Although Turing awardee and backpropagation pioneer Geoffrey Hinton's interests have largely shifted to unsupervised learning, he recently co-authored a paper that takes a look back at backpropagation and explores its potential to contribute to understanding how the human cortex learns. Hinton and a team of researchers from DeepMind, University College London, and University of Oxford published the paper last Friday on Nature Reviews Neuroscience. Their main idea is that biological brains could compute effective synaptic updates by using feedback connections to induce neuron activities whose locally computed differences encode backpropagation-like error signals. Backpropagation of errors, or backprop, is a widely used algorithm in training artificial neural networks using gradient descent for supervised learning. The basics of continuous backpropagation were proposed in the 1960s, and in 1986 a Nature paper co-authored by Hinton showed experimentally that backprop can generate useful internal representations for neural networks.