John Hopfield Google Scholar: h-index, Total Citations, and Citation Map
John Hopfield's h-index is 96 (182 i10-index, 98,364+ total citations across 306+ publications) according to Google Scholar as of October 2026. John Hopfield is affiliated with Professor, Princeton University.
John Hopfield is a researcher affiliated with Professor, Princeton University, specializing in Neural Networks, AI, Neuroscience. Their work has been cited 98,364 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
John Hopfield's Citation Metrics
Bibliometric impact based on 306 indexed publications.
- H-Index
- 96
- i10-Index
- 182
- Total Citations
- 98,364
- Citing Countries
- 81
As of October 2026.
John Hopfield has an h-index of 96 and 98,364 total citations across 306 publications, with research cited by institutions in 81 countries.
Global Impact Map
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Neural networks and physical systems with emergent collective computational abilities.
198230,416
Top Citing Countries
Top Citing Institutions
Visa Evidence Package
Views and exports tuned for EB-1A, O-1A, and EB-2 NIW petitions. Sustained acclaim, geographic reach, and independent-citation filtering are the strongest evidence categories immigration adjudicators look for.
Significant Contributions
Auto-detected research lines — a seminal paper and the follow-up work building on it. Review and edit before using in a petition. Each Free PDF opens in a new tab — EB-1A organises this into the structure USCIS applies to Criterion 5 of 8 CFR § 204.5(h)(3)(v); EB-1B re-frames it under § 204.5(i)(3) (outstanding researcher); NIW presents it under prong 2 of Matter of Dhanasar.
684 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher established foundational theoretical frameworks linking neural networks to physical systems with emergent collective computational abilities, significantly advancing the field of computational neuroscience.
The researcher pioneered the application of neural network models to solve complex optimization problems, establishing a foundational framework for computational decision-making processes.
The researcher established a foundational computational model of neural circuits, published in Science in 1986, which has become a seminal reference point in the field with over 3,000 citations.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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About John Hopfield's research
John Hopfield is a researcher in Neural Networks, AI and Neuroscience at Professor, Princeton University. Their work has been cited 98,364 times across 306 publications (h-index 96), according to Google Scholar.
Their most-cited work, “Neural networks and physical systems with emergent collective computational abilities.” (1982), has accumulated 30,416 citations. Other influential works include “Neural networks and physical systems with emergent collective computational abilities.” (1982) with 30,416 citations and “Neurons with graded response have collective computational properties like those of two-state neurons.” (1984) with 10,078 citations.
Citations of John Hopfield's research come primarily from United States, China and United Kingdom, reflecting international research impact across 5+ countries. The interactive citation map above shows the full geographic distribution of the institutions citing this work.
