Independent learning of internal models for kinematic and dynamic control of reaching
- Journal
- Nature Neuroscience
In The Last Decade
doi.org/10.1038/14826 →Countries where authors are citing Independent learning of internal models for kinematic and dynamic control of reaching
This map shows the geographic impact of Independent learning of internal models for kinematic and dynamic control of reaching. 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 Independent learning of internal models for kinematic and dynamic control of reaching with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Independent learning of internal models for kinematic and dynamic control of reaching more than expected).
Fields of papers citing Independent learning of internal models for kinematic and dynamic control of reaching
This network shows the impact of Independent learning of internal models for kinematic and dynamic control of reaching. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Independent learning of internal models for kinematic and dynamic control of reaching.
About Independent learning of internal models for kinematic and dynamic control of reaching
This paper, published in 1999, received 609 indexed citations . Written by John W. Krakauer, M. F. Ghilardi and Claude Ghez covering the research area of Cognitive Neuroscience, Biomedical Engineering and Social Psychology. It is primarily cited by scholars working on Cognitive Neuroscience (572 citations), Biomedical Engineering (295 citations) and Social Psychology (257 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/10.1038/14826.