Aušra Saudargienė
- Cognitive Neuroscience top 10%
- Neural dynamics and brain function 12
- Memory and Neural Mechanisms 3
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- Neuroscience and Neuropharmacology Research 13
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- Advanced Memory and Neural Computing 8
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- Neuroinflammation and Neurodegeneration Mechanisms 3
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- Alzheimer's disease research and treatments 2
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- Cardiac Health and Mental Health 2
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- Neural Networks and Applications 2
- Co-authors
- Bernd PorrFlorentin WörgötterMarja‐Leena LinneAdomas BunevičiusAistė PranckevičienėTiina ManninenRima NaginienėInga Griškova-Bulanova
- Partner nations
- LithuaniaUnited KingdomUnited States
In The Last Decade
Aušra Saudargienė
30 papers receiving 389 citations
Peers
Comparison fields: 5 of 98
- Cognitive Neuroscience 125
- Cellular and Molecular Neuroscience 116
- Biological Psychiatry 13
- Applied Psychology 22
- Clinical Psychology 67
Countries citing papers authored by Aušra Saudargienė
This map shows the geographic impact of Aušra Saudargienė'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 Aušra Saudargienė with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Aušra Saudargienė more than expected).
Fields of papers citing papers by Aušra Saudargienė
This network shows the impact of papers produced by Aušra Saudargienė. 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 Aušra Saudargienė. The network helps show where Aušra Saudargienė may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Aušra Saudargienė, 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 | 2025 | 1 | |
| 2 | 2023 | 2 | |
| 3 | 2023 | 2 | |
| 4 | 2022 | 12 | |
| 5 | 2022 | 3 | |
| 6 | 2022 | 41 | |
| 7 | 2021 | 1 | |
| 8 | 2021 | 65 | |
| 9 | 2020 | 23 | |
| 10 | 2020 | 4 | |
| 11 | 2019 | 9 | |
| 12 | 2018 | 19 | |
| 13 | 2015 | 5 | |
| 14 | 2012 | 1 | |
| 15 | Psichologinių tyrimų duomenų analizė : praktikos darbai | 2010 | 1 |
| 16 | Statistika su SPSS psichologiniuose tyrimuose : mokomoji knyga | 2006 | 3 |
| 17 | 2005 | 14 | |
| 18 | 2004 | 6 | |
| 19 | Analytical Solution of Spike-timing Dependent Plasticity Based on Synaptic Biophysics | 2003 | 10 |
| 20 | 1999 | 6 |
About Aušra Saudargienė
Aušra Saudargienė is a scholar working on Cognitive Neuroscience, Cellular and Molecular Neuroscience and Neurology, having authored 31 papers that have together received 398 indexed citations. Recurring topics across this work include Neuroscience and Neuropharmacology Research (13 papers), Neural dynamics and brain function (12 papers), Advanced Memory and Neural Computing (8 papers), Memory and Neural Mechanisms (3 papers), Neuroinflammation and Neurodegeneration Mechanisms (3 papers), Alzheimer's disease research and treatments (2 papers), Cardiac Health and Mental Health (2 papers) and Neural Networks and Applications (2 papers). The work is most often cited by research in Cognitive Neuroscience (125 citations), Cellular and Molecular Neuroscience (116 citations) and Biological Psychiatry (13 citations). Aušra Saudargienė has collaborated with scholars based in Lithuania, United Kingdom and United States. Frequent co-authors include Bernd Porr, Florentin Wörgötter, Marja‐Leena Linne, Adomas Bunevičius, Aistė Pranckevičienė, Tiina Manninen, Rima Naginienė, Inga Griškova-Bulanova, Julius Burkauskas and Julija Gečaitė-Stončienė. Their work appears in journals such as BMC Neuroscience, Biosystems, Neural Computation, Frontiers in Psychiatry and Advances in experimental medicine and biology.
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.