Ben D. B. Willmore

2.5k total citations
34 papers, 1.5k citations indexed

About

Ben D. B. Willmore is a scholar working on Cognitive Neuroscience, Cellular and Molecular Neuroscience and Artificial Intelligence. According to data from OpenAlex, Ben D. B. Willmore has authored 34 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 31 papers in Cognitive Neuroscience, 7 papers in Cellular and Molecular Neuroscience and 5 papers in Artificial Intelligence. Recurrent topics in Ben D. B. Willmore's work include Neural dynamics and brain function (29 papers), Visual perception and processing mechanisms (18 papers) and Neuroscience and Music Perception (11 papers). Ben D. B. Willmore is often cited by papers focused on Neural dynamics and brain function (29 papers), Visual perception and processing mechanisms (18 papers) and Neuroscience and Music Perception (11 papers). Ben D. B. Willmore collaborates with scholars based in United Kingdom, United States and Hong Kong. Ben D. B. Willmore's co-authors include D.J. Tolhurst, Andrew J. King, Jan W. H. Schnupp, Neil C. Rabinowitz, Jack L. Gallant, Nicol S. Harper, Darragh Smyth, Ryan Prenger, Oliver Schoppe and Gary E. Baker and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Communications and Neuron.

In The Last Decade

Ben D. B. Willmore

33 papers receiving 1.5k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ben D. B. Willmore United Kingdom 21 1.2k 390 215 150 126 34 1.5k
W. Vinje United States 6 1.3k 1.1× 557 1.4× 99 0.5× 71 0.5× 116 0.9× 9 1.6k
Tatyana O. Sharpee United States 26 1.8k 1.5× 703 1.8× 133 0.6× 155 1.0× 213 1.7× 70 2.4k
Odelia Schwartz United States 18 1.8k 1.5× 571 1.5× 64 0.3× 161 1.1× 171 1.4× 40 2.1k
Alexander M. Chan United States 12 558 0.5× 518 1.3× 86 0.4× 70 0.5× 69 0.5× 15 1.2k
Pietro Berkes United States 12 1.2k 1.0× 286 0.7× 42 0.2× 143 1.0× 295 2.3× 19 1.6k
Michael R. DeWeese United States 25 1.8k 1.5× 916 2.3× 128 0.6× 114 0.8× 324 2.6× 53 2.6k
Alan A. Stocker United States 20 1.6k 1.3× 201 0.5× 72 0.3× 66 0.4× 124 1.0× 60 2.0k
Kechen Zhang United States 19 1.8k 1.5× 1.0k 2.6× 104 0.5× 42 0.3× 207 1.6× 45 2.3k
Jean‐Michel Hupé France 17 1.9k 1.5× 452 1.2× 171 0.8× 29 0.2× 68 0.5× 33 2.1k
Monty A. Escabı́ United States 23 1.6k 1.3× 501 1.3× 287 1.3× 220 1.5× 127 1.0× 46 1.8k

Countries citing papers authored by Ben D. B. Willmore

Since Specialization
Citations

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

Fields of papers citing papers by Ben D. B. Willmore

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ben D. B. Willmore. 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 Ben D. B. Willmore. The network helps show where Ben D. B. Willmore may publish in the future.

