Clearly, such a structure cannot readily be produced on a desktop while preserving its functionality. Desktop Migration Implementation of connectionist models on so called supercomputers has yielded maximum performance. Due to the obvious limitations of desktop PCs, reproduction of performance of this magnitude cannot be expected from current systems. The PC platform can be made more useful for neural network simulations by implementing a connectionist model in a more distributed and parallel fashion. Implementation of a neural net with an object-oriented architecture is a logical first step to this goal.
Object oriented programming offers many advantages to neural network simulators.
Strong typing, information hiding, and overloading are qualities well suited for neural net simulation. Additionally, object orientation decomposes a simulator into basic building blocks, which can be easily broken up and distributed. The design of the classic neural net lends itself readily to object orientation. Nodes and connections are prime examples of basic components that the neural net can be broken into. Data hiding is a both a welcome and needed feature for both nodes and connections.