Fred Rieke

15.3k total citations · 2 hit papers
135 papers, 10.4k citations indexed

About

Fred Rieke is a scholar working on Molecular Biology, Cellular and Molecular Neuroscience and Cognitive Neuroscience. According to data from OpenAlex, Fred Rieke has authored 135 papers receiving a total of 10.4k indexed citations (citations by other indexed papers that have themselves been cited), including 93 papers in Molecular Biology, 89 papers in Cellular and Molecular Neuroscience and 80 papers in Cognitive Neuroscience. Recurrent topics in Fred Rieke's work include Retinal Development and Disorders (87 papers), Neural dynamics and brain function (74 papers) and Photoreceptor and optogenetics research (61 papers). Fred Rieke is often cited by papers focused on Retinal Development and Disorders (87 papers), Neural dynamics and brain function (74 papers) and Photoreceptor and optogenetics research (61 papers). Fred Rieke collaborates with scholars based in United States, United Kingdom and Japan. Fred Rieke's co-authors include William Bialek, Rob de Ruyter van Steveninck, D. A. Baylor, Greg D. Field, David K. Warland, Rob R. de Ruyter van Steveninck, Eric A. Schwartz, Felice A. Dunn, Gregory W. Schwartz and Alapakkam P. Sampath and has published in prestigious journals such as Nature, Science and Cell.

In The Last Decade

Fred Rieke

132 papers receiving 10.2k citations

Hit Papers

Spikes: Exploring the Neural Code 1991 2026 2002 2014 1996 1991 500 1000 1.5k

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Fred Rieke United States 53 5.9k 5.5k 5.4k 905 838 135 10.4k
Simon B. Laughlin United Kingdom 50 6.8k 1.2× 2.8k 0.5× 5.8k 1.1× 1.1k 1.2× 593 0.7× 98 12.7k
Markus Meister United States 58 8.9k 1.5× 5.9k 1.1× 7.3k 1.4× 1.2k 1.3× 528 0.6× 93 15.2k
Matteo Carandini United Kingdom 64 8.2k 1.4× 1.9k 0.3× 14.0k 2.6× 1.1k 1.2× 514 0.6× 126 15.9k
Michael Häusser United Kingdom 66 11.8k 2.0× 3.5k 0.6× 9.7k 1.8× 1.9k 2.1× 572 0.7× 121 16.0k
Alexander Sher United States 37 4.7k 0.8× 2.6k 0.5× 3.3k 0.6× 1.5k 1.7× 343 0.4× 90 9.0k
Robert Shapley United States 75 6.2k 1.1× 4.1k 0.7× 15.0k 2.8× 648 0.7× 349 0.4× 194 17.0k
R. Clay Reid United States 55 6.9k 1.2× 2.4k 0.4× 9.5k 1.8× 964 1.1× 403 0.5× 92 11.8k
William T. Newsome United States 66 6.7k 1.1× 2.4k 0.4× 20.3k 3.8× 1.1k 1.2× 1.1k 1.3× 100 22.4k
S. Murray Sherman United States 76 10.8k 1.8× 5.3k 1.0× 13.5k 2.5× 509 0.6× 339 0.4× 223 18.7k
Yasushi Miyashita Japan 62 4.5k 0.8× 2.2k 0.4× 10.8k 2.0× 443 0.5× 129 0.2× 232 16.0k

Countries citing papers authored by Fred Rieke

Since Specialization
Citations

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

Fields of papers citing papers by Fred Rieke

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Fred Rieke. 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 Fred Rieke. The network helps show where Fred Rieke may publish in the future.

