Connon I. Thomas

593 total citations
21 papers, 330 citations indexed

About

Connon I. Thomas is a scholar working on Cellular and Molecular Neuroscience, Molecular Biology and Cognitive Neuroscience. According to data from OpenAlex, Connon I. Thomas has authored 21 papers receiving a total of 330 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Cellular and Molecular Neuroscience, 10 papers in Molecular Biology and 10 papers in Cognitive Neuroscience. Recurrent topics in Connon I. Thomas's work include Neuroscience and Neuropharmacology Research (11 papers), Neural dynamics and brain function (10 papers) and Neuroscience and Neural Engineering (6 papers). Connon I. Thomas is often cited by papers focused on Neuroscience and Neuropharmacology Research (11 papers), Neural dynamics and brain function (10 papers) and Neuroscience and Neural Engineering (6 papers). Connon I. Thomas collaborates with scholars based in United States, Japan and Germany. Connon I. Thomas's co-authors include Naomi Kamasawa, Debbie Guerrero‐Given, Benjamin Scholl, Samuel Young, David Fitzpatrick, Christian Keine, Rachel Satterfield, R. Oliver Goral, Mónica S. Montesinos and Wei Dong and has published in prestigious journals such as Nature, Neuron and Journal of Neuroscience.

In The Last Decade

Connon I. Thomas

19 papers receiving 330 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Connon I. Thomas United States 10 213 180 100 60 25 21 330
Gregor F. P. Schuhknecht Switzerland 5 191 0.9× 161 0.9× 106 1.1× 77 1.3× 22 0.9× 6 385
Miklos Szoboszlay United States 9 264 1.2× 134 0.7× 157 1.6× 49 0.8× 6 0.2× 12 336
Rachel Satterfield United States 11 237 1.1× 253 1.4× 55 0.6× 118 2.0× 31 1.2× 12 381
Julia Ledderose Germany 12 236 1.1× 185 1.0× 182 1.8× 42 0.7× 45 1.8× 17 492
Brian K. Hoffpauir United States 8 222 1.0× 177 1.0× 98 1.0× 37 0.6× 17 0.7× 10 392
Jary Y. Delgado United States 10 273 1.3× 236 1.3× 86 0.9× 54 0.9× 44 1.8× 14 408
Katalin Czöndör France 8 250 1.2× 206 1.1× 45 0.5× 90 1.5× 19 0.8× 10 359
Andreas T. Grasskamp Germany 7 218 1.0× 242 1.3× 26 0.3× 175 2.9× 16 0.6× 16 416
Grit Bornschein Germany 9 227 1.1× 169 0.9× 55 0.6× 73 1.2× 4 0.2× 13 284
Qiyu Zhang United States 8 110 0.5× 86 0.5× 71 0.7× 29 0.5× 12 0.5× 11 269

Countries citing papers authored by Connon I. Thomas

Since Specialization
Citations

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

Fields of papers citing papers by Connon I. Thomas

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Connon I. Thomas. 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 Connon I. Thomas. The network helps show where Connon I. Thomas may publish in the future.

