Asymptotic evaluation of the information processing capacity in reservoir computing
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Abstract (arXiv)
Reservoir computing (RC) is becoming increasingly important because of its short training time. The squared error normalized by the target output is called the information processing capacity (IPC) and is used to evaluate the performance of an RC system. Since RC aims to learn the relationship between input and output time series, we should evaluate the IPC for infinitely long data rather than the IPC for finite-length data. However, a method for estimating it has not been established. We evaluated the IPC for infinitely long data using the asymptotic expansion of the IPC and weighted least-squares fitting. Then, we showed the validity of our method by numerical simulations. This work makes the performance evaluation of RC more evident.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 5 of its 27 references that are in the map. First posted on arXiv 15 Feb 2025.
Builds on
5 of 27 references.
References · 5
- 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,016
- 2018Echo state networks are universalcited by 86
- 2018Reservoir Computing Universality With Stochastic Inputscited by 48
- 2019A Unifying Framework for Information Processing in Stochastically Driven Dynamical Systemscited by 16
- 2025Memory Capacity of Nonlinear Recurrent Networks: Is it Informative?cited by 3
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Citing papers per year: 2025: 1; 2026: 2.
3 papers.