401.00885/) (2023)
- [Optimal training of finitely-sampled quantum reservoir computers for forecasting of chaotic dynamics](https://mlsanity.com/paper/2409.01394/) (2024)
- [Harnessing Synthetic Active Particles for Physical Reservoir Computing](https://mlsanity.com/paper/2307.15010/) (2023)
- [Effects of cavity nonlinearities and linear losses on silicon microring-based reservoir computing](https://mlsanity.com/paper/2310.09433/) (2023)
- [Dimensional criterion for forecasting nonlinear systems by reservoir computing](https://mlsanity.com/paper/2202.05159/) (2022)
- [Prediction of Unobserved Bifurcation by Unsupervised Extraction of Slowly Time-Varying System Parameter Dynamics from Time Series Using Reservoir Computing](https://mlsanity.com/paper/2406.13995/) (2024)

## Abstract (arXiv)

A reservoir computer is a complex dynamical system, often created by coupling nonlinear nodes in a network. The nodes are all driven by a common driving signal. In this work, three dimension estimation methods, false nearest neighbor, covariance and Kaplan-Yorke dimensions, are used to estimate the dimension of the reservoir dynamical system. It is shown that the signals in the reservoir system exist on a relatively low dimensional surface. Changing the spectral radius of the reservoir network can increase the fractal dimension of the reservoir signals, leading to an increase in testing error.