Research breakthrough · curated entry
Hopfield gives neural networks an energy function
Neural networks and physical systems with emergent collective computational abilities
- Date
- April 1982
- Category
- Research breakthrough
- Organization / Model
- Caltech
- Evidence
- 1 source · 1 evidence family
He mapped symmetrically connected recurrent networks onto spin-glass models, using an energy function to explain how a network settles into stable states and performs associative memory. It brought neural networks back into the shared view of physics and engineering, preparing the connectionist revival of the 1980s.
Curator's note: Cross-checked against the PNAS paper DOI; the 2024 Nobel Prize in Physics recognized this work.
Curated sources · 1
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Original paper · PNAS1982-04-01
Neural networks and physical systems with emergent collective computational abilities
doi.org
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GB/T 7714—2025 (electronic resource [EB/OL])
AI 纪年. Hopfield 用能量函数描述神经网络，连接主义重新获得物理基础[EB/OL]. (1982-04)[2026-11-03]. https://aihist.net/e/hopfield-1982.html.
APA 7 · American Psychological Association
AI 纪年. (1982, April). Hopfield 用能量函数描述神经网络，连接主义重新获得物理基础 [Web page]. https://aihist.net/e/hopfield-1982.html
MLA 9 · Modern Language Association
"Hopfield 用能量函数描述神经网络，连接主义重新获得物理基础." AI 纪年, April 1982, aihist.net/e/hopfield-1982.html. Accessed 3 Nov. 2026.
Chicago 17 · notes-bibliography
AI 纪年. "Hopfield 用能量函数描述神经网络，连接主义重新获得物理基础." Published April 1982. Accessed Nov. 3, 2026. https://aihist.net/e/hopfield-1982.html.
IEEE · citation-reference list
"Hopfield 用能量函数描述神经网络，连接主义重新获得物理基础," AI 纪年, Apr. 1982. [Online]. Available: https://aihist.net/e/hopfield-1982.html. [Accessed: 3 Nov. 2026].
ACM · author-date variant
AI 纪年. April 1982. Hopfield 用能量函数描述神经网络，连接主义重新获得物理基础. Retrieved November 3, 2026 from https://aihist.net/e/hopfield-1982.html.
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