Co-authorship network of co-authors of Connon I. Thomas

This figure shows the co-authorship network connecting the top 25 collaborators of Connon I. Thomas. A scholar is included among the top collaborators of Connon I. Thomas 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 Connon I. Thomas. Connon I. Thomas 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.
Thomas, Connon I., et al.. (2025). Presynaptic α2δs specify synaptic gain, not synaptogenesis, in the mammalian brain. Neuron. 113(12). 1886–1897.e9.
2.
3.
Thomas, Connon I., et al.. (2023). Postsynaptic mitochondria are positioned to support functional diversity of dendritic spines. eLife. 12. 6 indexed citations
4.
Thomas, Connon I., et al.. (2023). Postsynaptic mitochondria are positioned to support functional diversity of dendritic spines. eLife. 12. 14 indexed citations
5.
Scholl, Benjamin, et al.. (2022). A binocular synaptic network supports interocular response alignment in visual cortical neurons. Neuron. 110(9). 1573–1584.e4. 3 indexed citations
6.
Keine, Christian, Connon I. Thomas, Debbie Guerrero‐Given, et al.. (2022). Presynaptic Rac1 controls synaptic strength through the regulation of synaptic vesicle priming. eLife. 11. 13 indexed citations
7.
Guerrero‐Given, Debbie, et al.. (2022). Gold In-and-Out: A Toolkit for Analyzing Subcellular Distribution of Immunogold-Labeled Membrane Proteins in Freeze-Fracture Replica Images. Frontiers in Neuroanatomy. 16. 855218–855218. 7 indexed citations
8.
Hayano, Yasufumi, Jung Ho Hyun, André Steinecke, et al.. (2021). IgSF11 homophilic adhesion proteins promote layer-specific synaptic assembly of the cortical interneuron subtype. Science Advances. 7(29). 15 indexed citations
9.
Scholl, Benjamin, et al.. (2021). Author Correction: Cortical response selectivity derives from strength in numbers of synapses. Nature. 590(7846). E51–E51. 2 indexed citations
10.
Stawarski, Michał, et al.. (2021). Computational modeling predicts ephemeral acidic microdomains in the glutamatergic synaptic cleft. Biophysical Journal. 120(24). 5575–5591. 7 indexed citations
11.
Dong, Wei, R. Oliver Goral, Connon I. Thomas, et al.. (2020). Presynaptic development is controlled by the core active zone proteins CAST/ELKS. The Journal of Physiology. 598(12). 2431–2452. 20 indexed citations
12.
Scholl, Benjamin, et al.. (2020). Cortical response selectivity derives from strength in numbers of synapses. Nature. 590(7844). 111–114. 58 indexed citations
13.
Thomas, Connon I., et al.. (2020). Targeting Functionally Characterized Synaptic Architecture Using Inherent Fiducials and 3D Correlative Microscopy. Microscopy and Microanalysis. 27(1). 156–169. 16 indexed citations
14.
Thomas, Connon I., Christian Keine, Rachel Satterfield, et al.. (2019). Presynaptic Mitochondria Volume and Abundance Increase during Development of a High-Fidelity Synapse. Journal of Neuroscience. 39(41). 7994–8012. 43 indexed citations
15.
Thomas, Connon I. & Naomi Kamasawa. (2019). Processing Volumetric Data for Correlative Analysis: An Anecdote from a Core Facility. Microscopy and Microanalysis. 25(S2). 1382–1383.
16.
Kamasawa, Naomi, et al.. (2019). Synapse to Circuit - Correlative Microscopy Workflows for Functional Analysis of the Brain. Microscopy and Microanalysis. 25(S2). 1022–1023. 1 indexed citations
17.
Dong, Wei, R. Oliver Goral, Connon I. Thomas, et al.. (2018). CAST/ELKS Proteins Control Voltage-Gated Ca2+ Channel Density and Synaptic Release Probability at a Mammalian Central Synapse. Cell Reports. 24(2). 284–293.e6. 49 indexed citations
18.
Phan, Anna, Connon I. Thomas, Molee Chakraborty, et al.. (2018). Stromalin Constrains Memory Acquisition by Developmentally Limiting Synaptic Vesicle Pool Size. Neuron. 101(1). 103–118.e5. 8 indexed citations
19.
Goral, R. Oliver, Christian Keine, Connon I. Thomas, et al.. (2018). CaV2.1 α1 Subunit Expression Regulates Presynaptic CaV2.1 Abundance and Synaptic Strength at a Central Synapse. Neuron. 101(2). 260–273.e6. 45 indexed citations
20.
Thomas, Connon I., Naomi Kamasawa, Christian Keine, et al.. (2018). Light and Dark: Fluorescent and Electron Dense Labeling for Neuronal Cells Using a Novel Viral Vector. Microscopy and Microanalysis. 24(S1). 1352–1353. 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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