Co-authorship network of co-authors of Fred Rieke

This figure shows the co-authorship network connecting the top 25 collaborators of Fred Rieke. A scholar is included among the top collaborators of Fred Rieke 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 Fred Rieke. Fred Rieke 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.
Idrees, Saad, Michael B. Manookin, Fred Rieke, Greg D. Field, & Joel Zylberberg. (2024). Biophysical neural adaptation mechanisms enable artificial neural networks to capture dynamic retinal computation. Nature Communications. 15(1). 5957–5957. 4 indexed citations
2.
Grimes, William N., et al.. (2023). Rod-cone signal interference in the retina shapes perception in primates. SHILAP Revista de lepidopterología. 3. 1230084–1230084.
3.
Patterson, Sara S., et al.. (2022). Conserved circuits for direction selectivity in the primate retina. Current Biology. 32(11). 2529–2538.e4. 17 indexed citations
4.
Gutierrez, Gabrielle J., Fred Rieke, & Eric Shea‐Brown. (2021). Nonlinear convergence boosts information coding in circuits with parallel outputs. Proceedings of the National Academy of Sciences. 118(8). 4 indexed citations
5.
Grimes, William N., Mrinalini Hoon, Takeshi Yoshimatsu, et al.. (2021). A High-Density Narrow-Field Inhibitory Retinal Interneuron with Direct Coupling to Müller Glia. Journal of Neuroscience. 41(28). 6018–6037. 12 indexed citations
6.
Jorstad, Nikolas L., Matthew S. Wilken, Levi Todd, et al.. (2020). STAT Signaling Modifies Ascl1 Chromatin Binding and Limits Neural Regeneration from Muller Glia in Adult Mouse Retina. Cell Reports. 30(7). 2195–2208.e5. 80 indexed citations
7.
Okawa, Haruhisa, Wan‐Qing Yu, Ulf Matti, et al.. (2019). Dynamic assembly of ribbon synapses and circuit maintenance in a vertebrate sensory system. Nature Communications. 10(1). 2167–2167. 28 indexed citations
8.
Field, Greg D., et al.. (2018). Temporal resolution of single-photon responses in primate rod photoreceptors and limits imposed by cellular noise. Journal of Neurophysiology. 121(1). 255–268. 11 indexed citations
9.
Erickson‐Davis, Cordelia, Nicolas P. Cottaris, Fred Rieke, et al.. (2018). Simulation of visual perception and learning with a retinal prosthesis. Journal of Neural Engineering. 16(2). 25003–25003. 22 indexed citations
10.
Jorstad, Nikolas L., Matthew S. Wilken, William N. Grimes, et al.. (2017). Stimulation of functional neuronal regeneration from Müller glia in adult mice. Nature. 548(7665). 103–107. 339 indexed citations
11.
Latimer, Kenneth W., E. J. Chichilnisky, Fred Rieke, & Jonathan W. Pillow. (2014). Inferring synaptic conductances from spike trains with a biophysically inspired point process model. Neural Information Processing Systems. 27. 954–962. 9 indexed citations
12.
Angueyra, Juan M & Fred Rieke. (2013). Asymmetries between ON and OFF responses in primate vision first arise in photoreceptors. Investigative Ophthalmology & Visual Science. 54(15). 1293–1293. 2 indexed citations
13.
Doan, Thuy, Anthony W. Azevedo, James B. Hurley, & Fred Rieke. (2009). Arrestin Competition Influences the Kinetics and Variability of the Single-Photon Responses of Mammalian Rod Photoreceptors. Journal of Neuroscience. 29(38). 11867–11879. 30 indexed citations
14.
Caruso, Giovanni, et al.. (2005). Mathematical and computational modelling of spatio-temporal signalling in rod phototransduction. PubMed. 152(3). 119–119. 19 indexed citations
15.
Baehr, Wolfgang, et al.. (2005). The Physiological Role of PDE in Mouse Photoreceptors. Investigative Ophthalmology & Visual Science. 46(13). 1699–1699. 1 indexed citations
16.
Kim, Kerry J. & Fred Rieke. (2003). Slow Na+Inactivation and Variance Adaptation in Salamander Retinal Ganglion Cells. Journal of Neuroscience. 23(4). 1506–1516. 104 indexed citations
17.
Rieke, Fred, et al.. (2003). Bandpass Filtering at the Rod to Second-Order Cell Synapse in Salamander (Ambystoma tigrinum) Retina. Journal of Neuroscience. 23(9). 3796–3806. 62 indexed citations
18.
Rieke, Fred & D. A. Baylor. (2000). Origin and Functional Impact of Dark Noise in Retinal Cones. Neuron. 26(1). 181–186. 118 indexed citations
19.
Rieke, Fred. (1999). Computing with Lipids, Proteins, and Ions. Neuron. 23(1). 31–32. 1 indexed citations
20.
Bialek, William, et al.. (1989). Reading a Neural Code. University of Groningen research database (University of Groningen / Centre for Information Technology). 2. 36–43. 2 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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