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No one knew how to effectively train artificial neural networks with hidden layers - until 1986, when Hinton, the late David Rumelhart and Ronald Williams (now of Northeastern University) published the backpropagation algorithm. However, it was obvious even in the 1960s that solving more complicated problems required one or more “hidden” layers of neurons sandwiched between the input and output layers. During training, a neural network settled on the best weights for its neurons to eliminate or minimize errors. By the 1960s, it was clear that such neurons could be organized into a network with an input layer and an output layer, and the artificial neural network could be trained to solve a certain class of simple problems. The neuron multiplies each input with a so-called “synaptic” weight - a number signifying the importance assigned to that input - and then sums up the weighted inputs.
Each artificial neuron in these networks receives multiple inputs and produces an output, like its biological counterpart. Nevertheless, it was the best learning rule that neuroscientists had, and even before it dominated neuroscience, it inspired the development of the first artificial neural networks in the late 1950s. “The Hebbian rule is a very narrow, particular and not very sensitive way of using error information,” said Daniel Yamins, a computational neuroscientist and computer scientist at Stanford University.
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This principle, with some modifications, was successful at explaining certain limited types of learning and visual classification tasks.īut it worked far less well for large networks of neurons that had to learn from mistakes there was no directly targeted way for neurons deep within the network to learn about discovered errors, update themselves and make fewer mistakes. “But it also has value in its own right.” Learning Through Backpropagationįor decades, neuroscientists’ theories about how brains learn were guided primarily by a rule introduced in 1949 by the Canadian psychologist Donald Hebb, which is often paraphrased as “Neurons that fire together, wire together.” That is, the more correlated the activity of adjacent neurons, the stronger the synaptic connections between them. There’s a general impression that if we can unlock some of its principles, it might be helpful for AI,” said Bengio. All these efforts are bringing us closer to understanding the algorithms that may be at work in the brain. Some researchers are also incorporating the properties of certain types of cortical neurons and processes such as attention into their models. Three of them - feedback alignment, equilibrium propagation and predictive coding - have shown particular promise. Bengio and many others inspired by Hinton have been thinking about more biologically plausible learning mechanisms that might at least match the success of backpropagation.
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