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A substrate-independent framework to characterize reservoir computers.

Matthew Dale1,2, Julian F Miller2, Susan Stepney1,2

  • 1Department of Computer Science, University of York, York YO10 5DD, UK.

Proceedings. Mathematical, Physical, and Engineering Sciences
|July 12, 2019
PubMed
Summary

This study introduces a framework to measure the computational quality of any physical system for reservoir computing (RC). This quality metric helps compare different substrates and understand how their properties impact task performance.

Keywords:
characterizationphysical computationreservoir computing

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Area of Science:

  • Physics
  • Computer Science
  • Dynamical Systems

Background:

  • Reservoir computing (RC) utilizes nonlinear dynamical systems as reservoirs for computation.
  • Existing RC frameworks allow diverse physical substrates but lack a standardized method for quality assessment.
  • Substrate properties are reconfigurable, leading to unique qualities for specific computational tasks.

Purpose of the Study:

  • To present an experimental framework for characterizing the computational quality of any physical substrate for reservoir computing.
  • To establish a method for comparing different RC substrates based on their inherent quality.
  • To elucidate the relationship between substrate properties and task performance in RC.

Main Methods:

  • Development of a novel experimental framework to quantify the 'quality' of a substrate for RC.
  • Systematic evaluation of how substrate reconfiguration (e.g., topology, morphology) influences reservoir properties.
  • Mapping the correlation between substrate quality, intrinsic properties, and computational task performance.

Main Results:

  • The proposed framework enables objective comparison of diverse physical substrates for RC.
  • Quality is shown to be a crucial factor in determining a substrate's suitability for specific computational tasks.
  • The framework facilitates understanding the complex interplay between physical properties and computational capabilities.

Conclusions:

  • A standardized framework for assessing RC substrate quality has been established.
  • This framework advances the understanding of what makes a dynamical system suitable for computation.
  • The findings will guide the improved design of future physical substrates for reservoir computing.