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Path length statistics in reservoir computers.

T L Carroll1

  • 1U.S. Naval Research Lab, Washington, DC 20375, USA.

Chaos (Woodbury, N.Y.)
|September 3, 2020
PubMed
Summary
This summary is machine-generated.

Designing effective reservoir computers is complex. A new statistic, mean path length, predicts signal diversity and can simplify the design process by pre-screening network configurations, reducing simulation needs.

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Area of Science:

  • Computational neuroscience
  • Complex systems
  • Machine learning

Background:

  • Reservoir computing (RC) involves high-dimensional dynamical systems, making design challenging.
  • Parameter space exploration for RCs requires extensive simulation, hindering efficient design.

Purpose of the Study:

  • To identify a simple statistic for predicting reservoir computer performance.
  • To reduce the computational complexity of designing reservoir computers.

Main Methods:

  • Analysis of the relationship between mean path length between nodes and RC performance.
  • Evaluation of signal diversity using the covariance matrix of the reservoir computer's signals.

Main Results:

  • A statistic based on mean path length is correlated with reservoir computer performance.
  • This statistic predicts the diversity of signals generated by the reservoir, as measured by the covariance matrix.
  • The statistic alone is insufficient as it doesn't account for matching to training signals.

Conclusions:

  • Mean path length offers a valuable metric for initial screening of reservoir computer network configurations.
  • This approach can significantly reduce the design space and computational cost by eliminating unsuitable designs early.
  • Further research is needed to integrate this statistic with training signal matching for optimal reservoir computer design.