John A. Perrone

1.7k total citations
54 papers, 1.4k citations indexed

About

John A. Perrone is a scholar working on Cognitive Neuroscience, Computer Vision and Pattern Recognition and Molecular Biology. According to data from OpenAlex, John A. Perrone has authored 54 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 36 papers in Cognitive Neuroscience, 12 papers in Computer Vision and Pattern Recognition and 9 papers in Molecular Biology. Recurrent topics in John A. Perrone's work include Visual perception and processing mechanisms (35 papers), Neural dynamics and brain function (17 papers) and Advanced Vision and Imaging (10 papers). John A. Perrone is often cited by papers focused on Visual perception and processing mechanisms (35 papers), Neural dynamics and brain function (17 papers) and Advanced Vision and Imaging (10 papers). John A. Perrone collaborates with scholars based in New Zealand, United States and United Kingdom. John A. Perrone's co-authors include Leland S. Stone, Alexander Thiele, Richard J. Krauzlis, Samuel G. Charlton, Robert B. Isler, Nicola J. Starkey, Helen Clark, David G. Smith, Cynthia H. Null and Jill Stoltzfus and has published in prestigious journals such as Journal of Neuroscience, Nature Neuroscience and Journal of Environmental Management.

In The Last Decade

John A. Perrone

48 papers receiving 1.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
John A. Perrone New Zealand 20 1.1k 305 269 184 176 54 1.4k
Scott Watamaniuk United States 21 1.6k 1.5× 356 1.2× 234 0.9× 227 1.2× 131 0.7× 53 1.8k
Constance S. Royden United States 17 1.1k 1.0× 361 1.2× 72 0.3× 143 0.8× 107 0.6× 27 1.2k
Gang Luo United States 21 456 0.4× 274 0.9× 130 0.5× 113 0.6× 172 1.0× 134 1.4k
Duane R. Geruschat United States 18 428 0.4× 89 0.3× 152 0.6× 149 0.8× 136 0.8× 38 1.1k
Zijiang J. He United States 25 1.9k 1.7× 417 1.4× 109 0.4× 416 2.3× 115 0.7× 70 2.3k
K. I. Beverley Canada 26 2.3k 2.0× 428 1.4× 342 1.3× 235 1.3× 222 1.3× 37 2.5k
Teng Leng Ooi United States 21 1.4k 1.2× 275 0.9× 63 0.2× 355 1.9× 70 0.4× 65 1.7k
John A. Greenwood United Kingdom 23 1.2k 1.1× 195 0.6× 110 0.4× 72 0.4× 142 0.8× 68 1.8k
Eugene R. Wist Germany 20 1.1k 1.0× 102 0.3× 120 0.4× 179 1.0× 68 0.4× 49 1.4k
Andrew Duchon United States 10 1.1k 1.0× 230 0.8× 107 0.4× 264 1.4× 27 0.2× 17 1.7k

Countries citing papers authored by John A. Perrone

Since Specialization
Citations

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

Fields of papers citing papers by John A. Perrone

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of John A. Perrone

This figure shows the co-authorship network connecting the top 25 collaborators of John A. Perrone. A scholar is included among the top collaborators of John A. Perrone 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 A. Perrone. John A. Perrone 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.
Perrone, John A., et al.. (2023). Outcomes of Staple Line Reinforcement Following Robotic Assisted Sleeve Gastrectomy Based on MBSAQIP Database. Obesity Surgery. 33(9). 2662–2670. 4 indexed citations
2.
Clark, Helen, John A. Perrone, Robert B. Isler, & Samuel G. Charlton. (2016). Fixating on the size-speed illusion of approaching railway trains: What we can learn from our eye movements. Accident Analysis & Prevention. 99(Pt A). 110–113. 2 indexed citations
3.
Perrone, John A. & Dorion Liston. (2015). Redundancy reduction explains the expansion of visual direction space around the cardinal axes. Vision Research. 111(Pt A). 31–42. 1 indexed citations
4.
Perrone, John A., et al.. (2014). Proceedings of the 29th International Conference on Image and Vision Computing New Zealand. 1 indexed citations
5.
Perrone, John A. & Richard J. Krauzlis. (2014). Simulating component-to-pattern dynamic effects with a computer model of middle temporal pattern neurons. Journal of Vision. 14(1). 19–19. 2 indexed citations
6.
Clark, Helen, John A. Perrone, & Robert B. Isler. (2013). An illusory size–speed bias and railway crossing collisions. Accident Analysis & Prevention. 55. 226–231. 28 indexed citations
7.
Charlton, Samuel G., et al.. (2008). The role of looming and attention capture in drivers’ braking responses. Accident Analysis & Prevention. 40(4). 1375–1382. 38 indexed citations
8.
Perrone, John A. & Richard J. Krauzlis. (2008). Spatial integration by MT pattern neurons: A closer look at pattern-to-component effects and the role of speed tuning. Journal of Vision. 8(9). 1–1. 47 indexed citations
9.
Perrone, John A. & Richard J. Krauzlis. (2008). Vector subtraction using visual and extraretinal motion signals: A new look at efference copy and corollary discharge theories. Journal of Vision. 8(14). 24–24. 31 indexed citations
10.
Perrone, John A.. (2006). A Single Mechanism Can Explain the Speed Tuning Properties of MT and V1 Complex Neurons. Journal of Neuroscience. 26(46). 11987–11991. 19 indexed citations
11.
Perrone, John A.. (2005). Economy of scale: A motion sensor with variable speed tuning. Journal of Vision. 5(1). 3–3. 31 indexed citations
12.
Perrone, John A. & Alexander Thiele. (2002). A model of speed tuning in MT neurons. Vision Research. 42(8). 1035–1051. 67 indexed citations
13.
Perrone, John A. & Alexander Thiele. (2001). Speed skills: measuring the visual speed analyzing properties of primate MT neurons. Nature Neuroscience. 4(5). 526–532. 193 indexed citations
14.
Stone, Leland S. & John A. Perrone. (1997). Quantitative Simulations of MST Visual Receptive Field Properties Using a Template Model of Heading Estimation. The Society for Neuroscience Abstracts. 23. 1126. 4 indexed citations
15.
Stone, Leland S. & John A. Perrone. (1997). Human Heading Estimation During Visually Simulated Curvilinear Motion. Vision Research. 37(5). 573–590. 82 indexed citations
16.
Stone, Leland S., et al.. (1994). A Role for MST Neurons in Heading Estimation. Social Neuroscience. 4 indexed citations
17.
Perrone, John A. & Leland S. Stone. (1994). A model of self-motion estimation within primate extrastriate visual cortex. Vision Research. 34(21). 2917–2938. 203 indexed citations
18.
Perrone, John A.. (1992). Model for the computation of self-motion in biological systems. Journal of the Optical Society of America A. 9(2). 177–177. 135 indexed citations
19.
Perrone, John A.. (1987). Extracting 3-D egomotion information from a 2-D flow field. A biological solution?. Annual Meeting Optical Society of America. MY2–MY2. 3 indexed citations
20.
Perrone, John A.. (1967). Dysphagia, Due to Massive Cervical Exostoses. Archives of Otolaryngology - Head and Neck Surgery. 86(3). 346–347. 17 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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