World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
37
Citations
7549
World Ranking
8278
National Ranking
277

Overview

J. Leo van Hemmen is affiliated with the Technical University of Munich in Germany. Their research spans multiple disciplines including Engineering, Computer Science, and Physics and Astronomy. The scientist's work also touches on specialized subfields such as Otorhinolaryngology, Biomaterials, Biomedical Engineering, Global and Planetary Change, and Computer Networks and Communications.

The primary research topics covered by J. Leo van Hemmen include:

  • Ear Surgery and Otitis Media
  • Silk-based biomaterials and applications
  • Ecosystem dynamics and resilience
  • Nonlinear Dynamics and Pattern Formation
  • Mechanical and Optical Resonators
  • Neural dynamics and brain function
  • Advanced Sensor and Energy Harvesting Materials

The scientist's recent publications reflect their interdisciplinary approach. Notable papers include:

  • "Mathematization of nature: how it is done" (2021) published in Biological Cybernetics
  • "Geometric perturbation theory and Acoustic Boundary Condition Dynamics" (2020) published in Physica D Nonlinear Phenomena
  • "Tone generation in an open-end organ pipe: How a resonating sphere of air stops the pipe" (2022) published on arXiv (Cornell University)

Frequent collaborators in their research endeavors are:

  • David T. Heider
  • Bernhardt H. Edskes
  • J.L. van Leeuwen
  • Bernhard U. Seeber
  • Nicolas Brunel

Common venues where J. Leo van Hemmen publishes include:

  • Biological Cybernetics
  • Physica D Nonlinear Phenomena
  • arXiv (Cornell University)

Best Publications

  • A neuronal learning rule for sub-millisecond temporal coding

    Wulfram Gerstner;Wulfram Gerstner;Richard Kempter;J. Leo van Hemmen;Hermann Wagner;Hermann Wagner

  • Hebbian learning and spiking neurons

    Richard Kempter;Wulfram Gerstner;J. Leo van Hemmen

  • Reduction of the hodgkin-huxley equations to a single-variable threshold model

    Werner M. Kistler;Wulfram Gerstner;J. Leo van Hemmen

  • Why spikes? Hebbian learning and retrieval of time-resolved excitation patterns

    Wulfram Gerstner;Raphael Ritz;J. Leo Hemmen

  • What matters in neuronal locking

    Wulfram Gerstner;J. Leo van Hemmen;Jack D. Cowan

  • Intrinsic Stabilization of Output Rates by Spike-Based Hebbian Learning

    Richard Kempter;Wulfram Gerstner;J. Leo Van Hemmen

  • Modeling Synaptic Plasticity in Conjunction with the Timing of Pre- and Postsynaptic Action Potentials

    Werner M. Kistler;J. Leo Van Hemmen

  • Coherence and incoherence in a globally coupled ensemble of pulse-emitting units

    W. Gerstner;J. L. van Hemmen

  • A biologically motivated and analytically soluble model of collective oscillations in the cortex

    Wulfram Gerstner;Raphael Ritz;J. Leo Hemmen

  • Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks IV: Structuring synaptic pathways among recurrent connections

    Matthieu Gilson;Anthony N. Burkitt;David B. Grayden;Doreen A. Thomas

  • Cytokine-modulated regulation of helper T cell populations.

    Andrew Yates;Claudia Bergmann;J Leo Van Hemmen;Jaroslav Stark

  • Object localization through the lateral line system of fish: theory and experiment

    Julie Goulet;Jacob Engelmann;Boris P. Chagnaud;Jan-Moritz P. Franosch

  • Accelerating backpropagation through dynamic self-adaptation

    Ralf Salomon;J. Leo van Hemmen

  • Extracting Oscillations: Neuronal Coincidence Detection with Noisy Periodic Spike Input

    Richard Kempter;Wulfram Gerstner;J. Leo Van Hemmen;Hermann Wagner

  • Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks. I. Input selectivity–strengthening correlated input pathways

    Matthieu Gilson;Anthony N. Burkitt;David B. Grayden;Doreen A. Thomas

  • Temporal association

    Reimer Kühn;J. Leo van Hemmen

  • STDP in Recurrent Neuronal Networks.

    Matthieu Gilson;Matthieu Gilson;Anthony N. Burkitt;J. Leo van Hemmen

  • Vibration Sensitivity and a Computational Theory for Prey-Localizing Behavior in Sand Scorpions

    Philip H. Brownell;J. Leo van Hemmen

  • Formation of temporal-feature maps by axonal propagation of synaptic learning.

    Richard Kempter;Christian Leibold;Hermann Wagner;J. Leo van Hemmen

  • A biologically motivated and analytically soluble model of collective oscillations in the cortex: II.~Application to binding and pattern segmentation

    R. Ritz;W. Gerstner;U. Fuentes;J. L. Van Hemmen

  • Models of Neural Networks I

    Eytan Domany;J. Leo Hemmen;Klaus Schulten

  • Models of Neural Networks III

    Eytan Domany;J. Leo Hemmen;Klaus Schulten

Frequent Co-Authors

Wulfram Gerstner
Wulfram Gerstner École Polytechnique Fédérale de Lausanne
Anthony N. Burkitt
Anthony N. Burkitt University of Melbourne
Richard Kempter
Richard Kempter Humboldt-Universität zu Berlin
David B. Grayden
David B. Grayden University of Melbourne
Sandra Hirche
Sandra Hirche Technical University of Munich
Klaus Schulten
Klaus Schulten University of Illinois at Urbana-Champaign
Avidan U. Neumann
Avidan U. Neumann Bar-Ilan University
Andrew B. Schwartz
Andrew B. Schwartz University of Pittsburgh
Barry E. Stein
Barry E. Stein Wake Forest University
Andreas R. Bausch
Andreas R. Bausch Technical University of Munich

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