Moshe Parnas

936 total citations
21 papers, 634 citations indexed

About

Moshe Parnas is a scholar working on Cellular and Molecular Neuroscience, Ecology, Evolution, Behavior and Systematics and Genetics. According to data from OpenAlex, Moshe Parnas has authored 21 papers receiving a total of 634 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Cellular and Molecular Neuroscience, 8 papers in Ecology, Evolution, Behavior and Systematics and 8 papers in Genetics. Recurrent topics in Moshe Parnas's work include Neurobiology and Insect Physiology Research (16 papers), Insect and Arachnid Ecology and Behavior (8 papers) and Animal Behavior and Reproduction (7 papers). Moshe Parnas is often cited by papers focused on Neurobiology and Insect Physiology Research (16 papers), Insect and Arachnid Ecology and Behavior (8 papers) and Animal Behavior and Reproduction (7 papers). Moshe Parnas collaborates with scholars based in Israel, United States and Germany. Moshe Parnas's co-authors include Baruch Minke, Wolf Huetteroth, Maximilian Peters, Andrew C. Lin, Shaya Lev, Daniela Dadon, Gero Miesenböck, Inna Slutsky, Irena Vertkin and Ben Katz and has published in prestigious journals such as Nature Communications, Neuron and Journal of Neuroscience.

In The Last Decade

Moshe Parnas

21 papers receiving 621 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Moshe Parnas Israel 14 399 169 166 136 99 21 634
Changsoo Kim South Korea 12 541 1.4× 302 1.8× 251 1.5× 195 1.4× 141 1.4× 27 986
Andrew Bellemer United States 8 267 0.7× 83 0.5× 100 0.6× 67 0.5× 83 0.8× 8 400
Takayuki Watanabe Japan 20 411 1.0× 256 1.5× 41 0.2× 112 0.8× 147 1.5× 67 964
Takeshi Morita United States 10 247 0.6× 143 0.8× 206 1.2× 96 0.7× 109 1.1× 15 729
Anthi A. Apostolopoulou Germany 12 311 0.8× 83 0.5× 35 0.2× 152 1.1× 118 1.2× 15 574
De-Shou Cao United States 7 190 0.5× 109 0.6× 232 1.4× 74 0.5× 33 0.3× 11 492
Sylwester Chyb United States 14 801 2.0× 391 2.3× 357 2.2× 133 1.0× 288 2.9× 20 1.2k
Hung‐Tat Leung United States 10 300 0.8× 217 1.3× 66 0.4× 52 0.4× 53 0.5× 18 421
Guilin Ren China 13 191 0.5× 142 0.8× 46 0.3× 66 0.5× 87 0.9× 26 539

Countries citing papers authored by Moshe Parnas

Since Specialization
Citations

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

Fields of papers citing papers by Moshe Parnas

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Moshe Parnas

This figure shows the co-authorship network connecting the top 25 collaborators of Moshe Parnas. A scholar is included among the top collaborators of Moshe Parnas 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 Moshe Parnas. Moshe Parnas 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.
Chou, Ya-Hui, et al.. (2025). Nonlinear high-activity neuronal excitation enhances odor discrimination. Current Biology. 35(7). 1521–1538.e5. 1 indexed citations
2.
Deng, Patricia, et al.. (2024). A highly conserved A-to-I RNA editing event within the glutamate-gated chloride channel GluClα is necessary for olfactory-based behaviors in Drosophila. Science Advances. 10(36). eadi9101–eadi9101. 11 indexed citations
3.
Parnas, Moshe, et al.. (2024). Sensory encoding and memory in the mushroom body: signals, noise, and variability. Learning & Memory. 31(5). a053825–a053825. 3 indexed citations
4.
Ehmann, Nadine, et al.. (2023). Homeostatic synaptic plasticity rescues neural coding reliability. Nature Communications. 14(1). 2993–2993. 6 indexed citations
5.
Ehmann, Nadine, et al.. (2023). HOMEOSTATIC SYNAPTIC PLASTICITY RESCUES NEURAL CODING RELIABILITY. IBRO Neuroscience Reports. 15. S335–S336. 1 indexed citations
6.
Huetteroth, Wolf, et al.. (2022). Olfactory stimuli and moonwalker SEZ neurons can drive backward locomotion in Drosophila. Current Biology. 32(5). 1131–1149.e7. 11 indexed citations
7.
Hige, Toshihide, et al.. (2022). Lateral axonal modulation is required for stimulus-specific olfactory conditioning in Drosophila. Current Biology. 32(20). 4438–4450.e5. 23 indexed citations
8.
Ormerod, Kiel G., et al.. (2021). Glial ER and GAP junction mediated Ca2+ waves are crucial to maintain normal brain excitability. Glia. 70(1). 123–144. 18 indexed citations
9.
Ben-Chaim, Yair, et al.. (2021). GPCR voltage dependence controls neuronal plasticity and behavior. Nature Communications. 12(1). 7252–7252. 25 indexed citations
10.
Huetteroth, Wolf, et al.. (2020). Differential Role for a Defined Lateral Horn Neuron Subset in Naïve Odor Valence in Drosophila. Scientific Reports. 10(1). 6147–6147. 20 indexed citations
11.
Parnas, Moshe, et al.. (2019). Muscarinic Modulation of Antennal Lobe GABAergic Local Neurons Shapes Odor Coding and Behavior. Cell Reports. 29(10). 3253–3265.e4. 13 indexed citations
12.
Apostolopoulou, Anthi A., et al.. (2019). Inhibitory muscarinic acetylcholine receptors enhance aversive olfactory learning in adult Drosophila. eLife. 8. 34 indexed citations
13.
Kain, David, et al.. (2018). PySight: plug and play photon counting for fast continuous volumetric intravital microscopy. Optica. 5(9). 1104–1104. 11 indexed citations
14.
Shmueli, Anat, et al.. (2018). Odorant binding protein 69a connects social interaction to modulation of social responsiveness in Drosophila. PLoS Genetics. 14(4). e1007328–e1007328. 42 indexed citations
15.
Parnas, Moshe, Andrew C. Lin, Wolf Huetteroth, & Gero Miesenböck. (2013). Odor Discrimination in Drosophila: From Neural Population Codes to Behavior. Neuron. 79(5). 932–944. 91 indexed citations
16.
Peters, Maximilian, Victoria Trembovler, Alexander Alexandrovich, et al.. (2012). Carvacrol Together with TRPC1 Elimination Improve Functional Recovery after Traumatic Brain Injury in Mice. Journal of Neurotrauma. 29(18). 2831–2834. 25 indexed citations
17.
Parnas, Moshe, Maximilian Peters, & Baruch Minke. (2009). Linoleic acid inhibits TRP channels with intrinsic voltage sensitivity: Implications on the mechanism of linoleic acid action. Channels. 3(3). 164–166. 18 indexed citations
18.
Parnas, Moshe, Maximilian Peters, Daniela Dadon, et al.. (2009). Carvacrol is a novel inhibitor of Drosophila TRPL and mammalian TRPM7 channels. Cell Calcium. 45(3). 300–309. 141 indexed citations
19.
Parnas, Moshe, Ben Katz, Shaya Lev, et al.. (2009). Membrane Lipid Modulations Remove Divalent Open Channel Block from TRP-Like and NMDA Channels. Journal of Neuroscience. 29(8). 2371–2383. 48 indexed citations
20.
Parnas, Moshe, Ben Katz, & Baruch Minke. (2006). Open Channel Block by Ca2+ Underlies the Voltage Dependence of Drosophila TRPL Channel. The Journal of General Physiology. 129(1). 17–28. 31 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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