Misha Tsodyks
- Cognitive Neuroscience top 0.05%
- Neural dynamics and brain function 88
- Visual perception and processing mechanisms 23
- Memory and Neural Mechanisms 22
- Cellular and Molecular Neuroscience top 0.1%
- Neuroscience and Neuropharmacology Research 32
- Photoreceptor and optogenetics research 13
- Neuroscience and Neural Engineering 13
- Statistical and Nonlinear Physics top 0.5%
- Sensory Systems top 1%
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- Advanced Memory and Neural Computing 24
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- Neural Networks and Applications 29
- Co-authors
- Henry MarkramOmri BarakDaniel J. AmitYun WangAmos ArieliAmiram GrinvaldTal KenetGianluigi Mongillo
- Journals
- Nature (4 papers)Science (3 papers)Proceedings of the National Academy of Sciences (6 papers)
- Partner nations
- IsraelUnited StatesRussia
In The Last Decade
Misha Tsodyks
121 papers receiving 11.0k citations
Hit Papers
Peers
Comparison fields: 5 of 144
- Cognitive Neuroscience 10.1k
- Cellular and Molecular Neuroscience 6.0k
- Statistical and Nonlinear Physics 1.4k
- Sensory Systems 297
- Electrical and Electronic Engineering 2.8k
Countries citing papers authored by Misha Tsodyks
This map shows the geographic impact of Misha Tsodyks'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 Misha Tsodyks with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Misha Tsodyks more than expected).
Fields of papers citing papers by Misha Tsodyks
This network shows the impact of papers produced by Misha Tsodyks. 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 Misha Tsodyks. The network helps show where Misha Tsodyks may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Misha Tsodyks, 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 | 0 | |
| 2 | 2023 | 2 | |
| 3 | 2021 | 2 | |
| 4 | 2021 | 2 | |
| 5 | 2018 | 14 | |
| 6 | 2017 | 2 | |
| 7 | 2016 | 71 | |
| 8 | 2015 | 24 | |
| 9 | 2013 | 31 | |
| 10 | 2013 | 11 | |
| 11 | 2013 | 28 | |
| 12 | 2012 | 2 | |
| 13 | 2007 | 20 | |
| 14 | 2007 | 10 | |
| 15 | 2006 | 167 | |
| 16 | 2004 | 83 | |
| 17 | 2004 | 81 | |
| 18 | 2002 | 128 | |
| 19 | The neural code between neocortical pyramidal neurons depends on neurotransmitter release probabilitybreakdown → | 1997 | 1166 |
| 20 | 1991 | 28 |
About Misha Tsodyks
Misha Tsodyks is a scholar working on Cognitive Neuroscience, Cellular and Molecular Neuroscience and Artificial Intelligence, having authored 125 papers that have together received 11.3k indexed citations. Recurring topics across this work include Neural dynamics and brain function (88 papers), Neuroscience and Neuropharmacology Research (32 papers), Neural Networks and Applications (29 papers), Advanced Memory and Neural Computing (24 papers), Visual perception and processing mechanisms (23 papers), Memory and Neural Mechanisms (22 papers), Photoreceptor and optogenetics research (13 papers) and Neuroscience and Neural Engineering (13 papers). The work is most often cited by research in Cognitive Neuroscience (10.1k citations), Cellular and Molecular Neuroscience (6.0k citations) and Statistical and Nonlinear Physics (1.4k citations). Misha Tsodyks has collaborated with scholars based in Israel, United States and Russia. Frequent co-authors include Henry Markram, Omri Barak, Daniel J. Amit, Yun Wang, Amos Arieli, Amiram Grinvald, Tal Kenet, Gianluigi Mongillo, Klaus Pawelzik and Terrence J. Sejnowski. Their work appears in journals such as Nature, Science and Proceedings of the National Academy of Sciences.
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.