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Asymmetrically connected reservoir networks learn better.

Shailendra K Rathor1, Martin Ziegler2, Jörg Schumacher1,3

  • 1Technische Universität Ilmenau, Institute of Thermodynamics and Fluid Mechanics, P.O.Box 100565, D-98684 Ilmenau, Germany.

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Random, asymmetric connections in reservoir networks significantly boost performance. These findings highlight the importance of network structure for computational power and information processing capacity in recurrent neural networks.

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Area of Science:

  • Computational neuroscience
  • Artificial intelligence
  • Complex systems

Background:

  • Reservoir computing utilizes high-dimensional recurrent neural networks for complex temporal data processing.
  • Network connectivity patterns are hypothesized to influence computational capabilities.
  • Understanding optimal network topology is key to enhancing reservoir performance.

Purpose of the Study:

  • To systematically investigate how network connectivity, specifically symmetry and structure, impacts reservoir network performance.
  • To compare the computational power of reservoirs with random versus structured connectivities.
  • To quantify the information processing capacity across different network topologies.

Main Methods:

  • Systematic analysis of reservoir network connectivity.
  • Evaluation of network performance using the Mackey-Glass time series benchmark.
  • Quantification of information processing capacity for various network topologies.

Main Results:

  • Reservoirs with random and asymmetric connections outperformed all tested structured reservoirs.
  • This includes comparisons against biologically inspired topologies like small-world networks.
  • Maximum information processing capacity was observed in asymmetric and randomly connected networks.

Conclusions:

  • High-dimensional recurrent layer connectivity is critical for reservoir network performance.
  • Random and asymmetric network structures are superior for computational tasks like time series prediction.
  • Optimizing connectivity is essential for maximizing the information processing capacity of reservoir computing systems.