Similar to a biological neuron, the PE will activate only if the weighted sum of the inputs meets some criteria. Finally, the system will act on the error signal, depending upon the training style and its associated Training Rule. Most of the discussion which follows will be based upon one of the simpler forms of NN, the supervised, feedforward, backpropagation network. Other network models will be described in relation to this type. Transfer Functions Biological neurons activate only if the total incoming signal is above a certain value.
Artificial neurons are much the same way.
So far, CF's have been taken from statistics, thermodynamics, biology, and information theory. The training process adjusts the weights until the CF is minimized. A typical feedforward network might be used for such things as control systems and pattern identifiers. This is a standard type of NN, often implemented in A-Life projects. When dealing with patterns, or clusters of inputs, it assumes you already know how many patterns there are, and what they look like.