David A. Borton

3.8k total citations · 1 hit paper
66 papers, 1.9k citations indexed

About

David A. Borton is a scholar working on Cognitive Neuroscience, Cellular and Molecular Neuroscience and Neurology. According to data from OpenAlex, David A. Borton has authored 66 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 52 papers in Cognitive Neuroscience, 45 papers in Cellular and Molecular Neuroscience and 18 papers in Neurology. Recurrent topics in David A. Borton's work include Neuroscience and Neural Engineering (43 papers), EEG and Brain-Computer Interfaces (39 papers) and Neurological disorders and treatments (18 papers). David A. Borton is often cited by papers focused on Neuroscience and Neural Engineering (43 papers), EEG and Brain-Computer Interfaces (39 papers) and Neurological disorders and treatments (18 papers). David A. Borton collaborates with scholars based in United States, Switzerland and United Kingdom. David A. Borton's co-authors include Juan Aceros, Ming Yin, A. V. Nurmikko, Grégoire Courtine, José del R. Millán, Silvestro Micera, Yoon‐Kyu Song, John P. Donoghue, Christopher W. Bull and Fabien B. Wagner and has published in prestigious journals such as Neuron, Journal of Neuroscience and Nature Biotechnology.

In The Last Decade

David A. Borton

62 papers receiving 1.8k citations

Hit Papers

Long-term wireless streaming of neural recordings for cir... 2021 2026 2022 2024 2021 50 100 150

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
David A. Borton United States 22 1.2k 977 547 515 368 66 1.9k
Justin C. Sanchez United States 29 1.8k 1.5× 1.8k 1.9× 631 1.2× 574 1.1× 300 0.8× 121 2.9k
P.A. House United States 20 1.0k 0.8× 895 0.9× 327 0.6× 143 0.3× 274 0.7× 68 1.6k
Sam Musallam United States 17 1.2k 1.0× 1.5k 1.6× 447 0.8× 437 0.8× 118 0.3× 32 1.9k
John E. Downey United States 15 2.0k 1.7× 2.4k 2.4× 546 1.0× 969 1.9× 146 0.4× 20 2.9k
Francis R. Willett United States 17 1.2k 1.0× 1.8k 1.9× 508 0.9× 524 1.0× 120 0.3× 31 2.2k
Brian Wodlinger United States 13 1.6k 1.3× 1.8k 1.9× 384 0.7× 790 1.5× 106 0.3× 31 2.4k
Robert A. Gaunt United States 25 1.6k 1.3× 1.5k 1.6× 349 0.6× 881 1.7× 115 0.3× 71 2.3k
Marc W. Slutzky United States 22 1.0k 0.9× 1.7k 1.7× 336 0.6× 493 1.0× 123 0.3× 54 2.1k
Philip R. Kennedy United States 17 1.2k 1.0× 1.5k 1.5× 374 0.7× 306 0.6× 130 0.4× 32 2.0k

Countries citing papers authored by David A. Borton

Since Specialization
Citations

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

Fields of papers citing papers by David A. Borton

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David A. Borton. 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 David A. Borton. The network helps show where David A. Borton may publish in the future.

