It is composed of neurons or nodes, each one of them a simple model of a physiological neuron, and weights , which are connections with different strength between two neurons. Usually neurons are arranged in layers , which has a physiological basis too.
Neural networks are useful in artificial life because of their capability to learn .
That usually means that they can adapt by changing the value of connections between neurons. That stable state is considered the output of the network. In these networks all neurons act as input and output neurons, which means that they might all receive inputs from the environment, and its output used to perform some action. Although all kinds of neural networks are potentially useful in artificial life, not all of them are usually seen in artificial worlds. Supervised neural networks are usually meaningless, since agents do not have a professor that tells them continuously what they should do in response to each possible input.