Multiple layers are then arrayed in a network. Layers that are between the input layer and the output layer are called hidden layers. The number of neurons in each layer need not be the same as in other layers, though in general there is a reasonable match-up between the number of inputs and outputs. E.g., a neural net with 10 inputs and 2 outputs would have 10 neurons in the input layer and 2 neurons in the output layer. If this network had two hidden layers, there might be 10 neurons in the first hidden layer and 5 neurons in the second hidden layer.
Neural nets are generally fully-connected.
This means that every neuron in a layer has as inputs to it, the output of every neuron from the previous layer or the inputs from the environment if it is the first layer. After processing each set of inputs through all neurons and all layers, the neural net will have a set of one or more outputs. If the neural net is being trained, then for each set of inputs presented there will also be set of target outputs. These targets are also called goals that the user has determined the neural net should learn to produce when presented with that or a very similar set of inputs. The first several times that the network produces an output, its outputs and the targets will be nothing alike.