ArticleFrontiers in physiology2021
Optimizing Reservoir Computers for Signal Classification.
Article in Frontiers in physiology, 2021. 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.
- Morphologically tunable mycelium chips for physical reservoir computing.Scientific reports · 2026Article
- Intrinsic stabilization of synaptic plasticity improves learning and robustness in artificial neural networks.Nature communications · 2026Article
- Stochastic reservoir computers.Nature communications · 2025Article
- Impact of time-history terms on reservoir dynamics and prediction accuracy in echo state networks.Scientific reports · 2024Article
- Tipping Point Detection Using Reservoir Computing.Research (Washington, D.C.) · 2023Article
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1 author.
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Abstract
Reservoir computers are a type of recurrent neural network for which the network connections are not changed. To train the reservoir computer, a set of output signals from the network are fit to a training signal by a linear fit. As a result, training of a reservoir computer is fast, and reservoir computers may be built from analog hardware, resulting in high speed and low power consumption. To get the best performance from a reservoir computer, the hyperparameters of the reservoir computer must be optimized. In signal classification problems, parameter optimization may be computationally difficult; it is necessary to compare many realizations of the test signals to get good statistics on the classification probability. In this work, it is shown in both a spiking reservoir computer and a reservoir computer using continuous variables that the optimum classification performance occurs for the hyperparameters that maximize the entropy of the reservoir computer. Optimizing for entropy only requires a single realization of each signal to be classified, making the process much faster to compute.
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