H. Parnas

4.6k total citations
118 papers, 3.8k citations indexed

About

H. Parnas is a scholar working on Cellular and Molecular Neuroscience, Molecular Biology and Cognitive Neuroscience. According to data from OpenAlex, H. Parnas has authored 118 papers receiving a total of 3.8k indexed citations (citations by other indexed papers that have themselves been cited), including 82 papers in Cellular and Molecular Neuroscience, 75 papers in Molecular Biology and 16 papers in Cognitive Neuroscience. Recurrent topics in H. Parnas's work include Ion channel regulation and function (45 papers), Neuroscience and Neural Engineering (41 papers) and Neuroscience and Neuropharmacology Research (38 papers). H. Parnas is often cited by papers focused on Ion channel regulation and function (45 papers), Neuroscience and Neural Engineering (41 papers) and Neuroscience and Neuropharmacology Research (38 papers). H. Parnas collaborates with scholars based in Israel, United States and Germany. H. Parnas's co-authors include I. Parnas, J. Dudél, Lee A. Segel, Barbara Attardi, Giuseppe Attardi, Yair Ben-Chaim, Christian Franke, Inna Slutsky, Nathan Dascal and Binyamin Hochner and has published in prestigious journals such as Nature, Proceedings of the National Academy of Sciences and Journal of Biological Chemistry.

In The Last Decade

H. Parnas

118 papers receiving 3.6k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
H. Parnas Israel 37 2.4k 2.3k 444 389 224 118 3.8k
Martin A. Smith United States 41 4.9k 2.0× 1.4k 0.6× 205 0.5× 427 1.1× 222 1.0× 103 7.0k
Eduardo Rojas United States 31 2.8k 1.2× 1.7k 0.7× 125 0.3× 456 1.2× 75 0.3× 92 4.3k
Yasuhiro Morita Japan 40 1.2k 0.5× 1.3k 0.6× 167 0.4× 371 1.0× 263 1.2× 190 4.4k
Koji Ohira Japan 36 1.7k 0.7× 1.1k 0.5× 423 1.0× 161 0.4× 55 0.2× 118 4.4k
Keiichi Inoue Japan 38 2.4k 1.0× 3.0k 1.3× 429 1.0× 152 0.4× 181 0.8× 166 4.6k
Karl‐Heinz Smalla Germany 33 1.9k 0.8× 1.7k 0.7× 336 0.8× 802 2.1× 121 0.5× 74 3.4k
Katsumi Doi Japan 37 2.1k 0.9× 860 0.4× 450 1.0× 132 0.3× 243 1.1× 217 4.8k
Thomas Voigt United States 35 1.4k 0.6× 1.8k 0.8× 810 1.8× 141 0.4× 127 0.6× 112 4.2k
Xin Zheng China 27 1.6k 0.7× 1.1k 0.5× 183 0.4× 104 0.3× 86 0.4× 75 2.8k
Tohru Yoshioka Japan 35 1.7k 0.7× 1.7k 0.7× 419 0.9× 472 1.2× 73 0.3× 123 3.6k

