Master Memory Function for Delay-Based Reservoir Computers With Single-Variable Dynamics

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Abstract

We show that many delay-based reservoir computers considered in the literature can be characterized by a universal master memory function (MMF). Once computed for two independent parameters, this function provides linear memory capacity for any delay-based single-variable reservoir with small inputs. Moreover, we propose an analytical description of the MMF that enables its efficient and fast computation. Our approach can be applied not only to single-variable delay-based reservoirs governed by known dynamical rules, such as the Mackey-Glass or Stuart-Landau-like systems, but also to reservoirs whose dynamical model is not available.

Original languageEnglish
Pages (from-to)7712-7725
Number of pages14
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume35
Issue number6
DOIs
Publication statusPublished - 1 Jun 2024
Externally publishedYes

Keywords

  • Machine learning
  • nonlinear dynamics
  • reservoir computing

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