Thomas Dalgaty

776 total citations
26 papers, 468 citations indexed

About

Thomas Dalgaty is a scholar working on Electrical and Electronic Engineering, Cognitive Neuroscience and Artificial Intelligence. According to data from OpenAlex, Thomas Dalgaty has authored 26 papers receiving a total of 468 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Electrical and Electronic Engineering, 9 papers in Cognitive Neuroscience and 8 papers in Artificial Intelligence. Recurrent topics in Thomas Dalgaty's work include Advanced Memory and Neural Computing (23 papers), Ferroelectric and Negative Capacitance Devices (12 papers) and Neural dynamics and brain function (9 papers). Thomas Dalgaty is often cited by papers focused on Advanced Memory and Neural Computing (23 papers), Ferroelectric and Negative Capacitance Devices (12 papers) and Neural dynamics and brain function (9 papers). Thomas Dalgaty collaborates with scholars based in France, Switzerland and Italy. Thomas Dalgaty's co-authors include Elisa Vianello, N. Castellani, Damien Querlioz, Giacomo Indiveri, Melika Payvand, Filippo Moro, Jérôme Casas, Yiğit Demirağ, B. De Salvo and Elisa Donati and has published in prestigious journals such as Nature Communications, Nature Materials and SHILAP Revista de lepidopterología.

In The Last Decade

Thomas Dalgaty

24 papers receiving 462 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Thomas Dalgaty France 13 400 142 131 125 32 26 468
Cory Merkel United States 12 366 0.9× 202 1.4× 123 0.9× 108 0.9× 31 1.0× 47 439
Jaehyun Kim South Korea 8 309 0.8× 100 0.7× 106 0.8× 88 0.7× 43 1.3× 15 367
Justin M. Correll United States 5 623 1.6× 119 0.8× 107 0.8× 237 1.9× 34 1.1× 6 646
S. Bianchi Italy 11 353 0.9× 106 0.7× 122 0.9× 137 1.1× 15 0.5× 21 396
Dabin Wu China 4 578 1.4× 137 1.0× 73 0.6× 163 1.3× 16 0.5× 7 636
Hanchan Song South Korea 12 456 1.1× 84 0.6× 71 0.5× 214 1.7× 15 0.5× 27 480
Guanrui Wang China 9 446 1.1× 153 1.1× 131 1.0× 135 1.1× 22 0.7× 17 554
Hisham Abdalla United States 9 417 1.0× 100 0.7× 121 0.9× 240 1.9× 28 0.9× 20 523
Arnab Neelim Mazumder United States 8 441 1.1× 207 1.5× 127 1.0× 132 1.1× 33 1.0× 19 578
Rajkumar Kubendran United States 9 723 1.8× 146 1.0× 101 0.8× 212 1.7× 73 2.3× 26 808

Countries citing papers authored by Thomas Dalgaty

Since Specialization
Citations

This map shows the geographic impact of Thomas Dalgaty'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 Thomas Dalgaty with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Thomas Dalgaty more than expected).

Fields of papers citing papers by Thomas Dalgaty

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Thomas Dalgaty. 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 Thomas Dalgaty. The network helps show where Thomas Dalgaty may publish in the future.

