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Noise-constrained switching times for heteroclinic computing.

Fabio Schittler Neves1, Maximilian Voit1, Marc Timme1

  • 1Network Dynamics, Max Planck Institute for Dynamics and Self-Organization, 37077 Göttingen, Germany.

Chaos (Woodbury, N.Y.)
|April 3, 2017
PubMed
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Heteroclinic computing uses dynamic networks for computation. This study reveals how noise and system dynamics affect switching times in pulse-coupled systems, crucial for reliable analog computation under natural conditions.

Area of Science:

  • Computational neuroscience
  • Complex systems dynamics
  • Analog computing

Background:

  • Heteroclinic computing leverages collective system dynamics for universal computation.
  • Input signals are encoded as orbits approaching saddle states within heteroclinic networks.
  • Pulse-coupled oscillators and spiking neurons naturally form these networks, offering a substrate for analog computation.

Purpose of the Study:

  • To systematically investigate switching times in pulse-coupled systems.
  • To understand the influence of noise and intrinsic dissipation on switching times.
  • To explore the relationship between switching times, computation reliability, and signal intensity coding.

Main Methods:

  • Systematic investigation of switching times in dependence of noise and intrinsic dissipation levels.

Related Experiment Videos

  • Analysis of the interplay between local pulse responses and external noise.
  • Characterization of switching times and computation reliability.

    • Main Results:

    • Switching times in pulse-coupled systems increase exponentially with the number of switches, influenced by noise levels.
    • Switching times serve as a predictor for computation reliability.
    • Identified a complementary coding scheme for signal intensity, moving beyond signal identity coding.

    Conclusions:

    • Findings provide insights into heteroclinic computing under noisy, natural conditions.
    • Switching time characterization is key for designing reliable heteroclinic computing hardware.
    • Results pave the way for coding signal intensity in heteroclinic computing systems.