Davis Barch
Impact in
- Cognitive Neuroscience top 10%
- Neural dynamics and brain function
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- Advanced Memory and Neural Computing
- Ferroelectric and Negative Capacitance Devices
- CCD and CMOS Imaging Sensors
Papers in
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- Neural dynamics and brain function 4
- Visual perception and processing mechanisms 1
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- Advanced Memory and Neural Computing 4
- Ferroelectric and Negative Capacitance Devices 2
- Co-authors
- John V. Arthur (3 shared papers)Dharmendra S. Modha (4 shared papers)Alexander Andreopoulos (4 shared papers)Andrew S. Cassidy (4 shared papers)Myron Flickner (4 shared papers)David Van Den Berg (2 shared papers)Brian Taba (2 shared papers)Steven K. Esser (2 shared papers)
- Journals
- Neurocomputing (3 papers)IEEE Transactions on Computers (1 paper)Proceedings of the National Academy of Sciences (1 paper)IBM Journal of Research and Development (1 paper)
- Partner nations
- United States
In The Last Decade
Davis Barch
7 papers receiving 538 citations
Davis Barch's Hit Papers
Peers
Comparison fields: 5 of 50
- Cognitive Neuroscience 200
- Electrical and Electronic Engineering 447
- Artificial Intelligence 221
- Cellular and Molecular Neuroscience 113
- Computer Vision and Pattern Recognition 81
Countries citing papers authored by Davis Barch
This map shows the geographic impact of Davis Barch'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 Davis Barch with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Davis Barch more than expected).
Fields of papers citing papers by Davis Barch
This network shows the impact of papers produced by Davis Barch. 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 Davis Barch. The network helps show where Davis Barch may publish in the future.
Co-authors
The 24 scholars most cited alongside Davis Barch, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Convolutional networks for fast, energy-efficient neuromorphic computing Hit paper breakdown → | 2016 | 490 |
| 2 | 2016 | 24 | |
| 3 | 2015 | 12 | |
| 4 | 1999 | 11 | |
| 5 | 2016 | 8 | |
| 6 | 2002 | 2 | |
| 7 | 2000 | 1 |
About Davis Barch
Davis Barch is a scholar working on Cognitive Neuroscience, Electrical and Electronic Engineering, Cellular and Molecular Neuroscience, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 7 papers that have together received 548 indexed citations. Recurring topics across this work include Neural dynamics and brain function (4 papers), Advanced Memory and Neural Computing (4 papers), Ferroelectric and Negative Capacitance Devices (2 papers), Neural Networks and Reservoir Computing (2 papers), Photoreceptor and optogenetics research (2 papers), Visual perception and processing mechanisms (1 paper), Blind Source Separation Techniques (1 paper) and Neuroscience and Neural Engineering (1 paper). The work is most often cited by research in Cognitive Neuroscience (200 citations), Electrical and Electronic Engineering (447 citations), Artificial Intelligence (221 citations), Cellular and Molecular Neuroscience (113 citations) and Computer Vision and Pattern Recognition (81 citations). Davis Barch has collaborated with scholars based in United States. Frequent co-authors include John V. Arthur, Dharmendra S. Modha, Alexander Andreopoulos, Andrew S. Cassidy, Myron Flickner, David Van Den Berg, Brian Taba, Steven K. Esser, Jeffrey L. McKinstry and Carmelo di Nolfo. Their work appears in journals such as Neurocomputing, IEEE Transactions on Computers, Proceedings of the National Academy of Sciences and IBM Journal of Research and Development.
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.