The information is thus unchanging in the short term , requiring an evolutionary process to change the DNA encoded information. Neural 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. However, it is the object of the course to make clear the terms used in this definition and to expand considerably on its content. A Neural Network is an interconnected assembly of simple processing elements, units or nodes, whose functionality is loosely based on the animal neuron.