This is not true for backpropagation in a network with more than one modifiable connection layers. Here only a technical description is given.
Figure shows a typical architecture for networks with backpropagation as the learning rule.
There is also no lateral connectivity within the layers. Before learning, the weights of these connections are set to small random values. This input pattern determines the activations of the input nodes. The bias node has an activation of 1 during the whole learning process, and it is connected to each hidden and output layer node. The weights of these connections are changed by learning, just like all other weights.