Mike's Notes
Looks very useful, as a way into using Reservoir Computing. The abstract is copied below. Follow the PubMed link to find the full paper.
I have long planned to add Reservoir Computing to Pipi.
Resources
- https://www.semanticscholar.org/paper/Reservoir-computing-bootcamp-From-Python-NumPy-for-Inoue-Kubota/d4163724a636094954048e8cad007c47670d10a9
- https://pubmed.ncbi.nlm.nih.gov/41627226/
- https://rc-bootcamp.github.io
References
- Reference
Repository
- Home > Ajabbi Research > Library > Subscriptions > Semantic Scholar
- Home > Handbook >
Last Updated
17/03/2026
Reservoir computing bootcamp—From Python/NumPy tutorial for the complete beginners to cutting-edge research topics of reservoir computing
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Abstract
Reservoir computing (RC) is a machine learning framework that uses recurrent neural networks and is characterized by directly capitalizing on intrinsic dynamics instead of adjusting internal parameters. In particular, in the form of physical reservoir computing (PRC), recent studies have advanced by treating various physical systems as reservoirs and applying them to time-series data processing and quantifying information-processing properties. In this way, RC and PRC potentially have interdisciplinary impact, and as more researchers from diverse academic disciplines learn and utilize RC and PRC, there is potential for more creative research to emerge. In this paper, we introduce a Jupyter Notebook-based educational material called RC bootcamp for learning RC, being made publicly available under an open-source license (https://rc-bootcamp.github.io/). The RC bootcamp was originally developed and continuously updated within our research group to efficiently train our collaborators and new students, ultimately enabling them to conduct experiments by themselves. Considering the diverse backgrounds of learners, it starts with the basics of computer science and numerical computation using Python/NumPy, as well as fundamental implementations in RC, such as echo state networks and linear regression. Furthermore, it covers important analytical indicators based on dynamical systems theory, such as Lyapunov exponents, echo state property index, and information-processing capacity, as well as cutting-edge approaches utilizing chaos, including first-order, reduced and controlled error (FORCE) learning and innate training, and attractor design via bifurcation embedding. We expect that the RC bootcamp will become a useful educational material for learning RC and PRC and further invigorate research activities in the RC and PRC fields.
