ReservoirComputing.jl
ReservoirComputing.jl is a versatile and user-friendly Julia package designed for the implementation of Reservoir Computing models, such as Echo State Networks (ESNs). Reservoir Computing expands the input data into a higher-dimensional space, leveraging regression techniques for effective model training. This approach can be thought as a kernel method with an explicit kernel trick.
This library assumes some basic knowledge of Reservoir Computing. For a good introduction, we suggest the following papers: the first two are the seminal papers about ESN and liquid state machines, the others are in-depth review papers that should cover all the needed information. For the majority of the algorithms implemented in this library we cited in the documentation the original work introducing them. If you ever are in doubt about a method or a function just type ? function in the Julia REPL to read the relevant notes.
- Jaeger, Herbert: The “echo state” approach to analyzing and training recurrent neural networks-with an erratum note.
- Maass W, Natschläger T, Markram H: Real-time computing without stable states: a new framework for neural computation based on perturbations.
- Lukoševičius, Mantas: A practical guide to applying echo state networks. Neural networks: Tricks of the trade.
- Lukoševičius, Mantas, and Herbert Jaeger: Reservoir computing approaches to recurrent neural network training.
For faster computations on the CPU it is suggested to add using MKL to the script. For clarity's sake this library will not be indicated under every example in the documentation.
Installation
To install ReservoirComputing.jl, ensure you have Julia version 1.10 or higher. Follow these steps:
1. Open the Julia command line.
2. Enter the Pkg REPL mode by pressing ].
3. Type `add ReservoirComputing` and press Enter.For a more customized installation or to contribute to the package, consider cloning the repository:
using Pkg
Pkg.clone("https://github.com/SciML/ReservoirComputing.jl.git")or dev the package.
Features Overview
- Multiple Training Algorithms: Supports Ridge Regression, Linear Models, and LIBSVM regression methods for Reservoir Computing models.
- Diverse Prediction Methods: Offers both generative and predictive methods for Reservoir Computing predictions.
- Modifiable Training and Prediction: Allows modifications in Reservoir Computing states, such as state extension, padding, and combination methods.
- Non-linear Algorithm Options: Includes options for non-linear modifications in algorithms.
- Echo State Networks (ESNs): Features various input layers, reservoirs, and methods for driving ESN reservoir states.
- Cellular Automata-Based Reservoir Computing: Introduces models based on one-dimensional Cellular Automata for Reservoir Computing.
Contributing
Contributions to ReservoirComputing.jl are highly encouraged and appreciated. Whether it's through implementing new RC model variations, enhancing documentation, adding examples, or any improvement, your contribution is valuable. We welcome posts of relevant papers or ideas in the issues section. For deeper insights into the library's functionality, the API section in the documentation is a great resource. For any queries not suited for issues, please reach out to the lead developers via Slack or email.
Citing
If you use ReservoirComputing.jl in your work, we kindly ask you to cite it. Here is the BibTeX entry for your convenience:
@article{JMLR:v23:22-0611,
author = {Francesco Martinuzzi and Chris Rackauckas and Anas Abdelrehim and Miguel D. Mahecha and Karin Mora},
title = {ReservoirComputing.jl: An Efficient and Modular Library for Reservoir Computing Models},
journal = {Journal of Machine Learning Research},
year = {2022},
volume = {23},
number = {288},
pages = {1--8},
url = {http://jmlr.org/papers/v23/22-0611.html}
}Reproducibility
The documentation of this SciML package was built using these direct dependencies,
Status `/var/lib/buildkite-agent/builds/gpuci-11/julialang/reservoircomputing-dot-jl/docs/Project.toml`
⌅ [878138dc] CellularAutomata v0.0.2
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[2913bbd2] StatsBase v0.34.5
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Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading. To see why use `status --outdated`and using this machine and Julia version.
