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Using Inspiration from Synaptic Plasticity Rules to Optimize Traffic Flow in Distributed Engineered Networks.

Jonathan Y Suen1, Saket Navlakha2

  • 1Duke University, Department of Electrical and Computer Engineering. Durham, NC 27708, U.S.A. j.suen@duke.edu.

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This study introduces a neuro-inspired model for network flow control using synaptic plasticity rules. It demonstrates how brain-like plasticity can optimize traffic routing in engineered systems.

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Area of Science:

  • Neuroscience
  • Network Science
  • Computer Engineering

Background:

  • Controlling data flow is crucial for distributed networks like the Internet and transportation systems.
  • Synaptic plasticity in the brain allows for stable yet adaptable network activity.
  • Existing engineering models for network control often differ from biological mechanisms.

Purpose of the Study:

  • To develop a novel neuro-inspired model for network flow and routing control.
  • To demonstrate the application of biological synaptic plasticity rules in engineered networks.
  • To analyze the impact of different weight-update rules on network performance.

Main Methods:

  • Developed a model based on activity-dependent edge weight modification.
  • Modeled long-term potentiation and long-term depression as distributed gradient descent for traffic regulation.
  • Performed simulations and analytical characterizations of edge-weight-update rules.
  • Compared neuro-derived rules with engineering standards.

Main Results:

  • Neuro-inspired plasticity rules can effectively regulate traffic flow in engineered networks.
  • Different weight-update rules significantly impact network routing efficiency and robustness.
  • A strong correspondence was found between brain-derived synaptic plasticity and engineering control rules.

Conclusions:

  • Synaptic plasticity offers a viable, biologically-inspired approach to network flow control.
  • Common underlying principles may govern network regulation in both biological and engineered systems.
  • This work bridges neuroscience and engineering by applying brain mechanisms to solve network challenges.