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Spiking Neural P Systems with Extended Channel Rules.

Zeqiong Lv1, Tingting Bao1, Nan Zhou1

  • 1School of Computer and Software Enginering, Xihua University, Chengdu, 610039, P. R. China.

International Journal of Neural Systems
|August 19, 2020
PubMed
Summary

This study introduces spiking neural P systems with extended channel rules (SNP-ECR systems), a novel computing model. These systems offer enhanced neuron control and are proven to be Turing universal for number generation and function computation.

Keywords:
Membrane computingTuring universalityspiking neural P systemsspiking neural P systems with extended channel rules

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Area of Science:

  • Theoretical Computer Science
  • Computational Intelligence
  • Biologically Inspired Computing

Background:

  • Spiking neural P systems (SNP systems) are distributed parallel computing models inspired by biological neurons.
  • Existing SNP systems have limitations in controlling spike transmission between neurons.

Purpose of the Study:

  • To introduce and analyze a new variant of SNP systems called SNP systems with extended channel rules (SNP-ECR systems).
  • To investigate the computational power and universality of SNP-ECR systems.

Main Methods:

  • Introduction of a new spiking rule, the Extended Channel Rule (ECR), allowing neurons to send variable numbers of spikes.
  • Formal analysis of SNP-ECR systems as number generating/accepting devices.
  • Construction of a small universal SNP-ECR system for function computation.

Main Results:

  • SNP-ECR systems demonstrate a stronger firing control mechanism compared to standard SNP systems.
  • It is proven that SNP-ECR systems are Turing universal in their number generating and accepting capabilities.
  • A small universal SNP-ECR system capable of computing functions has been developed.

Conclusions:

  • SNP-ECR systems represent a significant advancement in the field of P systems, offering enhanced computational power.
  • The Turing universality of SNP-ECR systems confirms their potential for complex computations.
  • This research opens new avenues for exploring biologically inspired computing models with advanced control mechanisms.