{"id":13688384,"url":"https://github.com/reservoirpy/awesome-reservoir-computing","last_synced_at":"2026-04-03T20:31:36.746Z","repository":{"id":46281352,"uuid":"423837587","full_name":"reservoirpy/awesome-reservoir-computing","owner":"reservoirpy","description":"Awesome tutorials, papers, projects and tools for Reservoir Computing techniques like Echo State Networks (ESN).","archived":false,"fork":false,"pushed_at":"2021-11-18T21:47:07.000Z","size":27,"stargazers_count":50,"open_issues_count":0,"forks_count":4,"subscribers_count":3,"default_branch":"main","last_synced_at":"2025-04-25T01:02:37.352Z","etag":null,"topics":["artifical-neural-network","awesome","awesome-list","echo-state-networks","esn","reccurent-neural-network","reservoir-computing","rnn"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/reservoirpy.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2021-11-02T12:37:18.000Z","updated_at":"2025-04-22T15:46:13.000Z","dependencies_parsed_at":"2022-09-22T19:10:36.941Z","dependency_job_id":null,"html_url":"https://github.com/reservoirpy/awesome-reservoir-computing","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/reservoirpy%2Fawesome-reservoir-computing","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/reservoirpy%2Fawesome-reservoir-computing/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/reservoirpy%2Fawesome-reservoir-computing/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/reservoirpy%2Fawesome-reservoir-computing/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/reservoirpy","download_url":"https://codeload.github.com/reservoirpy/awesome-reservoir-computing/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":251856994,"owners_count":21655119,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["artifical-neural-network","awesome","awesome-list","echo-state-networks","esn","reccurent-neural-network","reservoir-computing","rnn"],"created_at":"2024-08-02T15:01:12.711Z","updated_at":"2026-04-03T20:31:36.732Z","avatar_url":"https://github.com/reservoirpy.png","language":null,"funding_links":[],"categories":["Others"],"sub_categories":[],"readme":"# Awesome Reservoir Computing\n\nAwesome tutorials, papers, projects and tools for Reservoir Computing techniques like Echo State Networks (ESN).\n\n## Table of contents\n\n* **[Introduction](#introduction)**\n* **[Tutorials](#tutorials)**\n* **[Papers](#papers)**\n* **[Tools](#tools)**\n* **[Contributing](#contributing)**\n\n\n### Introduction\n\n- [A Practical Guide to Applying Echo State Networks](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.720.616\u0026rep=rep1\u0026type=pdf) \n(2012) by Mantas Lukosevicius. Complete guide about ESNs, from theory to implementation.\n- [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.\n\n\n### Tutorials\n\n  #### Tuning Hyperparameters\n  \n  - Hinaut \u0026 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).\n\n### Founders\n\n#### Early Reservoirs\n- Dominey (1995) [Complex sensory-motor sequence learning based on recurrent \nstate 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 \n\n- Buonomano \u0026 Merzenich (1995) [Temporal Information Transformed into a Spatial Code \nby a Neural Network with Realistic Properties.](https://personal.utdallas.edu/~kilgard/11c%20BuonomanoMerzenich_Science1995.pdf) Science 267, 1028-1030\n\n#### Reservoir 2000's\n\n- 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 \n\n- 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\u0026rep=rep1\u0026type=pdf) Neural Computation, 14(11):2531-2560\n\n### Papers\n\n  #### Reviews\n  \n\n  #### Theory of RC\n  \n  \n  #### Online learning\n\n  - **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.\n  \n  - **FORCE:** Sussillo, D., \u0026 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.\n \n  - **Reward-modulated Hebbian learning *(3 factors rules)*:** Hoerzer, G. M., Legenstein, R., \u0026 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.\n\n  #### Intrinsic plasticity\n  \n  - Schrauwen, B., Wardermann, M., Verstraeten, D., Steil, J. J., \u0026 Stroobandt, D. (2008). [Improving reservoirs using intrinsic plasticity.](https://www.sciencedirect.com/science/article/pii/S0925231208000519)\n Neurocomputing, 71(7), 1159–1171. https://doi.org/10.1016/j.neucom.2007.12.020\n\n  #### RC for neurosciences\n  \n  - **RC as model of prefrontal cortex activity:** Enel, P., Procyk, E., Quilodran, R., \u0026 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.\n\n  - **RC as model of the working memory:** Strock, A., Hinaut, X., \u0026 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.\n  \n### Recent papers\n\n  #### 2021\n  \n  - Pedrelli \u0026 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.\n  \n  - 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.\n\n  - 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.\n\n\n  #### 2020\n  \n  - 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.\n\n\n### Tools\n\n\n### Contributing\n\nHave 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).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Freservoirpy%2Fawesome-reservoir-computing","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Freservoirpy%2Fawesome-reservoir-computing","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Freservoirpy%2Fawesome-reservoir-computing/lists"}