A quick sketch of the anatomy and connection of neurons

Neural networks are a method of machine learning based on neurons in the brain. In a neuron, signals are received at the dendrites. If the combined triggering impulse is higher than a certain threshold, the neuron fires. The impulse travels down the cell membrane of the axon and is transmitted via the axon terminators to the dendrites of the next neuron. When the neuronic pathway is traveled often, the connections are reinforced. The axon of the preceeding neuron and the dendrites of the subsequent neuron grow to be more highly connected. It is in this way that we learn. and why we can change behaviour patterns once they are established. Forcing the impulse to take another route results in an altered behaviour or thought.

This same approach is used to teach a computer how to 'think' about a problem. The neurons in the system become nodes. The nodes are arranged in several layers. "Impulses" begin at the input layer, travel through any number of hidden layers, and terminate at the output layer. The output layer gives the computer its learned behaviour.

Nodes can be highly interconnected. The connections themselves have a weight, which corresponds to the number of axon-dendrite connections in neurons. Nodes that are connected with a heigher weight are more likely to fire.

Neural networks must be trained in order to function properly. Untrained neural networks are like newborn babies: cute, but unable to perform any higher functions. They must be taught.