Rava Azeredo da Silveira

1.3k total citations
30 papers, 742 citations indexed

About

Rava Azeredo da Silveira is a scholar working on Cognitive Neuroscience, Cellular and Molecular Neuroscience and Molecular Biology. According to data from OpenAlex, Rava Azeredo da Silveira has authored 30 papers receiving a total of 742 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Cognitive Neuroscience, 12 papers in Cellular and Molecular Neuroscience and 11 papers in Molecular Biology. Recurrent topics in Rava Azeredo da Silveira's work include Neural dynamics and brain function (16 papers), Retinal Development and Disorders (8 papers) and Photoreceptor and optogenetics research (7 papers). Rava Azeredo da Silveira is often cited by papers focused on Neural dynamics and brain function (16 papers), Retinal Development and Disorders (8 papers) and Photoreceptor and optogenetics research (7 papers). Rava Azeredo da Silveira collaborates with scholars based in France, United States and Switzerland. Rava Azeredo da Silveira's co-authors include Botond Roska, Gautam B. Awatramani, Sandra Siegert, Thomas A. Münch, Tim J. Viney, Felix Franke, Andreas Hierlemann, Yariv Kafri, Michele Fiscella and Michael J. Berry and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Physical Review Letters and Neuron.

In The Last Decade

Rava Azeredo da Silveira

30 papers receiving 736 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Rava Azeredo da Silveira France 14 439 416 395 49 40 30 742
Dumitru Petrusca United States 5 517 1.2× 298 0.7× 746 1.9× 146 3.0× 15 0.4× 7 941
Michael Weliky United States 13 852 1.9× 361 0.9× 1.2k 3.0× 96 2.0× 5 0.1× 19 1.6k
Michael E. Rudd United States 17 174 0.4× 130 0.3× 736 1.9× 31 0.6× 4 0.1× 52 915
Jürgen Reingruber France 14 397 0.9× 461 1.1× 86 0.2× 12 0.2× 8 0.2× 26 739
Maxim I. Molodtsov United States 14 124 0.3× 734 1.8× 89 0.2× 26 0.5× 20 0.5× 20 1.2k
Ulisse Ferrari France 13 110 0.3× 94 0.2× 142 0.4× 20 0.4× 64 1.6× 26 341
Jonathan Kadmon United States 6 334 0.8× 125 0.3× 351 0.9× 48 1.0× 14 0.3× 10 689
Nicolas M. Brunet United States 16 439 1.0× 144 0.3× 999 2.5× 76 1.6× 13 0.3× 28 1.3k
Leslie C. Osborne United States 10 160 0.4× 98 0.2× 533 1.3× 70 1.4× 3 0.1× 15 689
Nikita Vladimirov United States 14 242 0.6× 378 0.9× 200 0.5× 16 0.3× 84 2.1× 21 810

Countries citing papers authored by Rava Azeredo da Silveira

Since Specialization
Citations

This map shows the geographic impact of Rava Azeredo da Silveira'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 Rava Azeredo da Silveira with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rava Azeredo da Silveira more than expected).

Fields of papers citing papers by Rava Azeredo da Silveira

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Rava Azeredo da Silveira. 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 Rava Azeredo da Silveira. The network helps show where Rava Azeredo da Silveira may publish in the future.

Co-authorship network of co-authors of Rava Azeredo da Silveira

This figure shows the co-authorship network connecting the top 25 collaborators of Rava Azeredo da Silveira. A scholar is included among the top collaborators of Rava Azeredo da Silveira based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Rava Azeredo da Silveira. Rava Azeredo da Silveira is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Harmening, Wolf M., et al.. (2025). Fixational eye movements as active sensation for high visual acuity. Proceedings of the National Academy of Sciences. 122(6). e2416266122–e2416266122. 3 indexed citations
2.
Silveira, Rava Azeredo da, et al.. (2025). Universal statistics of hippocampal place fields across species and dimensionalities. Neuron. 113(7). 1110–1120.e3. 2 indexed citations
3.
Aridor, Guy, Rava Azeredo da Silveira, & Michael Woodford. (2024). Information-constrained coordination of economic behavior. Journal of Economic Dynamics and Control. 172. 104985–104985. 1 indexed citations
4.
Woodford, Michael, et al.. (2024). Jointly efficient encoding and decoding in neural populations. PLoS Computational Biology. 20(7). e1012240–e1012240. 1 indexed citations
5.
Aridor, Guy, Rava Azeredo da Silveira, & Michael Woodford. (2024). Information-Constrained Coordination of Economic Behavior. SSRN Electronic Journal. 1 indexed citations
6.
Meyniel, Florent, et al.. (2023). Resource-rational account of sequential effects in human prediction. eLife. 13. 2 indexed citations
7.
Meyniel, Florent, et al.. (2021). Biases and Variability from Costly Bayesian Inference. Entropy. 23(5). 603–603. 2 indexed citations
8.
Silveira, Rava Azeredo da & Michael Woodford. (2019). Noisy Memory and Over-Reaction to News. AEA Papers and Proceedings. 109. 557–561. 15 indexed citations
9.
Drinnenberg, Antonia, Felix Franke, Rei Morikawa, et al.. (2018). How Diverse Retinal Functions Arise from Feedback at the First Visual Synapse. Neuron. 99(1). 117–134.e11. 37 indexed citations
10.
Pernice, Volker & Rava Azeredo da Silveira. (2018). Interpretation of correlated neural variability from models of feed-forward and recurrent circuits. PLoS Computational Biology. 14(2). e1005979–e1005979. 3 indexed citations
11.
Franke, Felix, et al.. (2016). Structures of Neural Correlation and How They Favor Coding. Neuron. 89(2). 409–422. 79 indexed citations
12.
Szikra, Tamás, Stuart Trenholm, Antonia Drinnenberg, et al.. (2014). Rods in daylight act as relay cells for cone-driven horizontal cell–mediated surround inhibition. Nature Neuroscience. 17(12). 1728–1735. 54 indexed citations
13.
Silveira, Rava Azeredo da & Michael J. Berry. (2014). High-Fidelity Coding with Correlated Neurons. PLoS Computational Biology. 10(11). e1003970–e1003970. 31 indexed citations
14.
Clark, Damon A., et al.. (2013). Dynamical Adaptation in Photoreceptors. PLoS Computational Biology. 9(11). e1003289–e1003289. 36 indexed citations
15.
Chen, Eric, Olivier Marre, Clark Fisher, et al.. (2013). Alert Response to Motion Onset in the Retina. Journal of Neuroscience. 33(1). 120–132. 30 indexed citations
16.
Silveira, Rava Azeredo da & Botond Roska. (2011). Cell Types, Circuits, Computation. Current Opinion in Neurobiology. 21(5). 664–671. 38 indexed citations
17.
Haselwandter, Christoph A., Martino Calamai, Mehran Kardar, Antoine Triller, & Rava Azeredo da Silveira. (2011). Formation and Stability of Synaptic Receptor Domains. Physical Review Letters. 106(23). 238104–238104. 33 indexed citations
18.
Münch, Thomas A., et al.. (2008). A Functional Role of AII Amacrine Cells in Light-Adapted Retina. Investigative Ophthalmology & Visual Science. 49(13). 1415–1415. 1 indexed citations
19.
Kafri, Yariv & Rava Azeredo da Silveira. (2008). Steady-State Chemotaxis inEscherichia coli. Physical Review Letters. 100(23). 238101–238101. 24 indexed citations
20.
Silveira, Rava Azeredo da & Stefano Zapperi. (2004). Critical hysteresis from random anisotropy. Physical Review B. 69(21). 7 indexed citations

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.

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