https://github.com/fabridamicelli/echoes

Machine Learning with Echo State Networks, a scikit-learn compatible package.
https://github.com/fabridamicelli/echoes

echo-state-networks esn esnetwork machine-learning neural-network python recurrent-neural-networks reservoir-computing shallow-learning

Last synced: 3 months ago
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Machine Learning with Echo State Networks, a scikit-learn compatible package.

• Host: GitHub
• URL: https://github.com/fabridamicelli/echoes
• Owner: fabridamicelli
• License: mit
• Created: 2019-11-27T14:03:50.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2025-09-29T08:17:56.000Z (9 months ago)
• Last Synced: 2025-12-15T14:18:51.537Z (6 months ago)
• Topics: echo-state-networks, esn, esnetwork, machine-learning, neural-network, python, recurrent-neural-networks, reservoir-computing, shallow-learning
• Language: Python
• Homepage: https://fabridamicelli.github.io/echoes/
• Size: 14.4 MB
• Stars: 44
• Watchers: 1
• Forks: 9
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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# echoes 

- 🤖 Machine learning with Echo State Networks(ESN)

- 🧱 High level API, `scikit-learn` compatible API, eg `fit-predict` interface 

- ⚡️ Fast! (Thanks to numba ✨)

## Installation and quick start

```bash

pip install echoes

```

See [Documentation](https://fabridamicelli.github.io/echoes/) and [example notebooks](https://github.com/fabridamicelli/echoes/tree/master/docs/examples/notebooks).

## Feedback

Any bugs, suggestions, problems? Just [open an issue!](https://github.com/fabridamicelli/echoes/issues/new)

## `echoes` in the wild

Here's a selection of scientific publications using this package to investigate a number of scientific topics:

- [Memory-augmented hybrid quantum reservoir computing](https://journals.aps.org/prapplied/abstract/10.1103/wzwv-7rk2)

- [Brain Connectivity meets Reservoir Computing](https://doi.org/10.1371/journal.pcbi.1010639)

- [A Computational Perspective on the No-Strong-Loops Principle in Brain Networks](https://www.biorxiv.org/content/10.1101/2025.09.24.678310v1)

## Citing

If you find echoes useful for a publication, then please use the following BibTeX to cite it:

```

@misc{echoes,

  author = {Damicelli, Fabrizio},

  title = {echoes: Echo State Networks with Python},

  year = {2019},

  publisher = {GitHub},

  journal = {GitHub repository},

  howpublished = {\url{https://github.com/fabridamicelli/echoes}},

}

```

## Tests 

Run tests with 

```bash

make test

```

## References

  - [Reservoir computing approaches to recurrent neural network training, Mantas & Jaeger, 2009](https://www.sciencedirect.com/science/article/pii/S1574013709000173)

  - [A Practical Guide to Applying Echo State Networks, Mantas, 2012](https://link.springer.com/chapter/10.1007/978-3-642-35289-8_36)

  - [Echo state network (scholarpedia), Jaeger](http://www.scholarpedia.org/article/Echo_state_network)

  - [Short Term Memory in Echo State Networks, Jaeger, 2001](http://publica.fraunhofer.de/eprints/urn_nbn_de_0011-b-731310.pdf)