The backpropagation algorithm is a learning rule for multi-layered Neural Networks, credited to Rumelhart and McClelland. The algorithm gives a prescription for adjusting the initially randomized set of synaptic weights (existing between all pairs of neurons in each successive layer of the network) so as to maximize the difference between the network's output of each input fact and the output with which the given input is known (or desired) to be associated. The backpropagation rule takes its name from the way in which the calculated error at the output layer is propagated backwards from the output layer to the Nth hidden layer to the (Nl)th hidden layer, and so on. Because this learning process requires us to "know" the correct pairing of input-output facts beforehand, this type of weight adjustment is called supervised learning.