Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
Impact in
Classified as
- Authors
- Peter DayanL. F. Abbott
- Journal
- MPG.PuRe (Max Planck Society)
In The Last Decade
doi.org/w80884487 →Countries where authors are citing Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
This map shows the geographic impact of Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. 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 Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems more than expected).
Fields of papers citing Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
This network shows the impact of Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems.
About Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
This paper, published in 2001, received 2.0k indexed citations . Written by Peter Dayan and L. F. Abbott covering the research area of Cognitive Neuroscience and Cellular and Molecular Neuroscience. It is primarily cited by scholars working on Cognitive Neuroscience (1.5k citations), Cellular and Molecular Neuroscience (740 citations), Electrical and Electronic Engineering (499 citations), Artificial Intelligence (360 citations) and Statistical and Nonlinear Physics (348 citations). Published in MPG.PuRe (Max Planck Society).
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/w80884487.