# Reservoir Computing Benchmarks: a tutorial review and critique

arXiv: https://arxiv.org/abs/2405.06561 · HTML: https://mlsanity.com/paper/2405.06561/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 140 references that are in the map.
- First posted on arXiv 10 May 2024.

## Builds on (most cited first, 13 of 13)

- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Data-Driven Forecasting of High-Dimensional Chaotic Systems with Long Short-Term Memory Networks](https://mlsanity.com/paper/1802.07486/) (2018)
- [Backpropagation Algorithms and Reservoir Computing in Recurrent Neural Networks for the Forecasting of Complex Spatiotemporal Dynamics](https://mlsanity.com/paper/1910.05266/) (2019)
- [Chaos as an interpretable benchmark for forecasting and data-driven modelling](https://mlsanity.com/paper/2110.05266/) (2021)
- [A Unifying Framework for Information Processing in Stochastically Driven Dynamical Systems](https://mlsanity.com/paper/1906.04608/) (2019)
- [Supervised Learning in Spiking Neural Networks with FORCE Training](https://mlsanity.com/paper/1609.02545/) (2016)
- [Model-Free Control of Dynamical Systems with Deep Reservoir Computing](https://mlsanity.com/paper/2010.02285/) (2020)
- [Exploiting Multiple Timescales in Hierarchical Echo State Networks](https://mlsanity.com/paper/2101.04223/) (2021)
- [SpaRCe: Improved Learning of Reservoir Computing Systems through Sparse Representations](https://mlsanity.com/paper/1912.08124/) (2019)
- [Signals to Spikes for Neuromorphic Regulated Reservoir Computing and EMG Hand Gesture Recognition](https://mlsanity.com/paper/2106.11169/) (2021)
- [Data-driven Reconstruction of Nonlinear Dynamics from Sparse Observation](https://mlsanity.com/paper/1906.04059/) (2019)
- [Quantifying the Computational Capability of a Nanomagnetic Reservoir Computing Platform with Emergent Magnetization Dynamics](https://mlsanity.com/paper/2111.14603/) (2021)
- [Exploring Transfer Function Nonlinearity in Echo State Networks](https://mlsanity.com/paper/1502.04423/) (2015)

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

- [Growing Reservoirs with Developmental Graph Cellular Automata](https://mlsanity.com/paper/2508.08091/) (2025)
- [ChemReservoir -- An Open-Source Framework for Chemically-Inspired Reservoir Computing](https://mlsanity.com/paper/2506.04249/) (2025)
- [Bridging Biological Hearing and Neuromorphic Computing: End-to-End Time-Domain Audio Signal Processing with Reservoir Computing](https://mlsanity.com/paper/2603.24283/) (2026)

## Abstract (arXiv)

Reservoir Computing is an Unconventional Computation model to perform computation on various different substrates, such as recurrent neural networks or physical materials. The method takes a 'black-box' approach, training only the outputs of the system it is built on. As such, evaluating the computational capacity of these systems can be challenging. We review and critique the evaluation methods used in the field of reservoir computing. We introduce a categorisation of benchmark tasks. We review multiple examples of benchmarks from the literature as applied to reservoir computing, and note their strengths and shortcomings. We suggest ways in which benchmarks and their uses may be improved to the benefit of the reservoir computing community.