Rich Pang

455 total citations
10 papers, 196 citations indexed

About

Rich Pang is a scholar working on Cellular and Molecular Neuroscience, Ecology, Evolution, Behavior and Systematics and Cognitive Neuroscience. According to data from OpenAlex, Rich Pang has authored 10 papers receiving a total of 196 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Cellular and Molecular Neuroscience, 5 papers in Ecology, Evolution, Behavior and Systematics and 5 papers in Cognitive Neuroscience. Recurrent topics in Rich Pang's work include Neural dynamics and brain function (5 papers), Neurobiology and Insect Physiology Research (5 papers) and Animal Behavior and Reproduction (4 papers). Rich Pang is often cited by papers focused on Neural dynamics and brain function (5 papers), Neurobiology and Insect Physiology Research (5 papers) and Animal Behavior and Reproduction (4 papers). Rich Pang collaborates with scholars based in United States, Germany and Switzerland. Rich Pang's co-authors include Adrienne L. Fairhall, Benjamin Lansdell, Aryeh Zolin, Raphael Cohn, Vanessa Ruta, Jeffrey A. Riffell, Michael H. Dickinson, Floris van Breugel, Diego A. Pacheco and Mala Murthy and has published in prestigious journals such as Nature, Proceedings of the National Academy of Sciences and Nature Neuroscience.

In The Last Decade

Rich Pang

8 papers receiving 195 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Rich Pang United States	7	126	75	55	44	36	10	196
Marcella Noorman United States	5	163 1.3×	52 0.7×	91 1.7×	51 1.2×	18 0.5×	9	236
Marisa Dreher United States	3	206 1.6×	56 0.7×	120 2.2×	79 1.8×	21 0.6×	4	258
Chao-Chun Chuang Taiwan	4	181 1.4×	112 1.5×	65 1.2×	64 1.5×	11 0.3×	6	259
Kunal Shah India	5	173 1.4×	73 1.0×	72 1.3×	55 1.3×	13 0.4×	9	311
Hannah Haberkern United States	7	238 1.9×	70 0.9×	127 2.3×	81 1.8×	31 0.9×	9	337
Stephen J Huston United States	7	200 1.6×	97 1.3×	59 1.1×	79 1.8×	24 0.7×	7	279
Benjamin D. Pedigo United States	5	85 0.7×	72 1.0×	39 0.7×	25 0.6×	10 0.3×	9	200
Laia Serratosa Capdevila United States	5	181 1.4×	37 0.5×	97 1.8×	63 1.4×	29 0.8×	7	213
Pablo S. Magani United States	2	173 1.4×	73 1.0×	73 1.3×	55 1.3×	13 0.4×	2	224
Ana Correia United Kingdom	4	80 0.6×	56 0.7×	34 0.6×	23 0.5×	10 0.3×	6	178

Countries citing papers authored by Rich Pang

Since Specialization

Citations

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

Fields of papers citing papers by Rich Pang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Rich Pang

This figure shows the co-authorship network connecting the top 25 collaborators of Rich Pang. A scholar is included among the top collaborators of Rich Pang 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 Rich Pang. Rich Pang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

10 of 10 papers shown

1.

Pang, Rich & Stefano Recanatesi. (2025). A non-Hebbian code for episodic memory. Science Advances. 11(8). eado4112–eado4112.

2.

Lynn, Christopher W., et al.. (2025). Exact minimax entropy models of large-scale neuronal activity. Physical review. E. 111(5). 54411–54411. 3 indexed citations

3.

Pang, Rich, Christa A. Baker, Mala Murthy, & Jonathan W. Pillow. (2025). Inferring neural population codes for Drosophila acoustic communication. Proceedings of the National Academy of Sciences. 122(21). e2417733122–e2417733122.

4.

Pacheco, Diego A., et al.. (2023). Flexible circuit mechanisms for context-dependent song sequencing. Nature. 622(7984). 794–801. 16 indexed citations

5.

Baker, Christa A., Claire McKellar, Rich Pang, et al.. (2022). Neural network organization for courtship-song feature detection in Drosophila. Current Biology. 32(15). 3317–3333.e7. 14 indexed citations

6.

Zolin, Aryeh, et al.. (2021). Context-dependent representations of movement in Drosophila dopaminergic reinforcement pathways. Nature Neuroscience. 24(11). 1555–1566. 47 indexed citations

7.

Pang, Rich & Adrienne L. Fairhall. (2019). Fast and flexible sequence induction in spiking neural networks via rapid excitability changes. eLife. 8. 8 indexed citations

8.

Pang, Rich, Floris van Breugel, Michael H. Dickinson, Jeffrey A. Riffell, & Adrienne L. Fairhall. (2018). History dependence in insect flight decisions during odor tracking. PLoS Computational Biology. 14(2). e1005969–e1005969. 37 indexed citations

9.

Pang, Rich, Benjamin Lansdell, & Adrienne L. Fairhall. (2016). Dimensionality reduction in neuroscience. Current Biology. 26(14). R656–R660. 52 indexed citations

10.

Pang, Rich, et al.. (2016). A simple generative model of the mouse mesoscale connectome. eLife. 5. e12366–e12366. 19 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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