This way of organizing the simulations is important. Cutting across the entrenched equation- based simulation of specialized models, this scheme is responsible for our ability to mix neural models and provide design flexibility. OOP designs have an enormous advantage with respect to simulation speed. Traditionally, interpreted environments are flexible but slow. On the other hand, compiled code is very fast, but it normally restricts the user interface.
Object oriented designs combine the advantages of both methods.
Object instantiation provides the user with a great deal of flexibility, while the objects themselves still run a compiled code. This severe shortcoming yielded suboptimal performance on a huge number of applications such as process control, time series analysis and prediction, and classification of non-stationary signals. The result was a complete coverage of temporal processing with ANNs. Because these enhancements fit precisely with the rest of the algorithmic structure we developed, we believe that our approach captures the essence of neural computation. Implementing a neural network was often more a programming task then a design and experimentation task.