Shabnam Kadir
- Cognitive Neuroscience top 2%
- Cellular and Molecular Neuroscience top 5%
- Electrical and Electronic Engineering
- Sensory Systems top 5%
- Molecular Biology
- Co-authors
- Kenneth D. HarrisDan F. M. GoodmanMatteo CarandiniGyörgy BuzsákiAndreas S. ToliasCyrille RossantAndres GrosmarkAman B. Saleem
- Topics
- Neural dynamics and brain function (7 papers)EEG and Brain-Computer Interfaces (4 papers)Neural Networks and Applications (2 papers)
- Partner nations
- United KingdomUnited StatesItaly
In The Last Decade
Shabnam Kadir
10 papers receiving 960 citations
Hit Papers
Peers
Comparison fields: 5 of 87
- Cognitive Neuroscience 804
- Cellular and Molecular Neuroscience 593
- Electrical and Electronic Engineering 106
- Sensory Systems 81
- Molecular Biology 60
Countries citing papers authored by Shabnam Kadir
This map shows the geographic impact of Shabnam Kadir'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 Shabnam Kadir with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shabnam Kadir more than expected).
Fields of papers citing papers by Shabnam Kadir
This network shows the impact of papers produced by Shabnam Kadir. 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 Shabnam Kadir. The network helps show where Shabnam Kadir may publish in the future.
Co-authorship network of co-authors of Shabnam Kadir
This figure shows the co-authorship network connecting the top 25 collaborators of Shabnam Kadir. A scholar is included among the top collaborators of Shabnam Kadir 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 Shabnam Kadir. Shabnam Kadir is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 30 | |
| 3 | 17 | |
| 4 | 8 | |
| 5 | Fast and accurate spike sorting of high-channel count probes with KiloSort | 166 |
| 6 | Spike sorting for large, dense electrode arraysbreakdown → | 510 |
| 7 | 225 | |
| 8 | 1 | |
| 9 | 4 | |
| 10 | Approximate Analytical Solution of 3D Fractional Microscale Heat Equation Using Modified Homotopy Perturbation Method | 2 |
About Shabnam Kadir
Shabnam Kadir is a scholar working on Cognitive Neuroscience, Modeling and Simulation and Geometry and Topology, having authored 10 papers that have together received 966 indexed citations. Recurring topics across this work include Neural dynamics and brain function (7 papers), EEG and Brain-Computer Interfaces (4 papers) and Neural Networks and Applications (2 papers). The work is most often cited by research in Cognitive Neuroscience (804 citations), Cellular and Molecular Neuroscience (593 citations) and Sensory Systems (81 citations). Shabnam Kadir has collaborated with scholars based in United Kingdom, United States and Italy. Frequent co-authors include Kenneth D. Harris, Dan F. M. Goodman, Matteo Carandini, György Buzsáki, Andreas S. Tolias, Cyrille Rossant, Andres Grosmark, Aman B. Saleem, Alexander S. Ecker and George H. Denfield. Their work appears in journals such as Nature Neuroscience, NeuroImage and Neural Computation.
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.