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<p>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. The authors 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, the authors 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.</p>
concurrent simulation; neural network models; nonlinearities; discrete event nonlinear dynamical systems; continuous-time differential equations; discrete-time difference equations; concurrent object-oriented discrete event simulation; object-oriented language; data abstraction; parallel architectures; general-purpose parallel computer; data structures; discrete event simulation; neural nets; object-oriented programming; parallel languages

W. Roome, M. Georgiopoulos and G. Heileman, "A General Framework for Concurrent Simulation on Neural Network Models," in IEEE Transactions on Software Engineering, vol. 18, no. , pp. 551-562, 1992.
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