was rediscovered and described in 1985 by Parker, and in 1986 by Rumelhart, Hinton and Williams. Rumelhart, Hinton and Williams showed experimentally that this method can generate useful internal representations of incoming data in hidden layers of neural networks. Yann LeCun proposed the modern form of the back-propagation learning algorithm for neural networks in his PhD thesis in 1987. In 1993, Eric Wan won an international pattern recognition contest through backpropagation. During the 2000s it fell out of favour, but returned in the 2010s, benefitting from cheap, powerful GPU-based computing systems. This has been especially so in speech recognition, machine vision, natural language processing, and language structure learning research (in which it has been used to explain a variety of phenomena related to first and second language learning.). Error backpropagation has been suggested to explain human brain ERP components like the N400 and P600. See also Artificial neural network Neural circuit Catastrophic interference Ensemble learning AdaBoost Overfitting Neural backpropagation Backpropagation through time Notes References Further reading External links Backpropagation neural network tutorial at the Wikiversity Machine learning algorithms Artificial neural networks