ArticleScientific reports2020
Model-size reduction for reservoir computing by concatenating internal states through time.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Artificial neural manifolds.Nature communications · 2026Article
- Modeling nonlinear oscillator networks using physics-informed hybrid reservoir computing.Scientific reports · 2025Article
- Post-processing methods for delay embedding and feature scaling of reservoir computers.Communications engineering · 2025Article
- Exploring Types of Photonic Neural Networks for Imaging and Computing-A Review.Nanomaterials (Basel, Switzerland) · 2024Review
- Connectome-based reservoir computing with the conn2res toolbox.Nature communications · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Reservoir computing (RC) is a machine learning algorithm that can learn complex time series from data very rapidly based on the use of high-dimensional dynamical systems, such as random networks of neurons, called "reservoirs." To implement RC in edge computing, it is highly important to reduce the amount of computational resources that RC requires. In this study, we propose methods that reduce the size of the reservoir by inputting the past or drifting states of the reservoir to the output layer at the current time step. To elucidate the mechanism of model-size reduction, the proposed methods are analyzed based on information processing capacity proposed by Dambre et al. (Sci Rep 2:514, 2012). In addition, we evaluate the effectiveness of the proposed methods on time-series prediction tasks: the generalized Hénon-map and NARMA. On these tasks, we found that the proposed methods were able to reduce the size of the reservoir up to one tenth without a substantial increase in regression error.
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Registered trials
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