Historical milestone · 1986
Backpropagation for distributed representations
Reviewed through September 18, 2026
1986 · Historical milestone
Backpropagation for distributed representations
- Era
- 1980s
- Theme
- Machine-learning foundations
- Evidence form
- Multilayer networks adjusted weights by propagating output error gradients backward
- School / paradigm
- Connectionism
- Institution / context
- UC San Diego / Carnegie Mellon
- Researchers
- David Rumelhart; Geoffrey Hinton; Ronald Williams
Researcher index
Geoffrey Hinton
Deep representation learning · CMU / Toronto
Backpropagation and deep belief nets
Why it still matters. Central architect of the connectionist revival and deep learning.
Representative source for this researcher — not necessarily the source of this milestone: https://doi.org/10.1162/neco.2006.18.7.1527 (opens in a new tab)
School of thought
Connectionism
Matched on school name.
Cognition emerges from learned distributed representations and weighted interactions among simple units.
Critique. Opacity, data/compute demands, unstable optimization, and weak guarantees or causal grounding.
Modern descendants. Foundation models, multimodal networks, representation learning, and differentiable agents.
Understand
Plain-language record, transferred from the reviewed source module.
Theory or experimental setup. Showed hidden units can learn task-relevant internal features and made multilayer representation learning practical.
Result / historical claim. Training was compute- and data-limited, gradients can vanish or overfit, and biological plausibility was disputed.
Apply
Professional implication, only where the reviewed record states one.
The checked-in record does not state a separate professional application for this entry. The topic page places it in the wider research lineage: .
Verify
Evidence status, stated limitations, and the external sources this record actually carries.
Evidence form. Multilayer networks adjusted weights by propagating output error gradients backward
Limitation / debate. Deep learning, end-to-end differentiable systems, representation learning, and foundation models.
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Cite or share
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