David J. Margolis

2.3k total citations
38 papers, 1.4k citations indexed

About

David J. Margolis is a scholar working on Cellular and Molecular Neuroscience, Cognitive Neuroscience and Molecular Biology. According to data from OpenAlex, David J. Margolis has authored 38 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Cellular and Molecular Neuroscience, 23 papers in Cognitive Neuroscience and 8 papers in Molecular Biology. Recurrent topics in David J. Margolis's work include Neural dynamics and brain function (20 papers), Neuroscience and Neuropharmacology Research (13 papers) and Photoreceptor and optogenetics research (11 papers). David J. Margolis is often cited by papers focused on Neural dynamics and brain function (20 papers), Neuroscience and Neuropharmacology Research (13 papers) and Photoreceptor and optogenetics research (11 papers). David J. Margolis collaborates with scholars based in United States, Switzerland and Netherlands. David J. Margolis's co-authors include Peter B. Detwiler, Fritjof Helmchen, Christian Lee, Henry Lütcke, Thomas Euler, Yu Young Jeong, Qian Cai, Jerry L. Chen, Lazar T. Sumanovski and Laleh Najafizadeh and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Neuroscience and Nature Neuroscience.

In The Last Decade

David J. Margolis

35 papers receiving 1.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
David J. Margolis United States 18 577 412 386 200 135 38 1.4k
Quentin Barraud Switzerland 15 679 1.2× 426 1.0× 305 0.8× 324 1.6× 153 1.1× 21 2.0k
Krystel R. Huxlin United States 29 348 0.6× 543 1.3× 896 2.3× 231 1.2× 75 0.6× 132 2.3k
Alex G. Lee United States 18 510 0.9× 481 1.2× 251 0.7× 166 0.8× 30 0.2× 38 1.9k
Noam Y. Harel United States 18 416 0.7× 282 0.7× 72 0.2× 163 0.8× 195 1.4× 57 1.4k
Michelle L. Starkey Switzerland 19 483 0.8× 181 0.4× 114 0.3× 279 1.4× 342 2.5× 26 1.3k
Natalia Lago Spain 22 1.4k 2.4× 321 0.8× 519 1.3× 273 1.4× 33 0.2× 38 2.0k
Diana Wagner United States 19 634 1.1× 533 1.3× 75 0.2× 204 1.0× 317 2.3× 25 2.4k
Tsutomu Soma Japan 20 309 0.5× 328 0.8× 189 0.5× 62 0.3× 105 0.8× 47 1.8k
Mario Buffelli Italy 22 688 1.2× 723 1.8× 167 0.4× 299 1.5× 28 0.2× 59 1.5k
Grant A. Robinson United States 23 696 1.2× 362 0.9× 328 0.8× 128 0.6× 44 0.3× 35 1.5k

Countries citing papers authored by David J. Margolis

Since Specialization
Citations

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

Fields of papers citing papers by David J. Margolis

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David J. Margolis. 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 David J. Margolis. The network helps show where David J. Margolis may publish in the future.

