Gašper Tkačik
Impact in
- Cognitive Neuroscience top 2%
- Neural dynamics and brain function
- Visual perception and processing mechanisms
- Biophysics top 1%
Papers in
-
- Neural dynamics and brain function 36
- Visual perception and processing mechanisms 12
- Genetics 25
- Evolution and Genetic Dynamics 18
- Co-authors
- William BialekThomas GregorAleksandra M. WalczakCurtis G. CallanElad SchneidmanOlivier MarreVijay BalasubramanianEric Wieschaus
- Journals
- Proceedings of the National Academy of Sciences (19 papers)PLoS Computational Biology (9 papers)Nature Communications (6 papers)PLoS ONE (6 papers)Genetics (3 papers)
- Partner nations
- AustriaUnited StatesFrance
In The Last Decade
Gašper Tkačik
81 papers receiving 3.3k citations
Peers
Comparison fields: 5 of 149
- Cognitive Neuroscience 1.0k
- Biophysics 218
- Statistical and Nonlinear Physics 367
- Molecular Biology 2.0k
- Cellular and Molecular Neuroscience 473
Countries citing papers authored by Gašper Tkačik
This map shows the geographic impact of Gašper Tkačik'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 Gašper Tkačik with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gašper Tkačik more than expected).
Fields of papers citing papers by Gašper Tkačik
This network shows the impact of papers produced by Gašper Tkačik. 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 Gašper Tkačik. The network helps show where Gašper Tkačik may publish in the future.
Co-authors
The 25 scholars most cited alongside Gašper Tkačik, 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 | 4 | |
| 2 | 2025 | 0 | |
| 3 | 2021 | 36 | |
| 4 | 2021 | 7 | |
| 5 | 2020 | 19 | |
| 6 | 2020 | 32 | |
| 7 | 2018 | 44 | |
| 8 | 2017 | 144 | |
| 9 | 2017 | 86 | |
| 10 | 2017 | 115 | |
| 11 | 2017 | 27 | |
| 12 | 2016 | 15 | |
| 13 | Estimating Nonlinear Neural Response Functions using GP Priors and Kronecker Methods | 2016 | 6 |
| 14 | 2015 | 134 | |
| 15 | Transformation of stimulus correlations by the retina | 2014 | 1 |
| 16 | 2014 | 150 | |
| 17 | 2013 | 64 | |
| 18 | 2012 | 40 | |
| 19 | 2009 | 84 | |
| 20 | 2005 | 149 |
About Gašper Tkačik
Gašper Tkačik is a scholar working on Cognitive Neuroscience, Genetics, Cellular and Molecular Neuroscience, Molecular Biology and Statistical and Nonlinear Physics, having authored 83 papers that have together received 3.4k indexed citations. Recurring topics across this work include Neural dynamics and brain function (36 papers), Gene Regulatory Network Analysis (33 papers), Evolution and Genetic Dynamics (18 papers), Visual perception and processing mechanisms (12 papers), RNA and protein synthesis mechanisms (9 papers), Retinal Development and Disorders (7 papers), Neural Networks and Applications (7 papers) and Neuroscience and Neural Engineering (6 papers). The work is most often cited by research in Cognitive Neuroscience (1.0k citations), Biophysics (218 citations), Statistical and Nonlinear Physics (367 citations), Molecular Biology (2.0k citations) and Cellular and Molecular Neuroscience (473 citations). Gašper Tkačik has collaborated with scholars based in Austria, United States and France. Frequent co-authors include William Bialek, Thomas Gregor, Aleksandra M. Walczak, Curtis G. Callan, Elad Schneidman, Olivier Marre, Vijay Balasubramanian, Eric Wieschaus, Jason Prentice and Călin C. Guet. Their work appears in journals such as Proceedings of the National Academy of Sciences, PLoS Computational Biology, Nature Communications, PLoS ONE and Genetics.
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.