Many hundreds of Neural Network types have been proposed over the years. In fact, because Neural Nets are so widely studied (for example, by Computer Scientists, Electronic Engineers, Biologists and Psychologists), they are given many different names. It referred to as Artificial Neural Networks (ANNs), Connectionism or Connectionist Models, Multi-layer Percpetrons (MLPs) and Parallel Distributed Processing (PDP).
However, despite all the different terms and different types, there are a small group of “classic” networks which are widely used and on which many others are based. These are: Back Propagation, Hopfield Networks, Competitive Networks and networks using Spiky Neurons. There are many variations even on these themes.
Algorithm
Most people would consider the Back Propagation network to be the quintessential Neural Net. Actually, Back Propagation is the training or learning algorithm rather than the network itself. These are called Feed-Forward Networks or occasionally Multi-Layer Perceptrons (MLPs).
A Back Propagation network learns by example. You give the algorithm examples of what you want the network to do and it changes the network’s weights so that, when training is finished, it will give you the required output for a particular input. Back Propagation networks are ideal for simple Pattern Recognition and Mapping Tasks
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