Eizaburo Doi

411 total citations
10 papers, 245 citations indexed

About

Eizaburo Doi is a scholar working on Cognitive Neuroscience, Molecular Biology and Signal Processing. According to data from OpenAlex, Eizaburo Doi has authored 10 papers receiving a total of 245 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Cognitive Neuroscience, 5 papers in Molecular Biology and 2 papers in Signal Processing. Recurrent topics in Eizaburo Doi's work include Neural dynamics and brain function (7 papers), Retinal Development and Disorders (5 papers) and Visual perception and processing mechanisms (4 papers). Eizaburo Doi is often cited by papers focused on Neural dynamics and brain function (7 papers), Retinal Development and Disorders (5 papers) and Visual perception and processing mechanisms (4 papers). Eizaburo Doi collaborates with scholars based in United States, Germany and Japan. Eizaburo Doi's co-authors include Michael S. Lewicki, Terrence J. Sejnowski, Thomas Wächtler, Toshio Inui, Te-Won Lee, Keith Mathieson, A. M. Litke, Martin Greschner, E. J. Chichilnisky and Timothy A. Machado and has published in prestigious journals such as Journal of Neuroscience, IEEE Transactions on Image Processing and PLoS Computational Biology.

In The Last Decade

Eizaburo Doi

10 papers receiving 237 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Eizaburo Doi United States	9	180	78	47	36	31	10	245
Niall McLoughlin United Kingdom	14	361 2.0×	61 0.8×	95 2.0×	69 1.9×	14 0.5×	21	528
Simon Barthelmé France	12	230 1.3×	29 0.4×	37 0.8×	75 2.1×	51 1.6×	25	447
Corey M. Ziemba United States	10	452 2.5×	30 0.4×	71 1.5×	93 2.6×	19 0.6×	17	506
J. Twitty United States	3	260 1.4×	80 1.0×	36 0.8×	16 0.4×	14 0.5×	3	419
Julie Martin United States	3	210 1.2×	24 0.3×	34 0.7×	40 1.1×	11 0.4×	6	241
Brian Potetz United States	9	123 0.7×	19 0.2×	45 1.0×	117 3.3×	33 1.1×	13	269
Jason Prentice United States	7	286 1.6×	61 0.8×	145 3.1×	20 0.6×	48 1.5×	12	334
Alexander G. Dimitrov United States	11	220 1.2×	35 0.4×	98 2.1×	21 0.6×	111 3.6×	39	357
Sergei P. Rebrik United States	5	334 1.9×	37 0.5×	189 4.0×	13 0.4×	32 1.0×	7	376
Samuel A. Ellias United States	6	171 0.9×	73 0.9×	172 3.7×	19 0.5×	65 2.1×	8	403

Countries citing papers authored by Eizaburo Doi

Since Specialization

Citations

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

Fields of papers citing papers by Eizaburo Doi

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Eizaburo Doi

This figure shows the co-authorship network connecting the top 25 collaborators of Eizaburo Doi. A scholar is included among the top collaborators of Eizaburo Doi 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 Eizaburo Doi. Eizaburo Doi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

10 of 10 papers shown

Doi, Eizaburo & Michael S. Lewicki. (2014). A Simple Model of Optimal Population Coding for Sensory Systems. PLoS Computational Biology. 10(8). e1003761–e1003761. 28 indexed citations

Doi, Eizaburo & Michael S. Lewicki. (2014). Optimal retinal population coding predicts inhomogeneous light adaptation and contrast sensitivity across the visual field. Journal of Vision. 14(10). 1188–1188. 2 indexed citations

Doi, Eizaburo, Jeffrey L. Gauthier, Greg D. Field, et al.. (2012). Efficient Coding of Spatial Information in the Primate Retina. Journal of Neuroscience. 32(46). 16256–16264. 72 indexed citations

Doi, Eizaburo & Michael S. Lewicki. (2011). Characterization of Minimum Error Linear Coding with Sensory and Neural Noise. Neural Computation. 23(10). 2498–2510. 11 indexed citations

Wächtler, Thomas, et al.. (2007). Cone selectivity derived from the responses of the retinal cone mosaic to natural scenes. Journal of Vision. 7(8). 6–6. 30 indexed citations

Doi, Eizaburo, et al.. (2007). Robust Coding Over Noisy Overcomplete Channels. IEEE Transactions on Image Processing. 16(2). 442–452. 16 indexed citations

Doi, Eizaburo, et al.. (2005). A Theoretical Analysis of Robust Coding over Noisy Overcomplete Channels. Neural Information Processing Systems. 18. 307–314. 12 indexed citations

Doi, Eizaburo & Michael S. Lewicki. (2005). Relations between the statistical regularities of natural images and the response properties of the early visual system. 8 indexed citations

Doi, Eizaburo & Michael S. Lewicki. (2004). Sparse Coding of Natural Images Using an Overcomplete Set of Limited Capacity Units. Neural Information Processing Systems. 17. 377–384. 13 indexed citations

10.

Doi, Eizaburo, Toshio Inui, Te-Won Lee, Thomas Wächtler, & Terrence J. Sejnowski. (2003). Spatiochromatic Receptive Field Properties Derived from Information-Theoretic Analyses of Cone Mosaic Responses to Natural Scenes. Neural Computation. 15(2). 397–417. 53 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.

Explore authors with similar magnitude of impact