A General Framework for Concurrent Simulation Of Neural Network Models

Gregory L. Heileman, Michael Georgiopoulos, William D. Roome

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

The analysis of complex neural network models via analytical techniques is often quite difficult due to the large numbers of components involved, and the nonlinearities associated with these components. For this reason, simulation is seen as an important tool in neural network research. In this paper we present a framework for simulating neural networks as discrete event nonlinear dynamical systems. This includes neural network models whose components are described by continuous-time differential equations, or by discrete-time difference equations. Specifically, we consider the design and construction of a concurrent object-oriented discrete event simulation environment for neural networks. The use of an object-oriented language provides the data abstraction facilities necessary to support modification and extension of the simulation system at a high level of abstraction. Furthermore, the ability to specify concurrent processing supports execution on parallel architectures. The use of this system is demonstrated by simulating a specific neural network model on a general-purpose parallel computer.

Original languageEnglish (US)
Pages (from-to)551-562
Number of pages12
JournalIEEE Transactions on Software Engineering
Volume18
Issue number7
DOIs
StatePublished - Jul 1992
Externally publishedYes

Keywords

  • Concurrent simulation
  • linear dynamical systems
  • neural networks
  • non-
  • object-oriented programming
  • parallel processing

ASJC Scopus subject areas

  • Software

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