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Updated: Apr 21, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Multistate network for loop searching system with self-recovery property.

Kei-Ichi Ueda¹, Masaaki Yadome², Yasumasa Nishiura²

¹Graduate School of Science and Engineering, University of Toyama, Toyama 930-8555, Japan.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics

|October 30, 2014

Summary

This summary is machine-generated.

This study introduces a network of FitzHugh-Nagumo excitable systems that self-organize to find loops. The network demonstrates self-recovery by re-establishing loops after connection disruptions using nonlinear dynamics.

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Last Updated: Apr 21, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Area of Science:

Nonlinear Dynamics
Network Science
Computational Neuroscience

Background:

Excitable systems, like the FitzHugh-Nagumo model, exhibit complex behaviors.
Understanding network dynamics is crucial for designing robust systems.
Spontaneous loop formation and recovery are key challenges in network design.

Purpose of the Study:

To propose a novel network of excitable systems capable of spontaneous loop searching.
To investigate mechanisms for self-recovery in networks with dynamic connections.
To leverage nonlinear dynamical properties for network resilience.

Main Methods:

Utilizing FitzHugh-Nagumo type excitable nodes with three equilibrium states.
Analyzing network attractors to identify stationary loop solutions.
Investigating regulatory rules for node interactions.
Applying postinhibitory rebound and saddle-node bifurcation phenomena.

Main Results:

The proposed network spontaneously initiates and completes loop searching.
Stationary solutions forming loops are identified as network attractors.
The system demonstrates self-recovery capabilities upon removal of connection links.
Nonlinear dynamical properties enable the system to find loops even after disruptions.

Conclusions:

The network effectively functions as a loop-searching system.
Postinhibitory rebound and saddle-node bifurcation are key to achieving self-recovery.
This approach offers a framework for designing resilient and adaptive networks.