Lea Duncker
Impact in
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- Neural dynamics and brain function
- Memory and Neural Mechanisms
- EEG and Brain-Computer Interfaces
- Functional Brain Connectivity Studies
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- Neuroscience and Neuropharmacology Research
Papers in
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- Neural dynamics and brain function 5
- Memory and Neural Mechanisms 1
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- Domain Adaptation and Few-Shot Learning 2
- Gaussian Processes and Bayesian Inference 1
- Neural Networks and Applications 1
- Co-authors
- Laura Driscoll (2 shared papers)Christopher D. Harvey (1 shared paper)Maneesh Sahani (3 shared papers)David Sussillo (1 shared paper)Krishna V. Shenoy (1 shared paper)Greg D. Field (1 shared paper)Jonathan W. Pillow (1 shared paper)
- Journals
- Current Opinion in Neurobiology (2 papers)Neural Computation (1 paper)UCL Discovery (University College London) (2 papers)
- Partner nations
- United StatesUnited Kingdom
In The Last Decade
Lea Duncker
5 papers receiving 119 citations
Peers
Comparison fields: 5 of 26
- Cognitive Neuroscience 96
- Cellular and Molecular Neuroscience 34
- Artificial Intelligence 43
- Biophysics 7
- Structural Biology 1
Countries citing papers authored by Lea Duncker
This map shows the geographic impact of Lea Duncker'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 Lea Duncker with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lea Duncker more than expected).
Fields of papers citing papers by Lea Duncker
This network shows the impact of papers produced by Lea Duncker. 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 Lea Duncker. The network helps show where Lea Duncker may publish in the future.
Co-authors
The 7 scholars most cited alongside Lea Duncker, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2022 | 64 | |
| 2 | 2021 | 29 | |
| 3 | Organizing recurrent network dynamics by task-computation to enable continual learning | 2020 | 23 |
| 4 | 2023 | 3 | |
| 5 | Temporal alignment and latent Gaussian process factor inference in population spike trains | 2018 | 2 |
About Lea Duncker
Lea Duncker is a scholar working on Cognitive Neuroscience, Artificial Intelligence, Cellular and Molecular Neuroscience, Molecular Biology and Biophysics, having authored 5 papers that have together received 121 indexed citations. Recurring topics across this work include Neural dynamics and brain function (5 papers), Domain Adaptation and Few-Shot Learning (2 papers), Advanced Fluorescence Microscopy Techniques (1 paper), Memory and Neural Mechanisms (1 paper), Receptor Mechanisms and Signaling (1 paper), Gaussian Processes and Bayesian Inference (1 paper), Neurobiology and Insect Physiology Research (1 paper) and Neural Networks and Applications (1 paper). The work is most often cited by research in Cognitive Neuroscience (96 citations), Cellular and Molecular Neuroscience (34 citations), Artificial Intelligence (43 citations), Biophysics (7 citations) and Structural Biology (1 citation). Lea Duncker has collaborated with scholars based in United States and United Kingdom. Frequent co-authors include Laura Driscoll, Christopher D. Harvey, Maneesh Sahani, David Sussillo, Krishna V. Shenoy, Greg D. Field and Jonathan W. Pillow. Their work appears in journals such as Current Opinion in Neurobiology, Neural Computation and UCL Discovery (University College London).
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.