Introducing time Network features Having looked at the component processors of artificial neural nets, it is time to compare the features of such nets with the von Neumann paradigm. The validity of some of these properties should become clearer as the operation of specific nets is described. Clearly the style of processing is completely different - it is more akin to signal processing than symbol processing. The combining of signals and producing new ones is to be contrasted with the execution of instructions stored in a memory.
Information is stored in a set of weights rather than a program.
The weights are supposed to adapt when the net is shown examples from a training set. High level concepts will be represented as a pattern of activity across many nodes rather than as the contents of a small portion of computer memory. These are just the tasks that the symbolic approach has difficulties with.