Co-authorship network of co-authors of David A. Borton

This figure shows the co-authorship network connecting the top 25 collaborators of David A. Borton. A scholar is included among the top collaborators of David A. Borton 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 David A. Borton. David A. Borton 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.
Calvert, Jonathan S., Jaeson Jang, Girish Chitnis, et al.. (2025). An active electronic, high-density epidural paddle array for chronic spinal cord neuromodulation. Journal of Neural Engineering. 22(2). 26023–26023.
2.
Borton, David A., et al.. (2024). Distinct neocortical mechanisms underlie human SI responses to median nerve and laser-evoked peripheral activation. Imaging Neuroscience. 2. 1 indexed citations
3.
Allawala, Anusha, Kelly R. Bijanki, Joshua A. Adkinson, et al.. (2024). Stereo-Electroencephalography–Guided Network Neuromodulation for Psychiatric Disorders: The Neurophysiology Monitoring Unit. Operative Neurosurgery. 27(3). 329–336.
4.
Allawala, Anusha, Kelly R. Bijanki, Raissa Mathura, et al.. (2024). Prefrontal network engagement by deep brain stimulation in limbic hubs. Frontiers in Human Neuroscience. 17. 1291315–1291315. 2 indexed citations
6.
Shaaya, Elias, et al.. (2022). A Review of Functional Restoration From Spinal Cord Stimulation in Patients With Spinal Cord Injury. Neurospine. 19(3). 703–734. 28 indexed citations
7.
Calvert, Jonathan S., et al.. (2022). Fast inference of spinal neuromodulation for motor control using amortized neural networks. Journal of Neural Engineering. 19(5). 56037–56037. 6 indexed citations
8.
Sheth, Sameer A., Kelly R. Bijanki, Brian Metzger, et al.. (2021). Deep Brain Stimulation for Depression Informed by Intracranial Recordings. Biological Psychiatry. 92(3). 246–251. 77 indexed citations
9.
Allawala, Anusha, Kelly R. Bijanki, Wayne K. Goodman, et al.. (2021). A Novel Framework for Network-Targeted Neuropsychiatric Deep Brain Stimulation. Neurosurgery. 89(2). E116–E121. 28 indexed citations
10.
Edhi, Muhammad Muzzammil, Suguru Koyama, Satoru Yoshikawa, et al.. (2020). Pain phenotypes classified by machine learning using electroencephalography features. NeuroImage. 223. 117256–117256. 35 indexed citations
11.
Powell, Marc, Juan Ansó, Ro’ee Gilron, et al.. (2020). NeuroDAC: an open-source arbitrary biosignal waveform generator. Journal of Neural Engineering. 18(1). 16010–16010. 2 indexed citations
12.
Allawala, Anusha, et al.. (2020). Automated and rapid self-report of nociception in transgenic mice. Scientific Reports. 10(1). 13215–13215. 1 indexed citations
13.
Provenza, Nicole R., Angelique C. Paulk, Noam Peled, et al.. (2019). Decoding task engagement from distributed network electrophysiology in humans. Journal of Neural Engineering. 16(5). 56015–56015. 26 indexed citations
14.
Borton, David A., Marco Bonizzato, Jack DiGiovanna, et al.. (2013). Corticospinal neuroprostheses to restore locomotion after spinal cord injury. Neuroscience Research. 78. 21–29. 37 indexed citations
15.
Borton, David A., Ming Yin, Juan Aceros, & A. V. Nurmikko. (2013). An implantable wireless neural interface for recording cortical circuit dynamics in moving primates. Journal of Neural Engineering. 10(2). 26010–26010. 239 indexed citations
16.
Wang, Jing, M. Diagne, Fabien B. Wagner, et al.. (2011). Approaches to optical neuromodulation from rodents to non-human primates by integrated optoelectronic devices. PubMed. 26. 7525–7528. 7 indexed citations
17.
Wang, Jing, Fabien B. Wagner, David A. Borton, et al.. (2011). Integrated device for combined optical neuromodulation and electrical recording for chronicin vivoapplications. Journal of Neural Engineering. 9(1). 16001–16001. 132 indexed citations
18.
Borton, David A., Ming Yin, Juan Aceros, et al.. (2011). Developing implantable neuroprosthetics: A new model in pig. PubMed. 57. 3024–3030. 12 indexed citations
19.
Song, Yoon‐Kyu, David A. Borton, William R. Patterson, et al.. (2009). Active Microelectronic Neurosensor Arrays for Implantable Brain Communication Interfaces. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 17(4). 339–345. 67 indexed citations
20.
Song, Yoon‐Kyu, William R. Patterson, Christopher W. Bull, et al.. (2007). A Brain Implantable Microsystem with Hybrid RF/IR Telemetry for Advanced Neuroengineering Applications. Conference proceedings. 2007. 445–448. 31 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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