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Computer Science

D-Index
51
Citations
10283
World Ranking
5346
National Ranking
248

Overview

Klaus Obermayer is affiliated with the Technical University of Berlin in Germany. Their research spans primarily the fields of Neuroscience and Computer Science, with a focus on several subfields including Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging, Cellular and Molecular Neuroscience, Artificial Intelligence, and Computer Networks and Communications.

The main topics of Klaus Obermayer's work include neural dynamics and brain function, functional brain connectivity studies, advanced neuroimaging techniques and applications, sleep and wakefulness research, stochastic dynamics and bifurcation, nonlinear dynamics and pattern formation, and EEG and brain-computer interfaces.

Several frequent coauthors collaborate with Obermayer, including Caglar Cakan, Cristiana Dimulescu, Agnes Flöel, Liliia Khakimova, and Christoph Metzner.

Obermayer's papers are often published in venues such as bioRxiv (Cold Spring Harbor Laboratory), Frontiers in Computational Neuroscience, PLoS Computational Biology, arXiv (Cornell University), and Frontiers in Neuroinformatics.

Recent significant publications include:

  • Biophysically grounded mean-field models of neural populations under electrical stimulation (2020, PLoS Computational Biology)
  • neurolib: A Simulation Framework for Whole-Brain Neural Mass Modeling (2021, Cognitive Computation)
  • Spatiotemporal Patterns of Adaptation-Induced Slow Oscillations in a Whole-Brain Model of Slow-Wave Sleep (2022, Frontiers in Computational Neuroscience)
  • Pairwise Synchrony and Correlations Depend on the Structure of the Population Code in Visual Cortex (2020, Cell Reports)
  • Brian2CUDA: Flexible and Efficient Simulation of Spiking Neural Network Models on GPUs (2022, Frontiers in Neuroinformatics)

Their research contributions focus on integrating computational modeling approaches with neurobiological data, emphasizing brain function dynamics, brain connectivity, and applications of neural simulation frameworks. Obermayer's work often addresses complex neural phenomena such as slow oscillations during sleep, population coding in visual cortex, and the computational efficiency of neural simulations.

Best Publications

  • Support vector learning for ordinal regression

    R. Herbrich;T. Graepel;K. Obermayer

  • Self-organizing maps: ordering, convergence properties and energy functions

    E. Erwin;K. Obermayer;K. Schulten

  • Gaussian process regression: active data selection and test point rejection

    Sambu Seo;M. Wallat;T. Graepel;K. Obermayer

  • Invariant computations in local cortical networks with balanced excitation and inhibition

    Jorge Mariño;James Schummers;David C Lyon;David C Lyon;Lars Schwabe

  • Models of Orientation and Ocular Dominance Columns in the Visual Cortex: A Critical Comparison

    E. Erwin;Klaus Obermayer;Klaus Schulten

  • A new summarization method for affymetrix probe level data

    Sepp Hochreiter;Djork-Arné Clevert;Klaus Obermayer

  • A principle for the formation of the spatial structure of cortical feature maps.

    Klaus Obermayer;Helge Ritter;Klaus Schulten

  • Statistical-mechanical analysis of self-organization and pattern formation during the development of visual maps

    K. Obermayer;G. G. Blasdel;K. Schulten

  • Soft learning vector quantization

    Sambu Seo;Klaus Obermayer

  • New methods for the computer-assisted 3-D reconstruction of neurons from confocal image stacks.

    Stephan Schmitt;Jan Felix Evers;Carsten Duch;Michael Scholz

  • Classification on Pairwise Proximity Data

    Thore Graepel;Ralf Herbrich;Peter Bollmann-Sdorra;Klaus Obermayer

  • Self-organizing maps: stationary states, metastability and convergence rate

    E. Erwin;K. Obermayer;K. Schulten

  • Self-organizing maps: Generalizations and new optimization techniques

    Thore Graepel;Matthias Burger;Klaus Obermayer

  • Fast model-based protein homology detection without alignment

    Sepp Hochreiter;Martin Heusel;Klaus Obermayer

  • An online spike detection and spike classification algorithm capable of instantaneous resolution of overlapping spikes

    Felix Franke;Michal Natora;Clemens Boucsein;Matthias H. Munk

  • Quadratic optimization for simultaneous matrix diagonalization

    R. Vollgraf;K. Obermayer

  • Learning Preference Relations for Information Retrieval

    Ralf Herbrich;Thore Graepel;Peter Bollmann-Sdorra;Klaus Obermayer

  • Soft nearest prototype classification

    S. Seo;M. Bode;K. Obermayer

  • Risk-sensitive reinforcement learning

    Yun Shen;Michael J. Tobia;Tobias Sommer;Klaus Obermayer

  • Classification on proximity data with LP-machines

    Thore Graepel;Ralf Herbrich;Bernhard Schölkopf;Alex Smola

Frequent Co-Authors

Helge Ritter
Helge Ritter Bielefeld University
Thore Graepel
Thore Graepel University College London
Sepp Hochreiter
Sepp Hochreiter Johannes Kepler University of Linz
Benjamin Blankertz
Benjamin Blankertz Technical University of Berlin
Eckart D. Gundelfinger
Eckart D. Gundelfinger Leibniz Institute for Neurobiology
Gaute T. Einevoll
Gaute T. Einevoll Norwegian University of Life Sciences
Ralf Herbrich
Ralf Herbrich Hasso Plattner Institute
John Rinzel
John Rinzel New York University
Tobias Sommer
Tobias Sommer Universität Hamburg

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