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https://github.com/reservoirpy/awesome-reservoir-computing

Awesome tutorials, papers, projects and tools for Reservoir Computing techniques like Echo State Networks (ESN).
https://github.com/reservoirpy/awesome-reservoir-computing

List: awesome-reservoir-computing

artifical-neural-network awesome awesome-list echo-state-networks esn reccurent-neural-network reservoir-computing rnn

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Awesome tutorials, papers, projects and tools for Reservoir Computing techniques like Echo State Networks (ESN).

Host: GitHub
URL: https://github.com/reservoirpy/awesome-reservoir-computing
Owner: reservoirpy
License: mit
Created: 2021-11-02T12:37:18.000Z (almost 3 years ago)
Default Branch: main
Last Pushed: 2021-11-18T21:47:07.000Z (almost 3 years ago)
Last Synced: 2024-05-19T18:07:44.102Z (4 months ago)
Topics: artifical-neural-network, awesome, awesome-list, echo-state-networks, esn, reccurent-neural-network, reservoir-computing, rnn
Homepage:
Size: 26.4 KB
Stars: 32
Watchers: 3
Forks: 1
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

README

        # Awesome Reservoir Computing

Awesome tutorials, papers, projects and tools for Reservoir Computing techniques like Echo State Networks (ESN).

## Table of contents

* **[Introduction](#introduction)**

* **[Tutorials](#tutorials)**

* **[Papers](#papers)**

* **[Tools](#tools)**

* **[Contributing](#contributing)**

### Introduction

- [A Practical Guide to Applying Echo State Networks](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.720.616&rep=rep1&type=pdf) 

(2012) by Mantas Lukosevicius. Complete guide about ESNs, from theory to implementation.

- [Echo State Network](http://www.scholarpedia.org/article/Echo_state_network) on Scholarpedia, by Herbert Jaeger. Generic introduction to Reservoir Computing from Echo State Networks.

### Tutorials

  #### Tuning Hyperparameters

  

  - Hinaut & Trouvain (2021) [Which Hype for My New Task? Hints and Random Search for Echo State Networks Hyperparameters.](https://hal.inria.fr/hal-03203318/document) In International Conference on Artificial Neural Networks (pp. 83-97).

### Founders

#### Early Reservoirs

- Dominey (1995) [Complex sensory-motor sequence learning based on recurrent 

state representation and reinforcement learning.](https://www.researchgate.net/profile/Peter-Dominey/publication/15651430_Complex_sensory-motor_sequence_learning_based_on_recurrent_state_representation_and_reinforcement_learning/links/00b7d51f0e1c84ba2d000000/Complex-sensory-motor-sequence-learning-based-on-recurrent-state-representation-and-reinforcement-learning.pdf) Biol. Cybernetics, Vol. 73, 265-274 

- Buonomano & Merzenich (1995) [Temporal Information Transformed into a Spatial Code 

by a Neural Network with Realistic Properties.](https://personal.utdallas.edu/~kilgard/11c%20BuonomanoMerzenich_Science1995.pdf) Science 267, 1028-1030

#### Reservoir 2000's

- Jaeger (2001) [The "echo state" approach to analysing and training recurrent neural networks.](https://www.ai.rug.nl/minds/uploads/EchoStatesTechRep.pdf) GMD Report 148, GMD - German National Research Institute for Computer Science 

- Maass et al. (2002) [Real-time computing without stable states: A new framework for neural computation based on perturbations.](https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.183.2874&rep=rep1&type=pdf) Neural Computation, 14(11):2531-2560

### Papers

  #### Reviews

  

  #### Theory of RC

  

  

  #### Online learning

  - **Backpropagation-decorrelation:** Steil, J. J. (2004). [Backpropagation-decorrelation: Online recurrent learning with O(N) complexity.](https://www.researchgate.net/profile/Jochen-Steil/publication/4116728_Backpropagation-Decorrelation_Online_recurrent_learning_with_ON_complexity/links/00463519271a850735000000/Backpropagation-Decorrelation-Online-recurrent-learning-with-ON-complexity.pdf) 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541), 2, 843–848 vol.2.

  

  - **FORCE:** Sussillo, D., & Abbott, L. F. (2009). [Generating Coherent Patterns of Activity from Chaotic Neural Networks.](https://www.sciencedirect.com/science/article/pii/S0896627309005479) Neuron, 63(4), 544–557.

 

  - **Reward-modulated Hebbian learning *(3 factors rules)*:** Hoerzer, G. M., Legenstein, R., & Maass, W. (2014). [Emergence of Complex Computational Structures From Chaotic Neural Networks Through Reward-Modulated Hebbian Learning.](https://academic.oup.com/cercor/article/24/3/677/392266) Cerebral Cortex, 24(3), 677–690.

  #### Intrinsic plasticity

  

  - Schrauwen, B., Wardermann, M., Verstraeten, D., Steil, J. J., & Stroobandt, D. (2008). [Improving reservoirs using intrinsic plasticity.](https://www.sciencedirect.com/science/article/pii/S0925231208000519)

 Neurocomputing, 71(7), 1159–1171. https://doi.org/10.1016/j.neucom.2007.12.020

  #### RC for neurosciences

  

  - **RC as model of prefrontal cortex activity:** Enel, P., Procyk, E., Quilodran, R., & Dominey, P. F. (2016). [Reservoir Computing Properties of Neural Dynamics in Prefrontal Cortex.](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004967) PLOS Computational Biology, 12(6), e1004967.

  - **RC as model of the working memory:** Strock, A., Hinaut, X., & Rougier, N. P. (2020). [A Robust Model of Gated Working Memory.](https://www.biorxiv.org/content/biorxiv/early/2019/08/01/589564.full.pdf) Neural Computation, 32(1), 153–181.

  

### Recent papers

  #### 2021

  

  - Pedrelli & Hinaut (2021). [Hierarchical-task reservoir for online semantic analysis from continuous speech.](https://ieeexplore.ieee.org/iel7/5962385/6104215/09548713.pdf) IEEE Transactions on Neural Networks and Learning Systems.

  

  - Manneschi et al. (2021). [SpaRCe: Improved Learning of Reservoir Computing Systems through Sparse Representations.](https://ieeexplore.ieee.org/iel7/5962385/6104215/09514399.pdf) IEEE Transactions on Neural Networks and Learning Systems.

  - Manneschi et al. (2021). [Exploiting multiple timescales in hierarchical echo state networks.](https://internal-journal.frontiersin.org/articles/10.3389/fams.2020.616658/full) Frontiers in Applied Mathematics and Statistics, 6, 76.

  #### 2020

  

  - Bianchi et al. (2020) [Reservoir computing approaches for representation and classification of multivariate time series.](https://ieeexplore.ieee.org/iel7/5962385/6104215/09127499.pdf) IEEE transactions on neural networks and learning systems, 32(5), 2169-2179.

### Tools

### Contributing

Have anything in mind that you think is awesome and would fit in this list? Feel free to send a [pull request](https://github.com/reservoirpy/awesome-reservoir-computing/pulls).