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Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
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Resource Selection in Cognitive Networks With Spiking Neural Networks.

Ricardo Lent1

  • 1Department of Engineering Technology, University of Houston, Houston, TX 77494 USA.

IEEE Transactions on Cognitive Communications and Networking
|November 12, 2019
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Summary
This summary is machine-generated.

This study introduces a spiking neural network controller for cognitive networking, optimizing resource selection for low-power neuromorphic chips. The approach effectively reduces file transfer times in challenging space communication scenarios.

Keywords:
Networkslearning systemsneural networkssimulationspace vehicle communication

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Neuroscience

Background:

  • Cognitive networking aims to dynamically optimize network resource allocation.
  • Low-power neuromorphic chips require efficient control mechanisms.
  • Spiking neural networks (SNNs) offer a biologically inspired, energy-efficient computation model.

Purpose of the Study:

  • To explore the feasibility of an SNN-based cognitive network controller (CNC).
  • To design a CNC capable of dynamic resource optimization for recurrent network tasks.
  • To evaluate the CNC's performance in a simulated space communication environment.

Main Methods:

  • Developed a CNC utilizing SNN principles for resource selection.
  • Implemented a spike-based coding strategy for action decisions.
  • Introduced a learning algorithm and synapse regulation method.
  • Simulated the CNC in a multichannel space communication link scenario.

Main Results:

  • The proposed CNC successfully optimized average file transfer time.
  • Achieved objective across a broad range of offered loads in a dynamic environment.
  • Demonstrated effectiveness compared to conventional methods.
  • Analyzed the impact of learning and space protocol parameters.

Conclusions:

  • The SNN-based CNC is a feasible approach for cognitive networking.
  • The CNC shows promise for optimizing resource selection in challenging, time-limited network environments.
  • This work facilitates the development of novel cognitive networking applications for neuromorphic hardware.