Greg D. Field

4.6k total citations
60 papers, 2.8k citations indexed

About

Greg D. Field is a scholar working on Molecular Biology, Cellular and Molecular Neuroscience and Cognitive Neuroscience. According to data from OpenAlex, Greg D. Field has authored 60 papers receiving a total of 2.8k indexed citations (citations by other indexed papers that have themselves been cited), including 45 papers in Molecular Biology, 38 papers in Cellular and Molecular Neuroscience and 33 papers in Cognitive Neuroscience. Recurrent topics in Greg D. Field's work include Retinal Development and Disorders (45 papers), Neural dynamics and brain function (30 papers) and Photoreceptor and optogenetics research (26 papers). Greg D. Field is often cited by papers focused on Retinal Development and Disorders (45 papers), Neural dynamics and brain function (30 papers) and Photoreceptor and optogenetics research (26 papers). Greg D. Field collaborates with scholars based in United States, Germany and Canada. Greg D. Field's co-authors include E. J. Chichilnisky, Fred Rieke, Alexander Sher, A. M. Litke, Jeffrey L. Gauthier, Jonathon Shlens, Martin Greschner, Alapakkam P. Sampath, Dumitru Petrusca and Suva Roy and has published in prestigious journals such as Nature, Proceedings of the National Academy of Sciences and Nature Communications.

In The Last Decade

Greg D. Field

57 papers receiving 2.7k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Greg D. Field United States 27 1.6k 1.6k 1.6k 220 139 60 2.8k
Stephen A. Baccus United States 24 1.6k 0.9× 1.6k 1.0× 1.2k 0.8× 390 1.8× 69 0.5× 42 3.0k
Sheila Nirenberg United States 21 1.4k 0.8× 1.3k 0.8× 840 0.5× 276 1.3× 297 2.1× 33 2.1k
Jeffrey L. Gauthier United States 21 1.8k 1.1× 1.3k 0.8× 770 0.5× 196 0.9× 175 1.3× 26 2.6k
Frank Sengpiel United Kingdom 34 2.7k 1.6× 1.4k 0.9× 872 0.6× 147 0.7× 69 0.5× 87 3.5k
Ehud Kaplan United States 23 2.1k 1.3× 892 0.6× 1.1k 0.7× 117 0.5× 154 1.1× 38 2.7k
W. Martin Usrey United States 30 4.8k 2.9× 2.5k 1.5× 925 0.6× 275 1.3× 164 1.2× 60 5.4k
Tim Gollisch Germany 23 1.6k 1.0× 1.2k 0.7× 859 0.5× 528 2.4× 185 1.3× 53 2.4k
Frank S. Werblin United States 32 1.5k 0.9× 3.1k 1.9× 2.8k 1.8× 428 1.9× 46 0.3× 67 4.3k
Martin Greschner United States 22 1.1k 0.7× 943 0.6× 925 0.6× 177 0.8× 59 0.4× 38 1.6k
Keith P. Purpura United States 24 2.5k 1.5× 1.1k 0.7× 499 0.3× 286 1.3× 252 1.8× 49 3.0k

Countries citing papers authored by Greg D. Field

Since Specialization
Citations

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

Fields of papers citing papers by Greg D. Field

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Greg D. Field. 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 Greg D. Field. The network helps show where Greg D. Field may publish in the future.

Co-authorship network of co-authors of Greg D. Field

This figure shows the co-authorship network connecting the top 25 collaborators of Greg D. Field. A scholar is included among the top collaborators of Greg D. Field 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 Greg D. Field. Greg D. Field 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.
Fleming, Elizabeth, Greg D. Field, Michael R. Tadross, & Court Hull. (2024). Local synaptic inhibition mediates cerebellar granule cell pattern separation and enables learned sensorimotor associations. Nature Neuroscience. 27(4). 689–701. 6 indexed citations
2.
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
3.
Scalabrino, Miranda L., et al.. (2023). Cones and cone pathways remain functional in advanced retinal degeneration. Current Biology. 33(8). 1513–1522.e4. 17 indexed citations
4.
Duncker, Lea, et al.. (2023). Scalable Variational Inference for Low-Rank Spatiotemporal Receptive Fields. Neural Computation. 35(6). 995–1027. 3 indexed citations
5.
Scalabrino, Miranda L., Lindsey A. Chew, Jason Xu, et al.. (2022). Robust cone-mediated signaling persists late into rod photoreceptor degeneration. eLife. 11. 9 indexed citations
6.
7.
Koh, Sehwon, Suva Roy, Samuel Strader, et al.. (2019). Thrombospondin-1 Promotes Circuit-Specific Synapse Formation Via β1-Integrin. SSRN Electronic Journal. 1 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.
Ray, Thomas A., Suva Roy, Christopher Kozlowski, et al.. (2018). Formation of retinal direction-selective circuitry initiated by starburst amacrine cell homotypic contact. eLife. 7. 37 indexed citations
10.
Field, Greg D., et al.. (2018). Nogo receptor 1 is expressed by nearly all retinal ganglion cells. PLoS ONE. 13(5). e0196565–e0196565. 4 indexed citations
11.
Greschner, Martin, Greg D. Field, Peter H. Li, et al.. (2016). Identification of a Retinal Circuit for Recurrent Suppression Using Indirect Electrical Imaging. Current Biology. 26(15). 1935–1942. 12 indexed citations
12.
Greschner, Martin, Greg D. Field, Peter H. Li, et al.. (2014). A Polyaxonal Amacrine Cell Population in the Primate Retina. Journal of Neuroscience. 34(10). 3597–3606. 45 indexed citations
13.
Li, Peter H., Greg D. Field, Martin Greschner, et al.. (2014). Retinal Representation of the Elementary Visual Signal. Neuron. 81(1). 130–139. 29 indexed citations
14.
Li, Peter H., Greg D. Field, Martin Greschner, et al.. (2014). Retinal Representation of the Elementary Visual Signal. Neuron. 82(2). 500–500. 2 indexed citations
15.
Greschner, Martin, et al.. (2013). Toward a complete functional classification of ganglion cells in the rat retina. Investigative Ophthalmology & Visual Science. 54(15). 3387–3387. 1 indexed citations
16.
Chatterjee, S., et al.. (2010). Advances in color science: From retina to behavior (The Journal of Neuroscience (2010) (14955-14963)). Journal of Neuroscience. 30(49). 3 indexed citations
17.
Okawa, Haruhisa, Kiyoharu J. Miyagishima, A. Cyrus Arman, et al.. (2010). Optimal processing of photoreceptor signals is required to maximize behavioural sensitivity. The Journal of Physiology. 588(11). 1947–1960. 34 indexed citations
18.
Field, Greg D., Martin Greschner, Jeffrey L. Gauthier, et al.. (2009). High-sensitivity rod photoreceptor input to the blue-yellow color opponent pathway in macaque retina. Nature Neuroscience. 12(9). 1159–1164. 86 indexed citations
19.
Sher, Alexander, Jeffrey L. Gauthier, Greg D. Field, et al.. (2009). Functional Identification of Individual Cones in the Receptive Fields of Primate Retinal Ganglion Cells. Investigative Ophthalmology & Visual Science. 50(13). 6150–6150. 1 indexed citations
20.
Field, Greg D., Jeffrey L. Gauthier, Martin Greschner, et al.. (2008). Light Adaptation Changes the Size of Receptive Fields in Seven Distinct Primate Retinal Ganglion Cell Types. Investigative Ophthalmology & Visual Science. 49(13). 3856–3856. 1 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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