Volker Steuber

2.2k total citations
63 papers, 1.4k citations indexed

About

Volker Steuber is a scholar working on Cognitive Neuroscience, Cellular and Molecular Neuroscience and Neurology. According to data from OpenAlex, Volker Steuber has authored 63 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 43 papers in Cognitive Neuroscience, 27 papers in Cellular and Molecular Neuroscience and 22 papers in Neurology. Recurrent topics in Volker Steuber's work include Neural dynamics and brain function (39 papers), Vestibular and auditory disorders (22 papers) and Neuroscience and Neuropharmacology Research (20 papers). Volker Steuber is often cited by papers focused on Neural dynamics and brain function (39 papers), Vestibular and auditory disorders (22 papers) and Neuroscience and Neuropharmacology Research (20 papers). Volker Steuber collaborates with scholars based in United Kingdom, United States and Germany. Volker Steuber's co-authors include R. Angus Silver, Erik De Schutter, Padraig Gleeson, Jason S. Rothman, Laurence Cathala, Asheesh Bedi, Daniel Kendoff, Daniel Choi, Answorth A. Allen and David W. Altchek and has published in prestigious journals such as Nature, Neuron and SHILAP Revista de lepidopterología.

In The Last Decade

Volker Steuber

58 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
Volker Steuber United Kingdom 15 612 561 310 237 227 63 1.4k
David J. Margolis United States 18 386 0.6× 577 1.0× 200 0.6× 412 1.7× 64 0.3× 38 1.4k
Randall K. Powers United States 37 1.5k 2.5× 1.4k 2.5× 379 1.2× 406 1.7× 51 0.2× 77 3.0k
Saumil S. Patel United States 20 1.2k 1.9× 663 1.2× 128 0.4× 274 1.2× 16 0.1× 71 1.8k
H. Peter Clamann United States 21 1.0k 1.7× 662 1.2× 286 0.9× 179 0.8× 51 0.2× 38 2.0k
Rune W. Berg Denmark 21 1.2k 1.9× 1.0k 1.8× 129 0.4× 257 1.1× 23 0.1× 52 1.8k
Ranu Jung United States 18 404 0.7× 381 0.7× 106 0.3× 95 0.4× 53 0.2× 80 1.1k
Suzie C. Tindall United States 11 498 0.8× 627 1.1× 827 2.7× 322 1.4× 122 0.5× 18 2.6k
Donald R. Humphrey United States 14 1.4k 2.3× 679 1.2× 408 1.3× 91 0.4× 31 0.1× 20 2.0k
A. G. Brown United Kingdom 18 463 0.8× 942 1.7× 234 0.8× 341 1.4× 73 0.3× 40 1.8k
Byoung‐Kyong Min South Korea 22 811 1.3× 299 0.5× 283 0.9× 73 0.3× 28 0.1× 50 2.2k

Countries citing papers authored by Volker Steuber

Since Specialization
Citations

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

Fields of papers citing papers by Volker Steuber

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Volker Steuber

This figure shows the co-authorship network connecting the top 25 collaborators of Volker Steuber. A scholar is included among the top collaborators of Volker Steuber 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 Volker Steuber. Volker Steuber 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.
Steuber, Volker, et al.. (2025). Computational modelling of olfactory receptors. Biochimica et Biophysica Acta (BBA) - General Subjects. 1869(8). 130825–130825. 1 indexed citations
2.
Steuber, Volker, et al.. (2022). EEG Spectral Feature Modulations Associated With Fatigue in Robot-Mediated Upper Limb Gross and Fine Motor Interactions. Frontiers in Neurorobotics. 15. 788494–788494. 11 indexed citations
3.
Mäki‐Marttunen, Tuomo, et al.. (2022). The effect of alterations of schizophrenia-associated genes on gamma band oscillations. SHILAP Revista de lepidopterología. 8(1). 46–46. 8 indexed citations
4.
Davey, Neil, et al.. (2021). Growth rules for the repair of Asynchronous Irregular neuronal networks after peripheral lesions. PLoS Computational Biology. 17(6). e1008996–e1008996. 2 indexed citations
5.
Steuber, Volker, et al.. (2020). Adaptive robot mediated upper limb training using electromyogram-based muscle fatigue indicators. PLoS ONE. 15(5). e0233545–e0233545. 9 indexed citations
6.
Zurowski, Bartosz, et al.. (2019). The Role of Parvalbumin-positive Interneurons in Auditory Steady-State Response Deficits in Schizophrenia. Scientific Reports. 9(1). 18525–18525. 16 indexed citations
7.
Amirabdollahian, Farshid, et al.. (2017). Study of Gross Muscle Fatigue During Human-Robot Interactions. University of Hertfordshire Research Archive (University of Hertfordshire). 187–192. 6 indexed citations
8.
9.
Maex, Reinoud, et al.. (2017). Nonspecific synaptic plasticity improves the recognition of sparse patterns degraded by local noise. Scientific Reports. 7(1). 46550–46550. 4 indexed citations
10.
Rooda, Oscar H. J. Eelkman, Jochen K. Spanke, Else A. Tolner, et al.. (2015). Cerebellar output controls generalized spike‐and‐wave discharge occurrence. Annals of Neurology. 77(6). 1027–1049. 105 indexed citations
11.
Davey, Neil, et al.. (2014). Evolving spiking neural networks for temporal pattern recognition in the presence of noise. University of Hertfordshire Research Archive (University of Hertfordshire). 1 indexed citations
12.
Ovsepian, Saak V., Volker Steuber, Marie Le Berre, et al.. (2013). A defined heteromeric KV1 channel stabilizes the intrinsic pacemaking and regulates the output of deep cerebellar nuclear neurons to thalamic targets. The Journal of Physiology. 591(7). 1771–1791. 17 indexed citations
13.
Steuber, Volker & Dieter Jaeger. (2012). Modeling the generation of output by the cerebellar nuclei. Neural Networks. 47. 112–119. 23 indexed citations
14.
Hoebeek, Freek E., Reinoud Maex, Neil Davey, et al.. (2011). STD-Dependent and Independent Encoding of Input Irregularity as Spike Rate in a Computational Model of a Cerebellar Nucleus Neuron. The Cerebellum. 10(4). 667–682. 21 indexed citations
15.
Maex, Reinoud & Volker Steuber. (2009). The first second: Models of short-term memory traces in the brain. Neural Networks. 22(8). 1105–1112. 6 indexed citations
16.
Steuber, Volker, et al.. (2008). Optimal noise in spiking neural networks for the detection of chemicals by simulated agents. Artificial Life. 443–449. 1 indexed citations
17.
Gleeson, Padraig, Volker Steuber, & R. Angus Silver. (2007). neuroConstruct: A Tool for Modeling Networks of Neurons in 3D Space. Neuron. 54(2). 219–235. 133 indexed citations
18.
Steuber, Volker & David Willshaw. (2004). A Biophysical Model of Synaptic Delay Learning and Temporal Pattern Recognition in a Cerebellar Purkinje Cell. Journal of Computational Neuroscience. 17(2). 149–164. 29 indexed citations
19.
Steuber, Volker & Erik De Schutter. (2002). Rank order decoding of temporal parallel fibre input patterns in a complex Purkinje cell model. Neurocomputing. 44-46. 183–188. 5 indexed citations
20.
Hillenbrand, Rainer, Volker Steuber, Udo Bartsch, et al.. (1996). Structural Features of a Close Homologue of L1 (CHL1)in the Mouse: A New Member of the L1 Family of Neural Recognition Molecules. European Journal of Neuroscience. 8(8). 1613–1629. 103 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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