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Related Experiment Videos

Synchronization phenomena in pulse-coupled networks driven by spike-train inputs.

Hiroyuki Torikai1, Toshimichi Saito

  • 1Department of Electronics, Electrical and Computer Engineering, Hosei University, Koganei, Tokyo 184-8584, Japan. torikai@k.hosei.ac.jp

IEEE Transactions on Neural Networks
|September 24, 2004
PubMed
Summary

A novel pulse-coupled network (PCN) of spiking oscillators (SOCs) demonstrates input decomposition and clustering phenomena. This electrical circuit exhibits multiple synchronization behaviors dependent on initial states, confirmed experimentally.

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Area of Science:

  • Nonlinear dynamics
  • Complex systems
  • Circuit theory

Background:

  • Spiking oscillators (SOCs) exhibit complex dynamics.
  • Pulse-coupled networks (PCNs) are used to model interacting oscillators.
  • Understanding synchronization phenomena in coupled oscillators is crucial.

Purpose of the Study:

  • To introduce a PCN of SOCs implemented as a simple electrical circuit.
  • To investigate the synchronization phenomena exhibited by the PCN when subjected to spike-train inputs.
  • To analyze the conditions and stability of observed synchronization patterns.

Main Methods:

  • Implementation of a spiking oscillator (SOC) with a periodic reset level.
  • Construction of a pulse-coupled network (PCN) using SOCs.

Related Experiment Videos

  • Application of spike-train inputs to the PCN.
  • Experimental verification using a test circuit.

    • Main Results:

    • The PCN demonstrated two main phenomena: input decomposition without overlap and input decomposition with overlapping leading to clustering.
    • Multiple synchronization phenomena were observed, with the specific phenomenon depending on the initial state of the network.
    • The study clarified the number of synchronization phenomena and their corresponding parameter regions, alongside their stability.
    • Experimental results confirmed the typical phenomena observed in the PCN.

    Conclusions:

    • The developed PCN of SOCs offers a simple yet effective model for complex dynamic behaviors.
    • The network exhibits rich input-processing capabilities, including decomposition and clustering, through synchronization.
    • The findings provide insights into the control and prediction of synchronization in coupled oscillator systems.