John Paul Strachan

21.6k total citations · 14 hit papers
123 papers, 16.0k citations indexed

About

John Paul Strachan is a scholar working on Electrical and Electronic Engineering, Cellular and Molecular Neuroscience and Artificial Intelligence. According to data from OpenAlex, John Paul Strachan has authored 123 papers receiving a total of 16.0k indexed citations (citations by other indexed papers that have themselves been cited), including 110 papers in Electrical and Electronic Engineering, 48 papers in Cellular and Molecular Neuroscience and 25 papers in Artificial Intelligence. Recurrent topics in John Paul Strachan's work include Advanced Memory and Neural Computing (108 papers), Ferroelectric and Negative Capacitance Devices (62 papers) and Neuroscience and Neural Engineering (46 papers). John Paul Strachan is often cited by papers focused on Advanced Memory and Neural Computing (108 papers), Ferroelectric and Negative Capacitance Devices (62 papers) and Neuroscience and Neural Engineering (46 papers). John Paul Strachan collaborates with scholars based in United States, Germany and Hong Kong. John Paul Strachan's co-authors include R. Stanley Williams, J. Joshua Yang, Wei Lü, Miao Hu, Mohammed A. Zidan, G. Medeiros‐Ribeiro, Ning Ge, Suhas Kumar, Qiangfei Xia and Zhiyong Li and has published in prestigious journals such as Nature, Physical Review Letters and Advanced Materials.

In The Last Decade

John Paul Strachan

115 papers receiving 15.7k citations

Hit Papers

Memristors with diffusive dynamics as synaptic emulato... 2010 2026 2015 2020 2016 2017 2016 2017 2018 500 1000 1.5k
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing
    2016 1.9k citations Zhongrui Wang, Saumil Joshi et al. Nature Materials profile →
  2. The future of electronics based on memristive systems
    2017 1.7k citations John Paul Strachan, Wei Lü et al. profile →
  3. ISAAC
    2016 1.1k citations Ali Shafiee, Anirban Nag et al. ACM SIGARCH Computer Architecture News profile →
  4. Analogue signal and image processing with large memristor crossbars
    2017 986 citations Can Li, Miao Hu et al. profile →
  5. Efficient and self-adaptive in-situ learning in multilayer memristor neural networks
    2018 711 citations Can Li, Miao Hu et al. Nature Communications profile →
  6. Memristor‐Based Analog Computation and Neural Network Classification with a Dot Product Engine
    2018 601 citations Miao Hu, Catherine E. Graves et al. Advanced Materials profile →
  7. Sub-nanosecond switching of a tantalum oxide memristor
    2011 566 citations Antonio C. Torrezan, John Paul Strachan et al. Nanotechnology profile →
  8. High switching endurance in TaOx memristive devices
    2010 533 citations J. Joshua Yang, John Paul Strachan et al. Applied Physics Letters profile →
  9. Dot-product engine for neuromorphic computing
    2016 522 citations Miao Hu, John Paul Strachan et al. profile →
  10. ISAAC: A Convolutional Neural Network Accelerator with In-Situ Analog Arithmetic in Crossbars
    2016 483 citations Ali Shafiee, Anirban Nag et al. profile →
  11. Chaotic dynamics in nanoscale NbO2 Mott memristors for analogue computing
    2017 476 citations Suhas Kumar, John Paul Strachan et al. profile →
  12. Dynamical memristors for higher-complexity neuromorphic computing
    2022 367 citations Suhas Kumar, John Paul Strachan et al. profile →
  13. Reinforcement learning with analogue memristor arrays
    2019 289 citations Zhongrui Wang, Can Li et al. profile →
  14. Power-efficient combinatorial optimization using intrinsic noise in memristor Hopfield neural networks
    2020 284 citations Suhas Kumar, Thomas Van Vaerenbergh et al. profile →

Peers

John Paul Strachan
Bin Gao China
Huaqiang Wu China
Shimeng Yu United States
He Qian China
Dmitri B. Strukov United States
Qiangfei Xia United States
Qing Wu United States
Abu Sebastian Switzerland
Daniele Ielmini Italy
Duncan R. Stewart United States
Bin Gao China
John Paul Strachan
Citations per year, relative to John Paul Strachan John Paul Strachan (= 1×) peers Bin Gao

Countries citing papers authored by John Paul Strachan

Since Specialization
Citations

This map shows the geographic impact of John Paul Strachan's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by John Paul Strachan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites John Paul Strachan more than expected).

Fields of papers citing papers by John Paul Strachan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by John Paul Strachan. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by John Paul Strachan. The network helps show where John Paul Strachan may publish in the future.

