Author

Alberto Padoan

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Preprint Oct 2026

The internal model principle in reservoir computing

The paper studies learning in reservoir computing systems through the lens of the internal model principle. The key idea is to model training signals as outputs of an exogenous signal generator, thus connecting reservoir computing to output regulation, observer theory, and frequency-domain interpolation. For linear sig...

Danyal Saqib, Alberto Padoan · 0 citations

#machine learning Apr 2026

On Dominant Manifolds in Reservoir Computing Networks

This work studies the emergence of low-dimensional dominant manifolds in the training of Reservoir Computing networks for temporal forecasting tasks and shows that the training data generate an invariant subspace of the trained reservoir, whose dimension equals the number of dominant modes.

Noa Kaplan, Alberto Padoan, A. Bizyaeva · arXiv.org · 2 citations