Co-authorship network of co-authors of Ben D. B. Willmore

This figure shows the co-authorship network connecting the top 25 collaborators of Ben D. B. Willmore. A scholar is included among the top collaborators of Ben D. B. Willmore 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 Ben D. B. Willmore. Ben D. B. Willmore 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.
Lohse, Michael, Andrew J. King, & Ben D. B. Willmore. (2024). Subcortical origin of nonlinear sound encoding in auditory cortex. Current Biology. 34(15). 3405–3415.e5. 1 indexed citations
2.
Willmore, Ben D. B., et al.. (2023). Hierarchical temporal prediction captures motion processing along the visual pathway. eLife. 12. 6 indexed citations
3.
Willmore, Ben D. B. & Andrew J. King. (2022). Adaptation in auditory processing. Physiological Reviews. 103(2). 1025–1058. 26 indexed citations
4.
Willmore, Ben D. B., et al.. (2020). Simple transformations capture auditory input to cortex. Proceedings of the National Academy of Sciences. 117(45). 28442–28451. 24 indexed citations
5.
Lohse, Michael, Victoria M. Bajo, Andrew J. King, & Ben D. B. Willmore. (2020). Neural circuits underlying auditory contrast gain control and their perceptual implications. Nature Communications. 11(1). 324–324. 40 indexed citations
6.
Willmore, Ben D. B., et al.. (2019). A dynamic network model of temporal receptive fields in primary auditory cortex. PLoS Computational Biology. 15(5). e1006618–e1006618. 12 indexed citations
7.
King, Andrew J., et al.. (2018). Contrast gain control in mouse auditory cortex. Journal of Neurophysiology. 120(4). 1872–1884. 21 indexed citations
8.
Willmore, Ben D. B., Oliver Schoppe, Andrew J. King, Jan W. H. Schnupp, & Nicol S. Harper. (2016). Incorporating Midbrain Adaptation to Mean Sound Level Improves Models of Auditory Cortical Processing. Journal of Neuroscience. 36(2). 280–289. 30 indexed citations
9.
Schoppe, Oliver, Nicol S. Harper, Ben D. B. Willmore, Andrew J. King, & Jan W. H. Schnupp. (2016). Measuring the Performance of Neural Models. Frontiers in Computational Neuroscience. 10. 10–10. 55 indexed citations
10.
Harper, Nicol S., Oliver Schoppe, Ben D. B. Willmore, et al.. (2016). Network Receptive Field Modeling Reveals Extensive Integration and Multi-feature Selectivity in Auditory Cortical Neurons. PLoS Computational Biology. 12(11). e1005113–e1005113. 38 indexed citations
11.
Willmore, Ben D. B., et al.. (2014). Hearing in noisy environments: noise invariance and contrast gain control. The Journal of Physiology. 592(16). 3371–3381. 35 indexed citations
12.
Rabinowitz, Neil C., Ben D. B. Willmore, Andrew J. King, & Jan W. H. Schnupp. (2013). Constructing Noise-Invariant Representations of Sound in the Auditory Pathway. PLoS Biology. 11(11). e1001710–e1001710. 103 indexed citations
13.
Willmore, Ben D. B., Harry Bulstrode, & D.J. Tolhurst. (2012). Contrast normalization contributes to a biologically-plausible model of receptive-field development in primary visual cortex (V1). Vision Research. 54. 49–60. 6 indexed citations
14.
Rabinowitz, Neil C., Ben D. B. Willmore, Jan W. H. Schnupp, & Andrew J. King. (2012). Spectrotemporal Contrast Kernels for Neurons in Primary Auditory Cortex. Journal of Neuroscience. 32(33). 11271–11284. 55 indexed citations
15.
Rabinowitz, Neil C., Ben D. B. Willmore, Jan W. H. Schnupp, & Andrew J. King. (2011). Contrast Gain Control in Auditory Cortex. Neuron. 70(6). 1178–1191. 183 indexed citations
16.
Willmore, Ben D. B., Ryan Prenger, & Jack L. Gallant. (2010). Neural Representation of Natural Images in Visual Area V2. Journal of Neuroscience. 30(6). 2102–2114. 83 indexed citations
17.
Willmore, Ben D. B. & Andrew J. King. (2009). Auditory Cortex: Representation through Sparsification?. Current Biology. 19(24). R1123–R1125. 7 indexed citations
18.
Caywood, Matthew S., Ben D. B. Willmore, & D.J. Tolhurst. (2004). Independent Components of Color Natural Scenes Resemble V1 Neurons in Their Spatial and Color Tuning. Journal of Neurophysiology. 91(6). 2859–2873. 45 indexed citations
19.
Willmore, Ben D. B. & D.J. Tolhurst. (2001). Characterizing the sparseness of neural codes. Network Computation in Neural Systems. 12(3). 255–270. 176 indexed citations
20.
Willmore, Ben D. B. & D.J. Tolhurst. (2000). Sparseness and kurtosis of computational models of simple-cell coding in primary visual cortex. The Journal of Physiology. 526. 1 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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