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G.L. Heileman, M. Georgiopoulos, W.D. Roome, "A General Framework for Concurrent Simulation on Neural Network Models," IEEE Transactions on Software Engineering, vol. 18, no. 7, pp. 551562, July, 1992.  
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@article{ 10.1109/32.148474, author = {G.L. Heileman and M. Georgiopoulos and W.D. Roome}, title = {A General Framework for Concurrent Simulation on Neural Network Models}, journal ={IEEE Transactions on Software Engineering}, volume = {18}, number = {7}, issn = {00985589}, year = {1992}, pages = {551562}, doi = {http://doi.ieeecomputersociety.org/10.1109/32.148474}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
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TY  JOUR JO  IEEE Transactions on Software Engineering TI  A General Framework for Concurrent Simulation on Neural Network Models IS  7 SN  00985589 SP551 EP562 EPD  551562 A1  G.L. Heileman, A1  M. Georgiopoulos, A1  W.D. Roome, PY  1992 KW  concurrent simulation; neural network models; nonlinearities; discrete event nonlinear dynamical systems; continuoustime differential equations; discretetime difference equations; concurrent objectoriented discrete event simulation; objectoriented language; data abstraction; parallel architectures; generalpurpose parallel computer; data structures; discrete event simulation; neural nets; objectoriented programming; parallel languages VL  18 JA  IEEE Transactions on Software Engineering ER   
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 continuoustime differential equations or by discretetime difference equations. Specifically, the authors consider the design and construction of a concurrent objectoriented discrete event simulation environment for neural networks. The use of an objectoriented 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 generalpurpose parallel computer.
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