Nihat Ay
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- Statistical Mechanics and Entropy 11
- Cognitive Neuroscience top 5%
- Neural dynamics and brain function 22
- Artificial Intelligence top 5%
- Neural Networks and Applications 16
- Bayesian Modeling and Causal Inference 6
- Reinforcement Learning in Robotics 5
- Developmental Biology top 10%
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- Computability, Logic, AI Algorithms 6
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- Gene Regulatory Network Analysis 8
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- Complex Systems and Time Series Analysis 6
- Co-authors
- Jürgen JostEckehard OlbrichDaniel PolaniNils BertschingerRalf DerGuido MontúfarJohannes RauhThomas Wennekers
- Partner nations
- GermanyUnited StatesUnited Kingdom
In The Last Decade
Nihat Ay
70 papers receiving 1.3k citations
Peers
Comparison fields: 5 of 112
- Statistical and Nonlinear Physics 379
- Cognitive Neuroscience 448
- Artificial Intelligence 462
- Developmental Biology 25
- Computational Theory and Mathematics 173
Countries citing papers authored by Nihat Ay
This map shows the geographic impact of Nihat Ay'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 Nihat Ay with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nihat Ay more than expected).
Fields of papers citing papers by Nihat Ay
This network shows the impact of papers produced by Nihat Ay. 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 Nihat Ay. The network helps show where Nihat Ay may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Nihat Ay, 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 | 2024 | 1 | |
| 3 | 2021 | 2 | |
| 4 | 2020 | 2 | |
| 5 | 2020 | 2 | |
| 6 | 2019 | 5 | |
| 7 | Evaluating morphological computation in muscle and dc-motor driven models of hopping movements | 2016 | 14 |
| 8 | Expressive Power of Conditional Restricted Boltzmann Machines | 2014 | 3 |
| 9 | 2013 | 16 | |
| 10 | 2013 | 34 | |
| 11 | 2009 | 19 | |
| 12 | Predictive information and emergent cooperativity in a chain of mobile robots | 2008 | 9 |
| 13 | Hierarchical Models, Marginal Polytopes, and Linear Codes | 2008 | 3 |
| 14 | 2008 | 11 | |
| 15 | 2007 | 62 | |
| 16 | 2006 | 1 | |
| 17 | 2006 | 24 | |
| 18 | 2006 | 1 | |
| 19 | 2003 | 25 | |
| 20 | 2000 | 3 |
About Nihat Ay
Nihat Ay is a scholar working on Statistical and Nonlinear Physics, Computational Mathematics and Cognitive Neuroscience, having authored 77 papers that have together received 1.3k indexed citations. Recurring topics across this work include Neural dynamics and brain function (22 papers), Neural Networks and Applications (16 papers), Statistical Mechanics and Entropy (11 papers), Gene Regulatory Network Analysis (8 papers), Bayesian Modeling and Causal Inference (6 papers), Complex Systems and Time Series Analysis (6 papers), Computability, Logic, AI Algorithms (6 papers) and Reinforcement Learning in Robotics (5 papers). The work is most often cited by research in Statistical and Nonlinear Physics (379 citations), Cognitive Neuroscience (448 citations) and Artificial Intelligence (462 citations). Nihat Ay has collaborated with scholars based in Germany, United States and United Kingdom. Frequent co-authors include Jürgen Jost, Eckehard Olbrich, Daniel Polani, Nils Bertschinger, Ralf Der, Guido Montúfar, Johannes Rauh, Thomas Wennekers, David C. Krakauer and Hông Vân Lê. Their work appears in journals such as Theory in Biosciences, Neurocomputing, Neural Computation, Advances in Complex Systems and International Journal of Approximate Reasoning.
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.