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Homeostatic plasticity for single node delay-coupled reservoir computing.

Hazem Toutounji1, Johannes Schumacher2, Gordon Pipa3

  • 1Neuroinformatics Department, Institute of Cognitive Science, University of Osnabrück, 49069 Osnabrück, Germany, and Department of Theoretical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim of Heidelberg University, 68159 Mannheim, Germany hazem.toutounji@zi-mannheim.de.

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Summary
This summary is machine-generated.

This study introduces a biologically inspired homeostatic plasticity mechanism to optimize temporal multiplexing in delay-coupled reservoirs, enhancing computational power and memory capacity for improved time-series prediction.

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Area of Science:

  • Computational Neuroscience
  • Machine Learning
  • Dynamical Systems

Background:

  • Reservoir computing utilizes infinite-dimensional dynamical systems, often implemented with delay-coupled nonlinear nodes, to process information.
  • Temporal multiplexing of subunits along the delay span is crucial for the computational power of these systems.
  • Existing methods lack adaptive mechanisms to optimize this temporal multiplexing.

Purpose of the Study:

  • To develop and analyze a biologically inspired homeostatic plasticity mechanism for learning optimal temporal multiplexing in delay-coupled reservoirs.
  • To demonstrate the enhancement of computational power through this adaptive plasticity.
  • To investigate the impact of plasticity on memory capacity, time-series prediction accuracy, and information processing.

Main Methods:

  • Derivation of a local, biologically inspired homeostatic plasticity rule that adjusts subunit distances based on input responsiveness.
  • Implementation of the plasticity mechanism within a delay-coupled reservoir computing architecture.
  • Evaluation of the reservoir's performance through analysis of memory capacity, NARMA-10 time-series prediction, and input-information capacity.

Main Results:

  • The homeostatic plasticity mechanism successfully learns optimal temporal multiplexing, significantly improving the reservoir's computational capabilities.
  • Memory capacity of the reservoir is demonstrably increased.
  • Prediction accuracy for the NARMA-10 time series improved by over 20% (reduced normalized root-mean-square error).

Conclusions:

  • Biologically inspired homeostatic plasticity offers an effective method for optimizing temporal multiplexing in delay-coupled reservoirs.
  • This plasticity mechanism enhances computational power, memory capacity, and prediction accuracy, offering a novel approach to reservoir computing.
  • The study highlights the influence of plasticity on key reservoir properties, including information capacity and subunit coupling.