Yuwei Cui

706 total citations
9 papers, 372 citations indexed

About

Yuwei Cui is a scholar working on Cognitive Neuroscience, Cellular and Molecular Neuroscience and Artificial Intelligence. According to data from OpenAlex, Yuwei Cui has authored 9 papers receiving a total of 372 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Cognitive Neuroscience, 6 papers in Cellular and Molecular Neuroscience and 2 papers in Artificial Intelligence. Recurrent topics in Yuwei Cui's work include Neural dynamics and brain function (9 papers), Visual perception and processing mechanisms (6 papers) and Neuroscience and Neural Engineering (4 papers). Yuwei Cui is often cited by papers focused on Neural dynamics and brain function (9 papers), Visual perception and processing mechanisms (6 papers) and Neuroscience and Neural Engineering (4 papers). Yuwei Cui collaborates with scholars based in United States, Canada and China. Yuwei Cui's co-authors include Daniel A. Butts, Jeff Hawkins, Subutai Ahmad, James M. McFarland, Liu D. Liu, Christopher C. Pack, Jonathan B. Demb, Silvia J. H. Park, Longwen Huang and A. Casti and has published in prestigious journals such as Journal of Neuroscience, Journal of Neurophysiology and eLife.

In The Last Decade

Yuwei Cui

9 papers receiving 361 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Yuwei Cui United States	8	261	112	89	71	51	9	372
Sebastian Gerwinn Germany	12	262 1.0×	101 0.9×	83 0.9×	88 1.2×	27 0.5×	25	479
Benjamin Lansdell United States	7	150 0.6×	68 0.6×	84 0.9×	50 0.7×	23 0.5×	11	293
Jasmine Collins United States	4	275 1.1×	97 0.9×	101 1.1×	53 0.7×	29 0.6×	6	419
Joseph Sirosh United States	8	306 1.2×	97 0.9×	188 2.1×	68 1.0×	34 0.7×	17	504
David H. Goldberg United States	13	194 0.7×	135 1.2×	53 0.6×	198 2.8×	32 0.6×	20	412
Samuel A. Ellias United States	6	171 0.7×	172 1.5×	65 0.7×	37 0.5×	73 1.4×	8	403
Guillaume Lajoie Canada	12	239 0.9×	75 0.7×	136 1.5×	91 1.3×	18 0.4×	28	360
Jim‐Shih Liaw United States	9	238 0.9×	219 2.0×	93 1.0×	124 1.7×	52 1.0×	23	396
Christoph Rasche Romania	12	219 0.8×	79 0.7×	49 0.6×	125 1.8×	20 0.4×	38	388
Benjamin D. Haeffele United States	7	149 0.6×	105 0.9×	61 0.7×	21 0.3×	16 0.3×	22	327

Countries citing papers authored by Yuwei Cui

Since Specialization

Citations

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

Fields of papers citing papers by Yuwei Cui

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yuwei Cui

This figure shows the co-authorship network connecting the top 25 collaborators of Yuwei Cui. A scholar is included among the top collaborators of Yuwei Cui 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 Yuwei Cui. Yuwei Cui 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:

9 of 9 papers shown

1.

Cui, Yuwei, Subutai Ahmad, & Jeff Hawkins. (2017). The HTM Spatial Pooler—A Neocortical Algorithm for Online Sparse Distributed Coding. Frontiers in Computational Neuroscience. 11. 111–111. 69 indexed citations

2.

Hawkins, Jeff, Subutai Ahmad, & Yuwei Cui. (2017). A Theory of How Columns in the Neocortex Enable Learning the Structure of the World. Frontiers in Neural Circuits. 11. 81–81. 82 indexed citations

3.

Cui, Yuwei, Liu D. Liu, James M. McFarland, Christopher C. Pack, & Daniel A. Butts. (2016). Inferring Cortical Variability from Local Field Potentials. Journal of Neuroscience. 36(14). 4121–4135. 36 indexed citations

4.

Cui, Yuwei, et al.. (2016). Divisive suppression explains high-precision firing and contrast adaptation in retinal ganglion cells. eLife. 5. 24 indexed citations

5.

Butts, Daniel A., Yuwei Cui, & A. Casti. (2016). Nonlinear computations shaping temporal processing of precortical vision. Journal of Neurophysiology. 116(3). 1344–1357. 7 indexed citations

6.

Cui, Yuwei, et al.. (2015). An accurate circuit-based description of retinal ganglion cell computation. BMC Neuroscience. 16(S1). 8 indexed citations

7.

McFarland, James M., Yuwei Cui, & Daniel A. Butts. (2013). Inferring Nonlinear Neuronal Computation Based on Physiologically Plausible Inputs. PLoS Computational Biology. 9(7). e1003143–e1003143. 104 indexed citations

8.

Cui, Yuwei, et al.. (2013). Diverse Suppressive Influences in Area MT and Selectivity to Complex Motion Features. Journal of Neuroscience. 33(42). 16715–16728. 34 indexed citations

9.

Huang, Longwen, et al.. (2011). Impact of noise structure and network topology on tracking speed of neural networks. Neural Networks. 24(10). 1110–1119. 8 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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