Spiking Neural P Systems with Learning Functions

Tao Song, Linqiang Pan, Tingfang Wu, Pan Zheng, M. L. Dennis Wong, Alfonso Rodríguez-Patón

Research output: Contribution to journalArticle

16 Citations (Scopus)
99 Downloads (Pure)

Abstract

Spiking neural P systems (SN P systems) are a class of distributed and parallel neural-like computing models, inspired from the way neurons communicate by means of spikes. In this paper, a new variant of the systems, called SN P systems with learning functions, is introduced. Such systems can dynamically strengthen and weaken connections among neurons during the computation. A class of specific SN P systems with simple Hebbian learning function is constructed to recognize English letters. The experimental results show that the SN P systems achieve average accuracy rate 98.76% in the test case without noise. In the test cases with low, medium, and high noises, the SN P systems outperform back propagation neural networks and probabilistic neural networks. Moreover, comparing with spiking neural networks, SN P systems perform a little better in recognizing letters with noise. The result of this paper is promising in terms of the fact that it is the first attempt to use SN P systems in pattern recognition after many theoretical advancements of SN P systems, and SN P systems exhibit the feasibility for tackling pattern recognition problems.

Original languageEnglish
Pages (from-to)176-190
Number of pages15
JournalIEEE Transactions on NanoBioscience
Volume18
Issue number2
Early online date1 Feb 2019
DOIs
Publication statusPublished - Apr 2019

Keywords

  • Bio-inspired computing
  • learning
  • letter classification
  • membrane computing
  • spiking neural P system

ASJC Scopus subject areas

  • Biotechnology
  • Bioengineering
  • Medicine (miscellaneous)
  • Biomedical Engineering
  • Pharmaceutical Science
  • Computer Science Applications
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Spiking Neural P Systems with Learning Functions'. Together they form a unique fingerprint.

  • Cite this