Daniel E. Feldman

9.9k total citations · 4 hit papers
73 papers, 7.0k citations indexed

About

Daniel E. Feldman is a scholar working on Cellular and Molecular Neuroscience, Cognitive Neuroscience and Electrical and Electronic Engineering. According to data from OpenAlex, Daniel E. Feldman has authored 73 papers receiving a total of 7.0k indexed citations (citations by other indexed papers that have themselves been cited), including 58 papers in Cellular and Molecular Neuroscience, 56 papers in Cognitive Neuroscience and 18 papers in Electrical and Electronic Engineering. Recurrent topics in Daniel E. Feldman's work include Neural dynamics and brain function (51 papers), Neuroscience and Neuropharmacology Research (36 papers) and Neuroscience and Neural Engineering (21 papers). Daniel E. Feldman is often cited by papers focused on Neural dynamics and brain function (51 papers), Neuroscience and Neuropharmacology Research (36 papers) and Neuroscience and Neural Engineering (21 papers). Daniel E. Feldman collaborates with scholars based in United States, Germany and United Kingdom. Daniel E. Feldman's co-authors include Michael Brecht, Shantanu P. Jadhav, Daniel J. Brasier, Tansu Celikel, Kevin J. Bender, Eric I. Knudsen, Roger A. Nicoll, J. H. Wolfe, Robert C. Malenka and Matteo Carandini and has published in prestigious journals such as Science, Proceedings of the National Academy of Sciences and Nature Communications.

In The Last Decade

Daniel E. Feldman

70 papers receiving 6.8k citations

Hit Papers

The Spike-Timing Dependen... 2000 2026 2008 2017 2012 2009 2000 2019 200 400 600

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Daniel E. Feldman 4.8k 4.8k 1.1k 1.1k 599 73 7.0k
Robert C. Froemke 4.0k 0.8× 3.8k 0.8× 1.2k 1.1× 992 0.9× 457 0.8× 88 7.5k
Matthew E. Larkum 7.0k 1.5× 6.5k 1.4× 1.2k 1.1× 1.3k 1.2× 485 0.8× 104 9.2k
Gilad Silberberg 4.3k 0.9× 4.7k 1.0× 448 0.4× 1.8k 1.6× 512 0.9× 83 7.2k
Dominique Debanne 3.0k 0.6× 4.6k 1.0× 847 0.8× 1.8k 1.6× 536 0.9× 89 5.9k
Huizhong W. Tao 4.5k 0.9× 4.0k 0.8× 722 0.7× 1.1k 1.0× 340 0.6× 86 6.2k
Kevin Fox 4.2k 0.9× 5.3k 1.1× 485 0.4× 1.8k 1.6× 590 1.0× 93 6.9k
Carl C.H. Petersen 8.4k 1.8× 8.3k 1.7× 937 0.8× 2.0k 1.8× 589 1.0× 122 11.9k
Attila Losonczy 4.5k 0.9× 5.3k 1.1× 379 0.3× 1.3k 1.2× 852 1.4× 78 6.8k
Mark Hübener 4.0k 0.8× 4.4k 0.9× 363 0.3× 1.9k 1.8× 664 1.1× 72 6.9k

Countries citing papers authored by Daniel E. Feldman

Since Specialization
Citations

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

Fields of papers citing papers by Daniel E. Feldman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel E. Feldman. 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 Daniel E. Feldman. The network helps show where Daniel E. Feldman may publish in the future.

