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Hans Ekkehard Plesser

Hans Ekkehard Plesser

Professor

  • Institutt for datavitenskap

As head of the Data Science Department at the Faculty of Science and Technlogy, I work with a growing number of highly motivated colleagues to establish data science as as strong field or research and education at NMBU.

My main field of research is simulation technology for large-scale brain models. I have played a key role in the development of the NEST simulator for nearly two decades. With NEST, we set a still-standing world record for large-scale neuronal network simulation on the Japanse K supercomputer, simulating a network of 1.86 billion neurons connected by over 10 trillion synapses. With my group and the NEST developer community elsewhere, we are now pushing the boundaries of simulations towards whole-brain models on coming exascale computers.

These efforts are deeply integrated into and partially funded by the Human Brain Project (HBP), one of three ICT flagship projects funded by the European Commission. Together with more than 100 partner institutions, we are working hard to establish EBRAINS as a long-term brain research research infrastructure in Europe. Together with many excellent colleagues from the Human Brain Project led by my NMBU colleague Gaute Einevoll, we recently explained our motiviation in The scientific case for brain simulation.

In addition to my position at NMBU I hold a Visiting Researcher position at the Institute for Neuroscience and Medicine (INM-6) at Research Centre Jülic and lead the NEST User and Developer Community as president of the NEST Initiative. 

You can follow my software development efforts on Github.

  • Fagfelt
    • Applied informatics
    • Computational neuroscience
    • Large-scale simulation
    • Reproducibility
    • Data science
  • Publikasjoner

    Liste med publikasjoner fra min forskning. (Cristin)

    Software

    1. The NEST Simulator.
    2. M.-O. Gewaltig, A. Morrison, and H. E. Plesser.
      NEST by example: An introduction to the neural simulation tool NEST.
      In N. Le Novère, editor, Computational Systems Neurobiology, chapter 18, pages 533-558. Springer Science+Business Media, Dordrecht, 2012. DOI 10.1007/978-94-007-3858-4_18. Preprint.

    Large-scale neuronal network simulation

    1. G. T. Einevoll, A. Destexhe, M. Diesmann, S. Grün, V. Jirsa, M. d Kamps, M. Migliore, T. V. Ness, H. E. Plesser, and F. Schürmann. The scientific case for brain simulations. Neuron, 102(4):735-744, 2019. DOI 10.1016/j.neuron.2019.03.027. On arXiv.org.
    2. T. Heiberg, B. Kriener, T. Tetzlaff, G. T. Einevoll, and H. E. Plesser.
      Firing-rate model for neurons with a broad repertoire of spiking behaviors.
      J Comput Neurosci, 45:103-132, 2018. DOI 10.1007/s10827-018-0693-9.
    3. H. E. Plesser.
      Reproducibility vs. replicability: A brief history of a confused terminology.
      Frontiers in Neuroinformatics, 11:76, 2018. DOI 10.3389/fninf.2017.00076.
    4. T. Ippen, J. M. Eppler, H. E. Plesser, M. Diesmann. Constructing neuronal network models in massively parallel environments. Front Neuroinform, 11:30, 2017. DOI 10.3389/fninf.2017.00030.
    5. S. Kunkel, M. Schmidt, J. M. Eppler, H. E. Plesser, G. Masumoto, J. Igarashi, S. Ishii, T. Fukai, A. Morrison, M. Diesmann, and M. Helias.
      Spiking network simulation code for petascale computers.
      Front Neuroinform, 8:78, 2014. DOI 10.3389/fninf.2014.00078.
    6. S. Kunkel, T. C. Potjans, J. M. Eppler, H. E. Plesser, A. Morrison, and M. Diesmann.
      Meeting the memory challenges of brain-scale network simulation.
      Front. Neuroinform., 5:35, 2012. DOI 10.3389/fninf.2011.00035.
    7. H. E. Plesser.
      Generating random numbers.
      In S. Grün and S. Rotter, editors, Analysis of Parallel Spike Trains, Springer Series in Computational Neuroscience, chapter 19, pages 399-411. Springer, New York, 2010.
    8. H. E. Plesser and M. Diesmann.
      Simplicity and efficiency of integrate-and-fire neuron models..
      Neural Comput, 21:353-359, 2009. DOI 10.1162/neco.2008.03-08-731. Preprint.
    9. H. E. Plesser, J. M. Eppler, A. Morrison, M. Diesmann, and M.-O. Gewaltig.
      Efficient parallel simulation of large-scale neuronal networks on clusters of multiprocessor computers.
      In A.-M. Kermarrec, L. Bougé, and T. Priol, editors, Euro-Par 2007: Parallel Processing, volume 4641 of Lecture Notes in Computer Science, pages 672-681, Berlin, 2007. Springer-Verlag. DOI 10.1007/978-3-540-74466-5. Reprint.
    10. A. Morrison, S. Straube, H. E. Plesser, and M. Diesmann.
      Exact subthreshold integration with continuous spike times in discrete time neural network simulations.
      Neural Comput, 19:47-79, 2007. Reprint.

