AI Research Atlas
Journal article

Neural learning

Learning internal representations

Backpropagation demonstrated how hidden units could learn useful internal representations.

David E. Rumelhart, Geoffrey E. Hinton and Ronald J. Williams

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The contribution

The paper explained how errors at a network output could guide weight changes in earlier layers. Its examples showed that a multilayer network could learn internal features, helping make gradient-based learning a practical research direction.

What this does not establish

This influential paper did not invent every form of reverse-mode differentiation or all earlier uses of backpropagation. Credit for the broader history is shared.

Why this date?

The Nature paper was published on 9 October 1986.

This entry follows the linked publication. Read the source and date conventions.

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