Co-authorship network of co-authors of Daniel E. Feldman

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel E. Feldman. A scholar is included among the top collaborators of Daniel E. Feldman 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 Daniel E. Feldman. Daniel E. Feldman 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.
Chen, A., et al.. (2025). Reward history guides focal attention in whisker somatosensory cortex. Nature Communications. 16(1). 5580–5580.
2.
Shamshiripour, Ali, Ravi Seshadri, Md Sami Hasnine, et al.. (2024). Potential short- to long-term impacts of on-demand urban air mobility on transportation demand in North America. Transportation Research Part A Policy and Practice. 190. 104288–104288. 1 indexed citations
3.
Monday, Hannah R., Han Chin Wang, & Daniel E. Feldman. (2023). Circuit-level theories for sensory dysfunction in autism: convergence across mouse models. Frontiers in Neurology. 14. 1254297–1254297. 11 indexed citations
4.
Isett, Brian R. & Daniel E. Feldman. (2020). Cortical Coding of Whisking Phase during Surface Whisking. Current Biology. 30(16). 3065–3074.e5. 6 indexed citations
5.
Ahn, Seoiyoung, et al.. (2019). A normalized template matching method for improving spike detection in extracellular voltage recordings. Scientific Reports. 9(1). 12087–12087. 29 indexed citations
6.
Antoine, Michelle, et al.. (2019). Increased Excitation-Inhibition Ratio Stabilizes Synapse and Circuit Excitability in Four Autism Mouse Models. Neuron. 101(4). 648–661.e4. 256 indexed citations breakdown →
7.
Feldman, Daniel E., et al.. (2018). Somatosensory maps. Handbook of clinical neurology. 151. 73–102. 38 indexed citations
8.
Feldman, Daniel E., Jorge Otero‐Millan, & Aasef G. Shaikh. (2018). Gravity-Independent Upbeat Nystagmus in Syndrome of Anti-GAD Antibodies. The Cerebellum. 18(2). 287–290. 8 indexed citations
9.
Keck, Tara, Taro Toyoizumi, Lu Chen, et al.. (2017). Integrating Hebbian and homeostatic plasticity: the current state of the field and future research directions. Philosophical Transactions of the Royal Society B Biological Sciences. 372(1715). 20160158–20160158. 130 indexed citations
10.
Clancy, Kelly B., et al.. (2015). Structure of a Single Whisker Representation in Layer 2 of Mouse Somatosensory Cortex. Journal of Neuroscience. 35(9). 3946–3958. 60 indexed citations
11.
Miyashita, Toshio, et al.. (2013). Long-term channelrhodopsin-2 (ChR2) expression can induce abnormal axonal morphology and targeting in cerebral cortex. Frontiers in Neural Circuits. 7. 8–8. 88 indexed citations
12.
Li, Lu, Kevin J. Bender, Patrick J. Drew, et al.. (2009). Endocannabinoid Signaling Is Required for Development and Critical Period Plasticity of the Whisker Map in Somatosensory Cortex. Neuron. 64(4). 537–549. 54 indexed citations
13.
Jadhav, Shantanu P., J. H. Wolfe, & Daniel E. Feldman. (2009). Sparse temporal coding of elementary tactile features during active whisker sensation. Nature Neuroscience. 12(6). 792–800. 179 indexed citations
14.
Wolfe, J. H., et al.. (2008). Texture Coding in the Rat Whisker System: Slip-Stick Versus Differential Resonance. PLoS Biology. 6(8). e215–e215. 174 indexed citations
15.
Bender, Kevin J., et al.. (2007). Postnatal development of cannabinoid receptor type 1 expression in rodent somatosensory cortex. Neuroscience. 145(1). 279–287. 31 indexed citations
16.
Feldman, Daniel E., et al.. (2006). A Dynamic Spatial Gradient of Hebbian Learning in Dendrites. Neuron. 51(2). 153–155. 6 indexed citations
17.
Jadhav, Shantanu P., et al.. (2005). Somatosensory Integration Controlled by Dynamic Thalamocortical Feed-Forward Inhibition. Neuron. 48(2). 315–327. 472 indexed citations
18.
Feldman, Daniel E., Roger A. Nicoll, Robert C. Malenka, & John Isaac. (1998). Long-Term Depression at Thalamocortical Synapses in Developing Rat Somatosensory Cortex. Neuron. 21(2). 347–357. 148 indexed citations
19.
Feldman, Daniel E. & Eric I. Knudsen. (1998). Experience-Dependent Plasticity and the Maturation of Glutamatergic Synapses. Neuron. 20(6). 1067–1071. 88 indexed citations
20.
Feldman, Daniel E., et al.. (1990). N-methyl-D-aspartate-evoked calcium uptake by kitten visual cortex maintained in vitro. Experimental Brain Research. 80(2). 252–9. 38 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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