John Paul Strachan

Active 2006–2026

120
Papers
21,249
Citations
50
h-index
73
i10-index

Citations

Citations per year for John Paul Strachan1980: 1 citations1987: 1 citations2005: 3 citations2007: 3 citations2008: 4 citations2009: 3 citations2010: 2 citations2011: 41 citations2012: 66 citations2013: 100 citations2014: 74 citations2015: 107 citations2016: 173 citations2017: 326 citations2018: 628 citations2019: 1,224 citations2020: 1,664 citations2021: 1,540 citations2022: 1,522 citations2023: 1,362 citations2024: 1,391 citations2025: 952 citations2026: 143 citations1981–1986: no citations, so these years are not shown1988–2004: no citations, so these years are not shown2006: no citations, so this year is not shown

Citation sources

Countries

World map of the countries and regions citing this authorChina: 2,251 citing papers, 24.7% of this breakdownUnited States: 2,187 citing papers, 24% of this breakdownSouth Korea: 610 citing papers, 6.7% of this breakdownGermany: 478 citing papers, 5.2% of this breakdownUnited Kingdom: 294 citing papers, 3.2% of this breakdownItaly: 273 citing papers, 3% of this breakdownSingapore: 264 citing papers, 2.9% of this breakdownIndia: 255 citing papers, 2.8% of this breakdownHong Kong: 220 citing papers, 2.4% of this breakdownTaiwan: 181 citing papers, 2% of this breakdownAustralia: 168 citing papers, 1.9% of this breakdownJapan: 158 citing papers, 1.7% of this breakdown
0%24.7%Other 19.5%

Fields

  • Engineering80.3%
  • Computer Science15.7%
  • Physics and Astronomy1.6%
  • Neuroscience0.6%
  • Biochemistry, Genetics and Molecular Biology0.5%
  • Materials Science0.3%
  • Other1%

Topics

  • Advanced Memory and Neural Computing28.8%
  • Ferroelectric and Negative Capacitance Devices13.8%
  • Neuroscience and Neural Engineering9.7%
  • Neural dynamics and brain function5.2%
  • Photoreceptor and optogenetics research5.1%
  • Neural Networks and Reservoir Computing5%
  • Other32.4%

Coauthors

All papers

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  1. The future of electronics based on memristive systems

    Authors: , , - Nature Electronics 2017 cited by 2,066

  2. Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing

    Authors: , , , , , , , , , , , , , , , , - Nature Materials 2016 cited by 2,299

  3. ISAAC

    Authors: , , , , , , , - ACM SIGARCH Computer Architecture News 2016 cited by 1,533

  4. ISAAC: A Convolutional Neural Network Accelerator with In-Situ Analog Arithmetic in Crossbars

    Authors: , , , , , , , - ACM/IEEE 43rd Annual International Symposium on Computer Architecture (ISCA) 2016 cited by 702

  5. Analogue signal and image processing with large memristor crossbars

    Authors: , , , , , , , , , , , , , , , , , , - Nature Electronics 2017 cited by 1,265

  6. Dynamical memristors for higher-complexity neuromorphic computing

    Authors: , , , , - Nature Reviews Materials 2022 cited by 528

  7. Fully memristive neural networks for pattern classification with unsupervised learning

    Authors: , , , , , , , , , , , , , , , , , , , , , , , - Nature Electronics 2018 cited by 1,092

  8. Efficient and self-adaptive in-situ learning in multilayer memristor neural networks

    Authors: , , , , , , , , , , , , , , , , - Nature Communications 2018 cited by 903

  9. PUMA: A Programmable Ultra-efficient Memristor-based Accelerator for Machine Learning Inference

    Authors: , , , , , , , , , , - Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS 2019 cited by 403

  10. Power-efficient combinatorial optimization using intrinsic noise in memristor Hopfield neural networks

    Authors: , , , , , , , , , , , , , - Nature Electronics 2020 cited by 397

  11. Memristor‐Based Analog Computation and Neural Network Classification with a Dot Product Engine

    Authors: , , , , , , , , , , , - Advanced Materials 2018 cited by 738

  12. Dot-product engine for neuromorphic computing: programming 1T1M crossbar to accelerate matrix-vector multiplication

    Authors: , , , , , , , , , - Design Automation Conference, DAC 2016 cited by 678

  13. Chaotic dynamics in nanoscale NbO2 Mott memristors for analogue computing

    Authors: , , - Nature 2017 cited by 627

  14. In situ training of feed-forward and recurrent convolutional memristor networks

    Authors: , , , , , , , , , , , , , , , , , - Nature Machine Intelligence, Nat. Mach. Intell. 2019 cited by 306

  15. Reinforcement learning with analogue memristor arrays

    Authors: , , , , , , , , , , , , , , , , , , - Nature Electronics 2019 cited by 370

  16. Long short-term memory networks in memristor crossbar arrays

    Authors: , , , , , , , , , , , , , , , , - Nature Machine Intelligence, Nat. Mach. Intell. 2018 cited by 389

  17. Capacitive neural network with neuro-transistors

    Authors: , , , , , , , , , , , , , , , , , , , , , , , - Nature Communications 2018 cited by 273

  18. Experimentally validated memristive memory augmented neural network with efficient hashing and similarity search

    Authors: , , , , , , , , , , , , - Nature Communications 2022 cited by 68

  19. Sub-nanosecond switching of a tantalum oxide memristor

    Authors: , , , - Nanotechnology 2011 cited by 695

  20. Rescuing Memristor-based Neuromorphic Design with High Defects

    Authors: , , , - Design Automation Conference 2017, DAC 2017 cited by 255

  21. Low‐Conductance and Multilevel CMOS‐Integrated Nanoscale Oxide Memristors

    Authors: , , , , , , , , - Advanced Electronic Materials 2019 cited by 106

  22. In‐Memory Computing with Memristor Content Addressable Memories for Pattern Matching

    Authors: , , , , , , - Advanced Materials 2020 cited by 103

  23. High‐Speed and Low‐Energy Nitride Memristors

    Authors: , , , , , , , , - Advanced Functional Materials 2016 cited by 348

  24. Physical origins of current and temperature controlled negative differential resistances in NbO2

    Authors: , , , , , , , , , , - Nature Communications 2017 cited by 210