# Reservoir Topology in Deep Echo State Networks

arXiv: https://arxiv.org/abs/1909.11022 · HTML: https://mlsanity.com/paper/1909.11022/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Sep 2019.

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

- [Hierarchical Architectures in Reservoir Computing Systems](https://mlsanity.com/paper/2105.06923/) (2021)
- [Evolutionary Echo State Network: evolving reservoirs in the Fourier space](https://mlsanity.com/paper/2206.04951/) (2022)
- [Re-visiting Reservoir Computing architectures optimized by Evolutionary Algorithms](https://mlsanity.com/paper/2211.06254/) (2022)
- [Reservoir Network with Structural Plasticity for Human Activity Recognition](https://mlsanity.com/paper/2503.00393/) (2025)
- [Topology Structure Optimization of Reservoirs Using GLMY Homology](https://mlsanity.com/paper/2509.11612/) (2025)
- [Explicit Interaction Architectures for Dynamical Learning: A Controlled Study of Structural Inductive Bias](https://mlsanity.com/paper/2606.19101/) (2026)

## Abstract (arXiv)

Deep Echo State Networks (DeepESNs) recently extended the applicability of Reservoir Computing (RC) methods towards the field of deep learning. In this paper we study the impact of constrained reservoir topologies in the architectural design of deep reservoirs, through numerical experiments on several RC benchmarks. The major outcome of our investigation is to show the remarkable effect, in terms of predictive performance gain, achieved by the synergy between a deep reservoir construction and a structured organization of the recurrent units in each layer. Our results also indicate that a particularly advantageous architectural setting is obtained in correspondence of DeepESNs where reservoir units are structured according to a permutation recurrent matrix.