    Reproducibility and replicability in computational science

    1. S. Crook, A. P. Davison, and H. E. Plesser.
      Learning from the past: Approaches for reproducibility in computational neuroscience.
      In J. M. Bower, editor, 20 Years in Computational Neuroscience, chapter 4, pages 73-102. Springer Science+Business Media, New York, 2013. DOI 10.1007/978-1-4614-1424-7_4.
    2. S. Crook, J. Bednar, S. Berger, R. Cannon, A. Davison, M. Djurfeldt, J. Eppler, B. Kriener, S. Furber, B. Graham, H. E. Plesser, L. Schwabe, L. Smith, V. Steuber, and S. v Albada.
      Creating, documenting and sharing network models.
      Network-Comp Neural 23:131-149, 2012. Online version. Preprint.
    3. E. Nordlie and H. E. Plesser.
      Visualizing neuronal network connectivity with connectivity pattern tables.
      Front. Neuroinform., 3:39, 2010. DOI 10.3389/neuro.11.039.2009.
    4. E. Nordlie, M.-O. Gewaltig, and H. E. Plesser.
      Towards reproducible descriptions of neuronal network models.
      PLoS Comput Biol, 5(8):e1000456, Aug 2009. DOI 10.1371/journal.pcbi.1000456.

    Computational neuroscience

    1. T. Heiberg, B. Kriener, T. Tetzlaff, A. Casti, G. T. Einevoll, and H. E. Plesser.
      Firing-rate models capture essential response dynamics of LGN relay cells.
      J Comput Neurosci, 35:359-375, 2013. DOI 10.1007/s10827-013-0456-6.
    2. G. T. Einevoll and H. E. Plesser.
      Extended difference-of-gaussians model incorporating cortical feedback for relay cells in the lateral geniculate nucleus of cat.
      Cogn Neurodyn, 6:307-324, 2012. DOI 10.1007/s11571-011-9183-8.
    3. G. T. Einevoll and H. E. Plesser.
      Response of the difference-of-gaussians model to circular drifting-grating patches.
      Visual Neurosci, 22:437-446, 2005.
    4. G. T. Einevoll and H. E. Plesser.
      Linear mechanistic models for the dorsal lateral geniculate nucleus of cat probed using drifting grating stimuli.
      Network-Comp Neural, 13:503-530, 2002. Reprint.
    5. H. E. Plesser and T. Geisel.
      Stochastic resonance in neuron models: Endogenous stimulation revisited.
      Phys Rev E, 63:031916-1-6, 2001. Reprint.
    6. H. E. Plesser and W. Gerstner.
      Noise in integrate-and-fire neurons: from stochastic input to escape rates.
      Neural Comput, 12:367-384, 2000. DOI 10.1162/089976600300015835. Reprint.
    7. H. E. Plesser and T. Geisel.
      Markov analysis of stochastic resonance in a periodically driven integrate-and-fire neuron.
      Phys Rev E, 59:7008-7017, 1999. Reprint.
    8. H. E. Plesser and S. Tanaka.
      Stochastic resonance in a model neuron with reset.
      Phys Lett A, 225:228-234, 1997.
  • Undervisning
  • Forskning og prosjekter

    Forskningsprosjekter med nettside utenfor NMBU

  • Mer om meg og CV

    Background

    • Diploma in Physics, RWTH Aachen, Germany
    • Doctoral degree in science, University of Göttingen, Germany
    • Associate professor in informatics at NMBU from 2002
    • Full professor in informatics at NMBU from 2018
    • Section head Science 2010–2016 and 2018/19
    • Section head Data Science since 2019
    • Guest researcher
      • RIKEN Brain Science Institute, Japan, 2008–2011
      • Simula Research Laboratory, 2009
      • Institute for Neuroscience and Medicine (INM-6), Research Centre Jülich, Germany, since 2013 
      • Center for Integrative Neural Plasticity, Universitetet i Oslo, 2016/17

    Offices

    • President, The NEST Initiative
    • Board member, Norwegian Neuroscience Society
    • Task leader, EBRAINS High-Level Support Team
    • Chairman of the board, Norwegian Research School for Neuroscience (2013–2020)
    • Norway's representative on the Human Brain Project Stakeholder Board (2016–2018)
    • Deputy leader, Human Brain Project Subproject High Performance Analytics and Computing (2018–2020)