Neural simulation of Petri nets

S. W. Chen, C. Y. Fang, K. E. Chang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


Petri nets and neural networks share a number of analogies. Investigations of their relationships can be sorted into two categories: (a) the modeling of neural activities with Petri nets, and (b) the neural simulation of Petri nets. The work presented in this paper belongs to the second category. Unlike divide-and-conquer approaches, the proposed method settles the extraneous skeleton of simulators. Inherent distinctions of Petri nets are characterized by the individual constituents of simulators. The constructed simulators thus reveal a consistently uniform structure on a macroscopic level. Compared with those generated by the divide-and-conquer approaches, ours look much portable and are empirically economic. Furthermore, in a fully parallel machine with enough nodes the overall time complexity of the neural simulator will be constant.

Original languageEnglish
Pages (from-to)183-207
Number of pages25
JournalParallel Computing
Issue number2
Publication statusPublished - 1999


  • Aggregator
  • Associator
  • Competer
  • Equivalence proof
  • Formatter
  • Latcher
  • Neural simulators
  • Petri nets

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Hardware and Architecture
  • Computer Networks and Communications
  • Computer Graphics and Computer-Aided Design
  • Artificial Intelligence


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