Hierarchical models of object recognition in cortex
- Journal
- Nature Neuroscience
In The Last Decade
doi.org/10.1038/14819 →Countries where authors are citing Hierarchical models of object recognition in cortex
This map shows the geographic impact of Hierarchical models of object recognition in cortex. 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 Hierarchical models of object recognition in cortex with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hierarchical models of object recognition in cortex more than expected).
Fields of papers citing Hierarchical models of object recognition in cortex
This network shows the impact of Hierarchical models of object recognition in cortex. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Hierarchical models of object recognition in cortex.
About Hierarchical models of object recognition in cortex
This paper, published in 1999, received 2.2k indexed citations . Written by Maximilian Riesenhuber and Tomaso Poggio covering the research area of Cognitive Neuroscience. It is primarily cited by scholars working on Cognitive Neuroscience (1.5k citations), Computer Vision and Pattern Recognition (876 citations) and Artificial Intelligence (361 citations). Published in Nature Neuroscience.
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.
This paper is also available at doi.org/10.1038/14819.