Co-authorship network of co-authors of Thomas Dalgaty

This figure shows the co-authorship network connecting the top 25 collaborators of Thomas Dalgaty. A scholar is included among the top collaborators of Thomas Dalgaty 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 Thomas Dalgaty. Thomas Dalgaty 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.
Dalgaty, Thomas, Elisa Vianello, & Damien Querlioz. (2025). Memristors for Bayesian in-memory computing. Nature Materials. 1 indexed citations
2.
Hirtzlin, Tifenn, et al.. (2025). Bayesian continual learning and forgetting in neural networks. Nature Communications. 16(1). 9614–9614.
3.
4.
Dalgaty, Thomas, Filippo Moro, Yiğit Demirağ, et al.. (2024). Mosaic: in-memory computing and routing for small-world spike-based neuromorphic systems. Nature Communications. 15(1). 142–142. 29 indexed citations
5.
Dalgaty, Thomas, et al.. (2023). End-to-End Implementation of a Convolutional Neural Network on a 3D-Integrated Image Sensor with Macropixel Array. Sensors. 23(4). 1909–1909. 3 indexed citations
6.
Hirtzlin, Tifenn, Thomas Dalgaty, N. Castellani, et al.. (2023). Bringing uncertainty quantification to the extreme-edge with memristor-based Bayesian neural networks. Nature Communications. 14(1). 7530–7530. 31 indexed citations
7.
Dalgaty, Thomas, et al.. (2023). The CNN vs. SNN Event-camera Dichotomy and Perspectives For Event-Graph Neural Networks. SPIRE - Sciences Po Institutional REpository. 1–6. 2 indexed citations
8.
Dalgaty, Thomas, et al.. (2023). HUGNet: Hemi-Spherical Update Graph Neural Network applied to low-latency event-based optical flow. SPIRE - Sciences Po Institutional REpository. 3953–3962. 5 indexed citations
9.
Dalgaty, Thomas, Tifenn Hirtzlin, Pierre Bessìère, et al.. (2023). Bayesian In-Memory Computing with Resistive Memories. SPIRE - Sciences Po Institutional REpository. 1–4. 2 indexed citations
10.
Moro, Filippo, Thomas Dalgaty, N. Castellani, et al.. (2022). Neuromorphic object localization using resistive memories and ultrasonic transducers. Nature Communications. 13(1). 3506–3506. 35 indexed citations
11.
Payvand, Melika, Filippo Moro, Kumiko Nomura, et al.. (2022). Self-organization of an inhomogeneous memristive hardware for sequence learning. Nature Communications. 13(1). 5793–5793. 25 indexed citations
12.
Dalgaty, Thomas, John P. Miller, Elisa Vianello, & Jérôme Casas. (2021). Bio-Inspired Architectures Substantially Reduce the Memory Requirements of Neural Network Models. Frontiers in Neuroscience. 15. 612359–612359. 3 indexed citations
13.
Dalgaty, Thomas, et al.. (2021). In situ learning using intrinsic memristor variability via Markov chain Monte Carlo sampling. Nature Electronics. 4(2). 151–161. 123 indexed citations
14.
Payvand, Melika, Yiğit Demirağ, Thomas Dalgaty, Elisa Vianello, & Giacomo Indiveri. (2020). Analog Weight Updates with Compliance Current Modulation of Binary ReRAMs for On-Chip Learning. Zurich Open Repository and Archive (University of Zurich). 1–5. 20 indexed citations
15.
Dalgaty, Thomas, et al.. (2020). Entropy source characterization in HfO2 RRAM for TRNG applications. SPIRE - Sciences Po Institutional REpository. 1–2. 5 indexed citations
16.
Giraud, Bastien, Thomas Dalgaty, J. F. Nodin, et al.. (2019). Novel 1T2R1T RRAM-based Ternary Content Addressable Memory for Large Scale Pattern Recognition. HAL (Le Centre pour la Communication Scientifique Directe). 35.5.1–35.5.4. 22 indexed citations
17.
Dalgaty, Thomas, Melika Payvand, B. De Salvo, et al.. (2019). Hybrid CMOS-RRAM Neurons with Intrinsic Plasticity. SPIRE - Sciences Po Institutional REpository. 1–5. 15 indexed citations
18.
Dalgaty, Thomas, Elisa Vianello, B. De Salvo, & Jérôme Casas. (2018). Insect-inspired neuromorphic computing. Current Opinion in Insect Science. 30. 59–66. 18 indexed citations
19.
Grossi, Alessandro, et al.. (2018). Role of synaptic variability in spike-based neuromorphic circuits with unsupervised learning. HAL (Le Centre pour la Communication Scientifique Directe). 1–5. 3 indexed citations
20.
Donati, Elisa, Melika Payvand, Renate Krause, et al.. (2018). Processing EMG signals using reservoir computing on an event-based neuromorphic system. 1–4. 37 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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