World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
40
Citations
18781
World Ranking
9020
National Ranking
448

Laurenz Wiskott publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Laurenz Wiskott sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 137 publications — 21st percentile

21% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Laurenz Wiskott D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Laurenz Wiskott sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 40 D-Index — 37th percentile

37% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Laurenz Wiskott is affiliated with Ruhr University Bochum in Germany. Their research spans multiple fields including Computer Science and Neuroscience, with a notable volume of publications in both areas.

The researcher has contributed significantly to subfields such as Artificial Intelligence, Cognitive Neuroscience, Computer Vision and Pattern Recognition, Cellular and Molecular Neuroscience, and various Computer Science Applications.

Main topics covered by their work include Reinforcement Learning in Robotics, Memory and Neural Mechanisms, Neural dynamics and brain function, Domain Adaptation and Few-Shot Learning, Neuroscience and Neuropharmacology Research, Explainable Artificial Intelligence (XAI), and Zebrafish Biomedical Research Applications.

Frequent co-authors in their collaborations include Sen Cheng, Raphael C. Engelhardt, Moritz Lange, Wolfgang Konen, and Robin Schiewer.

Their recent papers reflect a strong interest in memory, spatial learning, and computational neuroscience. Selected recent works include:

  • A Model of Semantic Completion in Generative Episodic Memory, 2022, Neural Computation
  • A map of spatial navigation for neuroscience, 2023, Neuroscience & Biobehavioral Reviews
  • Context-dependent extinction learning emerging from raw sensory inputs: a reinforcement learning approach, 2021, Scientific Reports
  • A Tutorial on the Spectral Theory of Markov Chains, 2023, Neural Computation
  • Modeling the function of episodic memory in spatial learning, 2023, Frontiers in Psychology

Common venues for publication include arXiv (Cornell University), Neural Computation, bioRxiv (Cold Spring Harbor Laboratory), Neuroscience & Biobehavioral Reviews, and Scientific Reports, indicating engagement with both preprint platforms and peer-reviewed journals.

Best Publications

  • Face recognition by elastic bunch graph matching

    L. Wiskott;J.-M. Fellous;N. Kuiger;C. von der Malsburg

  • Face recognition by elastic bunch graph matching

    L. Wiskott;J.-M. Fellous;N. Kruger;C. von der Malsburg

  • Face recognition by elastic bunch graph matching

    Laurenz Wiskott;Jean-Marc Fellous;Norbert Krüger;Christoph von der Malsburg

  • Slow feature analysis: unsupervised learning of invariances

    Laurenz Wiskott;Terrence J. Sejnowski

  • Deep Hierarchies in the Primate Visual Cortex: What Can We Learn for Computer Vision?

    N. Kruger;P. Janssen;S. Kalkan;M. Lappe

  • Slow feature analysis yields a rich repertoire of complex cell properties.

    Pietro Berkes;Laurenz Wiskott

  • Slowness and Sparseness Lead to Place, Head-Direction, and Spatial-View Cells

    Mathias Franzius;Henning Sprekeler;Laurenz Wiskott

  • Face Recognition and Gender determination

    Laurenz Wiskott;Jean-Marc Fellous;Norbert Krüger;Christoph von der Malsburg

  • Labeled bunch graphs for image analysis

    Laurenz Wiskott;Christoph von der Malsburg

  • Modular toolkit for Data Processing (MDP): a Python data processing framework

    Tiziano Zito;Niko Wilbert;Laurenz Wiskott;Pietro Berkes

  • Phantom faces for face analysis

    Laurenz Wiskott

  • Recognizing Faces by Dynamic Link Matching

    Laurenz Wiskott;Christoph von der Malsburg

  • CuBICA: independent component analysis by simultaneous third- and fourth-order cumulant diagonalization

    T. Blaschke;L. Wiskott

  • Reinforcement learning on slow features of high-dimensional input streams.

    Robert A. Legenstein;Niko Wilbert;Laurenz Wiskott

  • Slow feature analysis: a theoretical analysis of optimal free responses

    Laurenz Wiskott

  • Face recognition by dynamic link matching

    L. Wiskott

  • Slowness: an objective for spike-timing-dependent plasticity?

    Henning Sprekeler;Christian Michaelis;Laurenz Wiskott

  • Spatial representations of place cells in darkness are supported by path integration and border information

    Sijie Zhang;Fabian Schönfeld;Laurenz Wiskott;Denise Manahan-Vaughan

  • What Is the Relation Between Slow Feature Analysis and Independent Component Analysis

    Tobias Blaschke;Pietro Berkes;Laurenz Wiskott

  • A computational model for preplay in the hippocampus

    Amir Hossein Azizi;Laurenz Wiskott;Sen Cheng

  • From grids to places.

    Mathias Franzius;Roland Vollgraf;Laurenz Wiskott

Frequent Co-Authors

Christoph von der Malsburg
Christoph von der Malsburg Frankfurt Institute for Advanced Studies
Gerd Kempermann
Gerd Kempermann German Center for Neurodegenerative Diseases
Norbert Krüger
Norbert Krüger University of Southern Denmark
Jean-Marc Fellous
Jean-Marc Fellous University of Arizona
Robert Legenstein
Robert Legenstein Graz University of Technology
Denise Manahan-Vaughan
Denise Manahan-Vaughan Ruhr University Bochum
Terrence J. Sejnowski
Terrence J. Sejnowski Salk Institute for Biological Studies
Alessandro Treves
Alessandro Treves International School for Advanced Studies
Boris Gutkin
Boris Gutkin École Normale Supérieure
Simone Kühn
Simone Kühn Max Planck Institute for Human Development

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