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Dynamics and Information Import in Recurrent Neural Networks.

Claus Metzner1, Patrick Krauss1,2,3

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Summary

Recurrent neural networks (RNNs) import information optimally in low-density chaotic regimes, not the "edge of chaos." A new "Import Resonance" phenomenon reveals peak information import based on input coupling strength.

Keywords:
dynamical systemedge of chaosinformation processingrecurrent neural networks (RNNs)resonance phenomena

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Area of Science:

  • Computational Neuroscience
  • Complex Systems
  • Artificial Intelligence

Background:

  • Recurrent neural networks (RNNs) are dynamical systems with behaviors like periodic, chaotic, or fixed point attractors.
  • Network statistics, including connection density (d) and excitation-inhibition balance (b), govern RNN behavior.
  • The optimal dynamical regime for RNNs to import external information remains unclear.

Purpose of the Study:

  • To investigate how network statistics (balance and density) influence information import in RNNs.
  • To identify optimal dynamical regimes for information processing in RNNs.
  • To explore new resonance phenomena related to information import.

Main Methods:

  • Quantitative measures of information import: average correlations (C) and mutual information (I).
  • Analysis of information import dependence on network balance (b) and density (d).
  • Identification and characterization of resonance phenomena like Import Resonance (IR).

Main Results:

  • Phase diagrams C(b, d) and I(b, d) show high consistency, linking dynamical systems and information processing.
  • Information import is maximal in low-density chaotic regimes and at the chaotic/fixed point border, not the 'edge of chaos'.
  • A novel 'Import Resonance' (IR) phenomenon demonstrates peak information import with varying input coupling strength.

Conclusions:

  • Network statistics significantly impact information import capabilities of RNNs.
  • Optimal information import occurs in specific dynamical regimes beyond the 'edge of chaos'.
  • Import Resonance (IR) and Recurrence Resonance (RR) offer mechanisms to optimize information processing in artificial and biological neural systems.