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Nonlinear Spiking Neural P Systems.

Hong Peng1, Zeqiong Lv1, Bo Li1

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

International Journal of Neural Systems
|March 15, 2020
PubMed
Summary

This study introduces nonlinear spiking neural P systems (NSNP systems), a novel computing model. These systems demonstrate Turing-universality, proving their capability for complex computations and number generation.

Keywords:
Membrane computingnonlinear spiking neural P systemsregister machinesspiking neural P systemsuniversality

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Area of Science:

  • Theoretical Computer Science
  • Computational Intelligence
  • Biologically Inspired Computing

Background:

  • Spiking neural P systems (SNP systems) are a class of computational models inspired by biological neurons.
  • Existing SNP systems have limitations in modeling complex neuronal dynamics.

Purpose of the Study:

  • To introduce a new variant of spiking neural P systems called nonlinear spiking neural P systems (NSNP systems).
  • To analyze the computational power of these NSNP systems.

Main Methods:

  • NSNP systems utilize real numbers for neuron states and employ nonlinear spiking rules.
  • The firing of neurons involves nonlinear functions dependent on the neuron's state.
  • These systems operate as distributed, parallel, and nondeterministic computing entities.

Main Results:

  • The computational power of NSNP systems is formally investigated.
  • It is proven that NSNP systems possess Turing-universality for number-generating and accepting tasks.
  • Two small universal NSNP systems were constructed for function computation and number generation, with 117 and 164 neurons respectively.

Conclusions:

  • NSNP systems represent a significant advancement in the field of P systems and neural computing.
  • The demonstrated Turing-universality highlights their potential for solving complex computational problems.
  • The development of small universal NSNP systems offers practical implications for efficient computation.