Abstract
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 language | English |
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Pages (from-to) | 183-207 |
Number of pages | 25 |
Journal | Parallel Computing |
Volume | 25 |
Issue number | 2 |
DOIs | |
Publication status | Published - 1999 |
Keywords
- 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