The neural-net movement had a resurgence in the 1980s using a method called “backpropagation,” in which the strength of each simulated synapse was determined using a learning algorithm that adjusted the weight (the strength of the output) of each artificial neuron after each training trial so the network could “learn” to more correctly match the right answer.
