# Concentric ESN: Assessing the Effect of Modularity in Cycle Reservoirs

arXiv: https://arxiv.org/abs/1805.09244 · HTML: https://mlsanity.com/paper/1805.09244/

## Key facts

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

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

- [Exploiting Multiple Timescales in Hierarchical Echo State Networks](https://mlsanity.com/paper/2101.04223/) (2021)
- [A Dynamic Systems Approach to Modelling Human-Machine Rhythm Interaction](https://mlsanity.com/paper/2407.09538/) (2024)
- [A novel Reservoir Architecture for Periodic Time Series Prediction](https://mlsanity.com/paper/2405.10102/) (2024)

## Abstract (arXiv)

The paper introduces concentric Echo State Network, an approach to design reservoir topologies that tries to bridge the gap between deterministically constructed simple cycle models and deep reservoir computing approaches. We show how to modularize the reservoir into simple unidirectional and concentric cycles with pairwise bidirectional jump connections between adjacent loops. We provide a preliminary experimental assessment showing how concentric reservoirs yield to superior predictive accuracy and memory capacity with respect to single cycle reservoirs and deep reservoir models.