Michael Spratling

4.1k total citations
72 papers, 2.5k citations indexed

About

Michael Spratling is a scholar working on Cognitive Neuroscience, Computer Vision and Pattern Recognition and Artificial Intelligence. According to data from OpenAlex, Michael Spratling has authored 72 papers receiving a total of 2.5k indexed citations (citations by other indexed papers that have themselves been cited), including 41 papers in Cognitive Neuroscience, 21 papers in Computer Vision and Pattern Recognition and 20 papers in Artificial Intelligence. Recurrent topics in Michael Spratling's work include Neural dynamics and brain function (34 papers), Visual perception and processing mechanisms (20 papers) and Neural Networks and Applications (12 papers). Michael Spratling is often cited by papers focused on Neural dynamics and brain function (34 papers), Visual perception and processing mechanisms (20 papers) and Neural Networks and Applications (12 papers). Michael Spratling collaborates with scholars based in United Kingdom, United States and Luxembourg. Michael Spratling's co-authors include Mark H. Johnson, Gergely Csibra, Sylvain Sirois, Denis Mareschal, Michael S. C. Thomas, Gert Westermann, Greg Davis, Kris De Meyer, Sarah Grice and Michelle de Haan and has published in prestigious journals such as Science, Journal of Neuroscience and SHILAP Revista de lepidopterología.

In The Last Decade

Michael Spratling

68 papers receiving 2.4k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Michael Spratling United Kingdom 23 1.7k 333 322 311 294 72 2.5k
Suzanna Becker Canada 32 2.3k 1.3× 557 1.7× 354 1.1× 708 2.3× 659 2.2× 85 4.0k
Jochen Triesch Germany 31 1.8k 1.0× 496 1.5× 765 2.4× 414 1.3× 434 1.5× 188 3.2k
Virginia R. de United States 22 1.3k 0.8× 497 1.5× 236 0.7× 220 0.7× 462 1.6× 70 2.1k
Sean M. Polyn United States 18 3.5k 2.0× 394 1.2× 153 0.5× 501 1.6× 381 1.3× 34 3.9k
Srikanth Ryali United States 32 4.4k 2.5× 268 0.8× 160 0.5× 218 0.7× 268 0.9× 70 5.5k
Kendrick Kay United States 31 4.2k 2.4× 154 0.5× 620 1.9× 240 0.8× 285 1.0× 96 5.0k
Günther Palm Germany 23 1.0k 0.6× 63 0.2× 258 0.8× 384 1.2× 675 2.3× 113 2.1k
Stephen José Hanson United States 26 1.6k 0.9× 359 1.1× 304 0.9× 229 0.7× 673 2.3× 70 3.0k
Takeo Watanabe United States 42 5.1k 2.9× 256 0.8× 260 0.8× 707 2.3× 158 0.5× 162 5.9k
Ariel Rokem United States 34 2.0k 1.2× 131 0.4× 161 0.5× 544 1.7× 106 0.4× 93 4.1k

Countries citing papers authored by Michael Spratling

Since Specialization
Citations

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

Fields of papers citing papers by Michael Spratling

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Michael Spratling

This figure shows the co-authorship network connecting the top 25 collaborators of Michael Spratling. A scholar is included among the top collaborators of Michael Spratling 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 Michael Spratling. Michael Spratling 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.
Spratling, Michael, et al.. (2025). Robust shortcut and disordered robustness: Improving adversarial training through adaptive smoothing. Pattern Recognition. 163. 111474–111474.
2.
Qiu, Jianing, et al.. (2024). AROID: Improving Adversarial Robustness Through Online Instance-Wise Data Augmentation. International Journal of Computer Vision. 133(2). 929–950. 2 indexed citations
3.
Spratling, Michael, et al.. (2023). Query semantic reconstruction for background in few-shot segmentation. The Visual Computer. 40(2). 799–810. 4 indexed citations
4.
Spratling, Michael. (2016). Predictive coding as a model of cognition. Cognitive Processing. 17(3). 279–305. 67 indexed citations
5.
Spratling, Michael. (2016). A review of predictive coding algorithms. Brain and Cognition. 112. 92–97. 236 indexed citations
6.
Spratling, Michael. (2016). A Hierarchical Predictive Coding Model of Object Recognition in Natural Images. Cognitive Computation. 9(2). 151–167. 43 indexed citations
7.
Sirois, Sylvain, Michael Spratling, Michael S. C. Thomas, et al.. (2014). Neuroconstructivism: How the brain constructs cognition. SHILAP Revista de lepidopterología. 3 indexed citations
8.
Spratling, Michael. (2013). Classification using sparse representations: a biologically plausible approach. Biological Cybernetics. 108(1). 61–73. 14 indexed citations
9.
Spratling, Michael. (2012). Image Segmentation Using a Sparse Coding Model of Cortical Area V1. IEEE Transactions on Image Processing. 22(4). 1631–1643. 57 indexed citations
10.
Meyer, Kris De & Michael Spratling. (2012). A Model of Partial Reference Frame Transforms Through Pooling of Gain-Modulated Responses. Cerebral Cortex. 23(5). 1230–1239. 12 indexed citations
11.
Spratling, Michael. (2010). Predictive Coding as a Model of Response Properties in Cortical Area V1. Journal of Neuroscience. 30(9). 3531–3543. 143 indexed citations
12.
Meyer, Kris De & Michael Spratling. (2009). A model of non-linear interactions between cortical top-down and horizontal connections explains the attentional gating of collinear facilitation. Vision Research. 49(5). 553–568. 12 indexed citations
13.
Spratling, Michael, et al.. (2009). Unsupervised Learning of Overlapping Image Components Using Divisive Input Modulation. Computational Intelligence and Neuroscience. 2009(1). 381457–381457. 56 indexed citations
14.
Spratling, Michael. (2008). Predictive coding as a model of biased competition in visual attention. Vision Research. 48(12). 1391–1408. 157 indexed citations
15.
Sirois, Sylvain, Michael Spratling, Michael S. C. Thomas, et al.. (2008). Précis ofNeuroconstructivism: How the Brain Constructs Cognition. Behavioral and Brain Sciences. 31(3). 321–331. 79 indexed citations
16.
Spratling, Michael. (2006). Learning Image Components for Object Recognition. Journal of Machine Learning Research. 7(28). 793–815. 56 indexed citations
17.
Westermann, Gert, Denis Mareschal, Mark H. Johnson, et al.. (2006). Neuroconstructivism. Developmental Science. 10(1). 75–83. 120 indexed citations
18.
Spratling, Michael. (2005). Learning viewpoint invariant perceptual representations from cluttered images. IEEE Transactions on Pattern Analysis and Machine Intelligence. 27(5). 753–761. 25 indexed citations
19.
Spratling, Michael & Gillian R. Hayes. (1998). A self-organising neural network for modelling cortical development.. CogPrints (University of Southampton). 333–338. 1 indexed citations
20.
Spratling, Michael & Gillian R. Hayes. (1998). Learning sensory-motor cortical mappings without training.. CogPrints (University of Southampton). 339–344. 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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