# Reservoir computing with simple oscillators: Virtual and real networks

arXiv: https://arxiv.org/abs/1802.08590 · HTML: https://mlsanity.com/paper/1802.08590/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Feb 2018.

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

- [Master memory function for delay-based reservoir computers with single-variable dynamics](https://mlsanity.com/paper/2108.12643/) (2021)
- [SpikeCLIP: A Contrastive Language-Image Pretrained Spiking Neural Network](https://mlsanity.com/paper/2310.06488/) (2023)
- [Infinite-dimensional Folded-in-time Deep Neural Networks](https://mlsanity.com/paper/2101.02966/) (2021)

## Abstract (arXiv)

The reservoir computing scheme is a machine learning mechanism which utilizes the naturally occuring computational capabilities of dynamical systems. One important subset of systems that has proven powerful both in experiments and theory are delay-systems. In this work, we investigate the reservoir computing performance of hybrid network-delay systems systematically by evaluating the NARMA10 and the Sante Fe task.. We construct 'multiplexed networks' that can be seen as intermediate steps on the scale from classical networks to the 'virtual networks' of delay systems. We find that the delay approach can be extended to the network case without loss of computational power, enabling the construction of faster reservoir computing substrates.