Backprop is not a learning rule. Backpropagation is a form of supervised learning. This would have been just as true of any computer programme with inputs and outputs. What is particular about neural nets is that they learn by changing the interconnectivity of neuron-like units. It's not patterns of activation that are stored so much as connection strengths, so that a given input will produce a certain pattern of activation.
Inputs modify the weights on the basis of supervision.
B.P. Kid sib wouldn't be able to figure it out from this that after the net produces an output, this is propagated backwards toward the input, strengthening all the connections that led to that output if it was correct, weakening them if it was wrong. For a better mark, explain more clearly and fully, and link to categorisation or supervision, or symbols, or unsupervised learning, etc.