Julia Version 1.11.5
Commit 760b2e5b739 (2025-04-14 06:53 UTC)
Build Info:
Official https://julialang.org/ release
Platform Info:
OS: Linux (x86_64-linux-gnu)
CPU: 48 × AMD EPYC 7402 24-Core Processor
WORD_SIZE: 64
LLVM: libLLVM-16.0.6 (ORCJIT, znver2)
Threads: 1 default, 0 interactive, 1 GC (on 2 virtual cores)
Environment:
JULIA_CPU_THREADS = 2
JULIA_DEPOT_PATH = /root/.cache/julia-buildkite-plugin/depots/01852978-cea0-41b9-93ac-ff3dc03e5dc5
LD_LIBRARY_PATH = /usr/local/nvidia/lib:/usr/local/nvidia/lib64
JULIA_PKG_SERVER =A more complete overview of all dependencies and their versions is also provided.
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[35661453] Xorg_xkbcomp_jll v1.4.7+0
[33bec58e] Xorg_xkeyboard_config_jll v2.44.0+0
[c5fb5394] Xorg_xtrans_jll v1.6.0+0
[3161d3a3] Zstd_jll v1.5.7+1
[35ca27e7] eudev_jll v3.2.14+0
[214eeab7] fzf_jll v0.61.1+0
[a4ae2306] libaom_jll v3.11.0+0
[0ac62f75] libass_jll v0.15.2+0
[1183f4f0] libdecor_jll v0.2.2+0
[2db6ffa8] libevdev_jll v1.13.4+0
[f638f0a6] libfdk_aac_jll v2.0.3+0
[36db933b] libinput_jll v1.28.1+0
[b53b4c65] libpng_jll v1.6.49+0
[f27f6e37] libvorbis_jll v1.3.7+2
[009596ad] mtdev_jll v1.1.7+0
[1317d2d5] oneTBB_jll v2022.0.0+0
⌅ [1270edf5] x264_jll v2021.5.5+0
⌅ [dfaa095f] x265_jll v3.5.0+0
[d8fb68d0] xkbcommon_jll v1.8.1+0
[0dad84c5] ArgTools v1.1.2
[56f22d72] Artifacts v1.11.0
[2a0f44e3] Base64 v1.11.0
[ade2ca70] Dates v1.11.0
[8ba89e20] Distributed v1.11.0
[f43a241f] Downloads v1.6.0
[7b1f6079] FileWatching v1.11.0
[9fa8497b] Future v1.11.0
[b77e0a4c] InteractiveUtils v1.11.0
[4af54fe1] LazyArtifacts v1.11.0
[b27032c2] LibCURL v0.6.4
[76f85450] LibGit2 v1.11.0
[8f399da3] Libdl v1.11.0
[37e2e46d] LinearAlgebra v1.11.0
[56ddb016] Logging v1.11.0
[d6f4376e] Markdown v1.11.0
[a63ad114] Mmap v1.11.0
[ca575930] NetworkOptions v1.2.0
[44cfe95a] Pkg v1.11.0
[de0858da] Printf v1.11.0
[3fa0cd96] REPL v1.11.0
[9a3f8284] Random v1.11.0
[ea8e919c] SHA v0.7.0
[9e88b42a] Serialization v1.11.0
[1a1011a3] SharedArrays v1.11.0
[6462fe0b] Sockets v1.11.0
[2f01184e] SparseArrays v1.11.0
[f489334b] StyledStrings v1.11.0
[4607b0f0] SuiteSparse
[fa267f1f] TOML v1.0.3
[a4e569a6] Tar v1.10.0
[8dfed614] Test v1.11.0
[cf7118a7] UUIDs v1.11.0
[4ec0a83e] Unicode v1.11.0
[e66e0078] CompilerSupportLibraries_jll v1.1.1+0
[deac9b47] LibCURL_jll v8.6.0+0
[e37daf67] LibGit2_jll v1.7.2+0
[29816b5a] LibSSH2_jll v1.11.0+1
[c8ffd9c3] MbedTLS_jll v2.28.6+0
[14a3606d] MozillaCACerts_jll v2023.12.12
[4536629a] OpenBLAS_jll v0.3.27+1
[05823500] OpenLibm_jll v0.8.5+0
[efcefdf7] PCRE2_jll v10.42.0+1
[bea87d4a] SuiteSparse_jll v7.7.0+0
[83775a58] Zlib_jll v1.2.13+1
[8e850b90] libblastrampoline_jll v5.11.0+0
[8e850ede] nghttp2_jll v1.59.0+0
[3f19e933] p7zip_jll v17.4.0+2
Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading. To see why use `status --outdated -m`You can also download the manifest file and the project file.