Neural networks are typically structured in layers. Layers are made up of a number of interconnected 'nodes' which contain an 'activation function'. Inputs are presented to the network via input layers. Input layers are connected to the hidden layers, which communicate with output layers. The actual processing is done via a system of weighted 'connections'.
Connections are unidirectional communication channels from one node to another.
Once a network has been set up for a particular application, that network is ready to be trained. Most Artificial Neural Networks contain some form of 'learning rule', which modifies the weights of the connections to different layers according to the input patterns that it is fed with. To start, initial weights of the connections are chosen randomly. The network processes the inputs and compares its resulting outputs against the desired outputs. Errors are then propagated back through the system, which causes the system to adjust the weight.