Co-authorship network of co-authors of John Paul Strachan

This figure shows the co-authorship network connecting the top 25 collaborators of John Paul Strachan. A scholar is included among the top collaborators of John Paul Strachan based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with John Paul Strachan. John Paul Strachan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Pedretti, Giacomo, et al.. (2025). Real-time raw signal genomic analysis using fully integrated memristor hardware. Nature Computational Science. 5(10). 940–951.
2.
3.
Pedretti, Giacomo, Xia Sheng, Jim Ignowski, et al.. (2024). Computing high-degree polynomial gradients in memory. Nature Communications. 15(1). 8211–8211. 10 indexed citations
4.
5.
Dick, Robert P., Rob Aitken, John Paul Strachan, et al.. (2023). Research Challenges for Energy-Efficient Computing in Automated Vehicles. Computer. 56(3). 47–58. 3 indexed citations
6.
Strachan, John Paul. (2023). Unleashing the Power of Moiré Materials in Neuromorphic Computing. Chinese Physics Letters. 40(12). 127202–127202. 1 indexed citations
7.
Kazemi, Arman, Ann Franchesca Laguna, Rui Lin, et al.. (2022). Experimentally validated memristive memory augmented neural network with efficient hashing and similarity search. Nature Communications. 13(1). 6284–6284. 43 indexed citations
8.
Li, Can, Catherine E. Graves, Xia Sheng, et al.. (2020). Analog content-addressable memories with memristors. Nature Communications. 11(1). 1638–1638. 124 indexed citations
9.
Graves, Catherine E., et al.. (2020). In‐Memory Computing with Memristor Content Addressable Memories for Pattern Matching. Advanced Materials. 32(37). e2003437–e2003437. 82 indexed citations
10.
Graves, Catherine E., Sity Lam, Xuema Li, et al.. (2019). Memristor TCAMs Accelerate Regular Expression Matching for Network Intrusion Detection. IEEE Transactions on Nanotechnology. 18. 963–970. 38 indexed citations
11.
Ge, Ning, Jung Ho Yoon, Miao Hu, et al.. (2017). An efficient analog Hamming distance comparator realized with a unipolar memristor array: a showcase of physical computing. Scientific Reports. 7(1). 40135–40135. 25 indexed citations
12.
Graves, Catherine E., et al.. (2017). Temperature and field-dependent transport measurements in continuously tunable tantalum oxide memristors expose the dominant state variable. Applied Physics Letters. 110(12). 36 indexed citations
13.
Shafiee, Ali, Anirban Nag, Naveen Muralimanohar, et al.. (2016). ISAAC. ACM SIGARCH Computer Architecture News. 44(3). 14–26. 1111 indexed citations breakdown →
14.
Wang, Zhongrui, Saumil Joshi, Sergey Savel’ev, et al.. (2016). Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing. Nature Materials. 16(1). 101–108. 1899 indexed citations breakdown →
15.
Dávila, Noraica, et al.. (2016). Repeatable, accurate, and high speed multi-level programming of memristor 1T1R arrays for power efficient analog computing applications. Nanotechnology. 27(36). 365202–365202. 145 indexed citations
16.
Shafiee, Ali, Anirban Nag, Naveen Muralimanohar, et al.. (2016). ISAAC: A Convolutional Neural Network Accelerator with In-Situ Analog Arithmetic in Crossbars. 14–26. 483 indexed citations breakdown →
17.
Kumar, Suhas, Matthew D. Pickett, John Paul Strachan, et al.. (2013). Local Temperature Redistribution and Structural Transition During Joule‐Heating‐Driven Conductance Switching in VO2. Advanced Materials. 25(42). 6128–6132. 187 indexed citations
18.
Feng, Miao, John Paul Strachan, J. Joshua Yang, et al.. (2012). Anatomy of a Nanoscale Conduction Channel Reveals the Mechanism of a High-Performance Memristor. Scholarworks (University of Massachusetts Amherst). 2012. 2 indexed citations
19.
Torrezan, Antonio C., John Paul Strachan, G. Medeiros‐Ribeiro, & R. Stanley Williams. (2011). Sub-nanosecond switching of a tantalum oxide memristor. Nanotechnology. 22(48). 485203–485203. 566 indexed citations breakdown →
20.
Acremann, Yves, John Paul Strachan, T. Tyliszczak, et al.. (2006). Time-Resolved Imaging of Spin Transfer Switching: Beyond the Macrospin Concept. Physical Review Letters. 96(21). 217202–217202. 129 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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