An ANN consists of a number of neurons simulated in a computer with connections between them. Each of these connections has a certain strength, or “weight”, indicating how strong the connection is. A connection with a large weight is efficient at passing on a signal from one neuron to another. The information and expertise that ANNs possess is coded in these strengths between neurons.
Simulated neurons can be connected in various patterns, termed “architectures”. Although complex architectures exist (for instance in Cell Assemblies, which model closely structures in the human brain), the simplest architecture, the Multi-Layer Perceptron, is probably the most common. An MLP consists of neurons in distinct layers, each of which is connected to the next layer along. Signals can pass only one way through the MLP, from the inputs of the first layer to the outputs of the last one.