Stephen Nease

15 papers receiving 379 citations

Peers

Stephen Nease
Comparison fields: 5 of 28
  • Cognitive Neuroscience 113
  • Hardware and Architecture 40
  • Electrical and Electronic Engineering 340
  • Cellular and Molecular Neuroscience 95
  • Artificial Intelligence 116
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Stephen Brink United States
Richard Wunderlich United States
Suma George United States
Brian Degnan United States
Shubha Ramakrishnan United States
A. Pavasović Serbia
Stefan Scholze Germany
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Stephen Nease relative to Stephen Brink United States Stephen Brink's profile →
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Citations per year

Countries citing papers authored by Stephen Nease

Since Specialization
Citations

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

Fields of papers citing papers by Stephen Nease

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 17 scholars most cited alongside Stephen Nease, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Stephen Nease Line = papers co-authored together Stephen Nease links everyone, so they are left out of the graph.

All Works

15 of 15 papers shown
#Work
1 2016112
2 201285
3 201241
4 201329
5 201327
6 201627
7 201126
8 201611
9 20159
10 20166
11 20183
12 20122
13 20132
14 20142
15 20151

About Stephen Nease

Stephen Nease is a scholar working on Electrical and Electronic Engineering, Artificial Intelligence, Cellular and Molecular Neuroscience, Cognitive Neuroscience and Biomedical Engineering, having authored 15 papers that have together received 383 indexed citations. Recurring topics across this work include Advanced Memory and Neural Computing (10 papers), Neuroscience and Neural Engineering (5 papers), Neural dynamics and brain function (4 papers), CCD and CMOS Imaging Sensors (4 papers), Analog and Mixed-Signal Circuit Design (4 papers), Ferroelectric and Negative Capacitance Devices (3 papers), Neural Networks and Applications (2 papers) and Evolutionary Algorithms and Applications (2 papers). The work is most often cited by research in Cognitive Neuroscience (113 citations), Hardware and Architecture (40 citations), Electrical and Electronic Engineering (340 citations), Cellular and Molecular Neuroscience (95 citations) and Artificial Intelligence (116 citations). Stephen Nease has collaborated with scholars based in United States and Germany. Frequent co-authors include P. Hasler, Stephen Brink, Richard Wunderlich, Suma George, Jennifer Hasler, Shubha Ramakrishnan, Elisabetta Chicca, Michelle Collins, Sahil Shah and Scott Koziol. Their work appears in journals such as Journal of the Audio Engineering Society, IEEE Transactions on Biomedical Circuits and Systems, IEEE Journal of Solid-State Circuits, IEEE Transactions on Very Large Scale Integration (VLSI) Systems and IEEE Transactions on Circuits and Systems I Regular Papers.

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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