Co-authorship network of co-authors of David J. Margolis

This figure shows the co-authorship network connecting the top 25 collaborators of David J. Margolis. A scholar is included among the top collaborators of David J. Margolis 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 David J. Margolis. David J. Margolis 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.
2.
Fang, Jennifer L., et al.. (2024). Imaging the large‐scale and cellular response to focal traumatic brain injury in mouse neocortex. Experimental Physiology. 110(2). 321–344. 2 indexed citations
3.
Abraira, Victoria E., et al.. (2023). Cell-Type Specific Connectivity of Whisker-Related Sensory and Motor Cortical Input to Dorsal Striatum. eNeuro. 11(1). ENEURO.0503–23.2023. 3 indexed citations
4.
Boreland, Andrew J., et al.. (2021). Fabrication of a Multilayer Implantable Cortical Microelectrode Probe to Improve Recording Potential. Journal of Microelectromechanical Systems. 30(4). 569–581. 7 indexed citations
5.
Lee, Christian, Laleh Najafizadeh, & David J. Margolis. (2020). Investigating learning-related neural circuitry with chronic in vivo optical imaging. Brain Structure and Function. 225(2). 467–480. 8 indexed citations
6.
Swerdel, Mavis R., et al.. (2019). Optogenetic and transcriptomic interrogation of enhanced muscle function in the paralyzed mouse whisker pad. Journal of Neurophysiology. 121(4). 1491–1500. 7 indexed citations
7.
Lee, Christian, et al.. (2019). Opposing Influence of Sensory and Motor Cortical Input on Striatal Circuitry and Choice Behavior. Current Biology. 29(8). 1313–1323.e5. 17 indexed citations
8.
Eyo, Ukpong B., Mingshu Mo, Min‐Hee Yi, et al.. (2018). P2Y12R-Dependent Translocation Mechanisms Gate the Changing Microglial Landscape. Cell Reports. 23(4). 959–966. 86 indexed citations
9.
Zhu, Li, Christian Lee, David J. Margolis, & Laleh Najafizadeh. (2018). Decoding cortical brain states from widefield calcium imaging data using visibility graph. Biomedical Optics Express. 9(7). 3017–3017. 25 indexed citations
10.
Ye, Xuan, Tuancheng Feng, Prasad Tammineni, et al.. (2017). Regulation of Synaptic Amyloid-β Generation through BACE1 Retrograde Transport in a Mouse Model of Alzheimer's Disease. Journal of Neuroscience. 37(10). 2639–2655. 54 indexed citations
11.
Lee, Christian & David J. Margolis. (2016). Pupil Dynamics Reflect Behavioral Choice and Learning in a Go/NoGo Tactile Decision-Making Task in Mice. Frontiers in Behavioral Neuroscience. 10. 200–200. 32 indexed citations
12.
Chen, Jerry L., et al.. (2015). Pathway-specific reorganization of projection neurons in somatosensory cortex during learning. Nature Neuroscience. 18(8). 1101–1108. 110 indexed citations
13.
Sargen, Michael R., Ole Hoffstad, & David J. Margolis. (2014). Warm, Humid, and High Sun Exposure Climates Are Associated with Poorly Controlled Eczema: PEER (Pediatric Eczema Elective Registry) Cohort, 2004–2012. Journal of Investigative Dermatology. 134(6). 1779–1779.
14.
Margolis, David J., et al.. (2014). Network Oscillations Drive Correlated Spiking of ON and OFF Ganglion Cells in the rd1 Mouse Model of Retinal Degeneration. PLoS ONE. 9(1). e86253–e86253. 52 indexed citations
15.
Margolis, David J., Henry Lütcke, & Fritjof Helmchen. (2013). Microcircuit dynamics of map plasticity in barrel cortex. Current Opinion in Neurobiology. 24(1). 76–81. 22 indexed citations
16.
Chen, Jerry L., et al.. (2013). Online correction of licking‐induced brain motion during two‐photon imaging with a tunable lens. The Journal of Physiology. 591(19). 4689–4698. 39 indexed citations
17.
Lütcke, Henry, David J. Margolis, & Fritjof Helmchen. (2013). Steady or changing? Long-term monitoring of neuronal population activity. Trends in Neurosciences. 36(7). 375–384. 71 indexed citations
18.
Minderer, Matthias, Wenrui Liu, Lazar T. Sumanovski, et al.. (2011). Chronic imaging of cortical sensory map dynamics using a genetically encoded calcium indicator. The Journal of Physiology. 590(1). 99–107. 35 indexed citations
19.
Margolis, David J., et al.. (2010). Dendritic Calcium Signaling in ON and OFF Mouse Retinal Ganglion Cells. Journal of Neuroscience. 30(21). 7127–7138. 49 indexed citations
20.
Euler, Thomas, et al.. (2008). Eyecup scope—optical recordings of light stimulus-evoked fluorescence signals in the retina. Pflügers Archiv - European Journal of Physiology. 457(6). 1393–1414. 110 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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