Countries citing papers authored by H. Parnas

Since Specialization
Citations

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

Fields of papers citing papers by H. Parnas

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of H. Parnas

This figure shows the co-authorship network connecting the top 25 collaborators of H. Parnas. A scholar is included among the top collaborators of H. 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 H. Parnas. H. 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.
Berlin, Shai, et al.. (2020). A Collision Coupling Model Governs the Activation of Neuronal GIRK1/2 Channels by Muscarinic-2 Receptors. Frontiers in Pharmacology. 11. 1216–1216. 5 indexed citations
2.
Ben-Chaim, Yair, et al.. (2019). The coupling of the M2 muscarinic receptor to its G protein is voltage dependent. PLoS ONE. 14(10). e0224367–e0224367. 10 indexed citations
3.
Priest, Michael F., et al.. (2016). A Novel Voltage Sensor in the Orthosteric Binding Site of the M2 Muscarinic Receptor. Biophysical Journal. 111(7). 1396–1408. 35 indexed citations
4.
Ben-Chaim, Yair, et al.. (2013). Voltage Affects the Dissociation Rate Constant of the m2 Muscarinic Receptor. PLoS ONE. 8(9). e74354–e74354. 26 indexed citations
5.
Priest, Michael F., et al.. (2011). Depolarization induces a conformational change in the binding site region of the M 2 muscarinic receptor. Proceedings of the National Academy of Sciences. 109(1). 285–290. 33 indexed citations
6.
Parnas, I., et al.. (2006). Role of NSF in Neurotransmitter Release: A Peptide Microinjection Study at the Crayfish Neuromuscular Junction. Journal of Neurophysiology. 96(3). 1053–1060. 4 indexed citations
7.
Tzafriri, Abraham R., Michel Bercovier, & H. Parnas. (2002). Reaction Diffusion Model of the Enzymatic Erosion of Insoluble Fibrillar Matrices. Biophysical Journal. 83(2). 776–793. 36 indexed citations
8.
Ратнер, Е. И., Oded Tour, & H. Parnas. (2000). Evaluation of the Number of Agonist Molecules Needed to Activate a Ligand-Gated Channel from the Current Rising Phase. Biophysical Journal. 78(2). 731–745. 2 indexed citations
9.
Levi, Rafael, et al.. (1999). Right-left discrimination in a biologically oriented model of the cockroach escape system. Biological Cybernetics. 81(2). 89–99. 5 indexed citations
10.
Tour, Oded, H. Parnas, & I. Parnas. (1998). Depolarization Increases the Single-Channel Conductance and the Open Probability of Crayfish Glutamate Channels. Biophysical Journal. 74(4). 1767–1778. 8 indexed citations
11.
Linial, Michal, Nili Ilouz, & H. Parnas. (1997). Voltage‐Dependent Interaction Between the Muscarinic ACh Receptor and Proteins of the Exocytic Machinery. The Journal of Physiology. 504(2). 251–258. 83 indexed citations
12.
Khanin, Raya, H. Parnas, & Lee A. Segel. (1997). “First Step” Negative Feedback Accounts for Inhibition of Fast Neurotransmitter Release. Journal of Theoretical Biology. 188(3). 261–276. 9 indexed citations
13.
Spira, Micha E., et al.. (1997). Simultaneous Measurement of Intracellular Ca2+ and Asynchronous Transmitter Release from the same Crayfish Bouton. The Journal of Physiology. 501(2). 251–262. 71 indexed citations
14.
Bufler, Johannes, Christian Franke, H. Parnas, & J. Dudél. (1996). Open Channel Block by Physostigmine and Procaine in Embryonic‐like Nicotinic Receptors of Mouse Muscle. European Journal of Neuroscience. 8(4). 677–687. 55 indexed citations
15.
Tour, Oded, H. Parnas, & I. Parnas. (1995). The Double‐ticker: An Improved Fast Drug‐application System Reveals Desensitization of the Glutamate Channel from a Closed State. European Journal of Neuroscience. 7(10). 2093–2100. 7 indexed citations
16.
Segel, Lee A., et al.. (1995). Modulated excitability: a new way to obtain bursting neurons. Biological Cybernetics. 72(5). 455–461. 3 indexed citations
17.
Parnas, H., et al.. (1992). Neurotransmitter release: Facilitation and three-dimensional diffusion of intracellular calcium. Bulletin of Mathematical Biology. 54(5). 875–894. 4 indexed citations
18.
Aumann, Yonatan & H. Parnas. (1991). Evaluation of the time course of neurotransmitter release from the measured PSC and MPSC. Bulletin of Mathematical Biology. 53(4). 537–555. 9 indexed citations
19.
Parnas, H., et al.. (1991). A minimal biophysical model for an excitable and oscillatory neuron. Biological Cybernetics. 65(6). 487–500. 36 indexed citations
20.
Hochner, Binyamin, H. Parnas, & I. Parnas. (1991). Effects of intra-axonal injection of Ca2+ buffers on evoked release and on facilitation in the crayfish neuromuscular junction. Neuroscience Letters. 125(2). 215–218. 43 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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