# Deep Echo State Network (DeepESN): A Brief Survey

arXiv: https://arxiv.org/abs/1712.04323 · HTML: https://mlsanity.com/paper/1712.04323/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2017.

## Built on by (most cited first, 22 of 22)

- [TE-ESN: Time Encoding Echo State Network for Prediction Based on Irregularly Sampled Time Series Data](https://mlsanity.com/paper/2105.00412/) (2021)
- [Elastic Similarity and Distance Measures for Multivariate Time Series](https://mlsanity.com/paper/2102.10231/) (2021)
- [Spiking Neural Networks and Bio-Inspired Supervised Deep Learning: A Survey](https://mlsanity.com/paper/2307.16235/) (2023)
- [On the Post-hoc Explainability of Deep Echo State Networks for Time Series Forecasting, Image and Video Classification](https://mlsanity.com/paper/2102.08634/) (2021)
- [Analysis, Characterization, Prediction and Attribution of Extreme Atmospheric Events with Machine Learning: a Review](https://mlsanity.com/paper/2207.07580/) (2022)
- [MH-GIN: Multi-scale Heterogeneous Graph-based Imputation Network for AIS Data (Extended Version)](https://mlsanity.com/paper/2507.20362/) (2025)
- [Evolutionary Echo State Network: evolving reservoirs in the Fourier space](https://mlsanity.com/paper/2206.04951/) (2022)
- [LETS-GZSL: A Latent Embedding Model for Time Series Generalized Zero Shot Learning](https://mlsanity.com/paper/2207.12007/) (2022)
- [Re-visiting Reservoir Computing architectures optimized by Evolutionary Algorithms](https://mlsanity.com/paper/2211.06254/) (2022)
- [From Neurons to Computation: Biological Reservoir Computing for Pattern Recognition](https://mlsanity.com/paper/2505.03510/) (2025)
- [Echo State Networks as State-Space Models: A Systems Perspective](https://mlsanity.com/paper/2509.04422/) (2025)
- [Empirical Analysis of Limits for Memory Distance in Recurrent Neural Networks](https://mlsanity.com/paper/2212.11085/) (2022)
- [Recurrent Stochastic Configuration Networks with Incremental Blocks](https://mlsanity.com/paper/2411.11303/) (2024)
- [From Neural Activity to Computation: Biological Reservoirs for Pattern Recognition in Digit Classification](https://mlsanity.com/paper/2510.05637/) (2025)
- [Echo State Networks for Time Series Forecasting: Hyperparameter Sweep and Benchmarking](https://mlsanity.com/paper/2602.03912/) (2026)
- [Neuro-Inspired Visual Pattern Recognition via Biological Reservoir Computing](https://mlsanity.com/paper/2602.05737/) (2026)
- [Rethinking Random Transformers as Adaptive Sequence Smoothers for Sleep Staging](https://mlsanity.com/paper/2605.09905/) (2026)
- [KNN, An Underestimated Model for Regional Rainfall Forecasting](https://mlsanity.com/paper/2103.15235/) (2021)
- [Heterogeneous Reservoir Computing Models for Persian Speech Recognition](https://mlsanity.com/paper/2205.12594/) (2022)
- [Deep Recurrent Stochastic Configuration Networks for Modelling Nonlinear Dynamic Systems](https://mlsanity.com/paper/2410.20904/) (2024)
- [HypER: Hyperbolic Echo State Networks for Capturing Stretch-and-Fold Dynamics in Chaotic Flows](https://mlsanity.com/paper/2508.18196/) (2025)
- [Empirical Boundaries of Reservoir Computing for Few-Shot Sequence Pattern Tasks](https://mlsanity.com/paper/2610.19997/) (2026)

## Abstract (arXiv)

The study of deep recurrent neural networks (RNNs) and, in particular, of deep Reservoir Computing (RC) is gaining an increasing research attention in the neural networks community. The recently introduced Deep Echo State Network (DeepESN) model opened the way to an extremely efficient approach for designing deep neural networks for temporal data. At the same time, the study of DeepESNs allowed to shed light on the intrinsic properties of state dynamics developed by hierarchical compositions of recurrent layers, i.e. on the bias of depth in RNNs architectural design. In this paper, we summarize the advancements in the development, analysis and applications of DeepESNs.