Priesemann, Viola, Prof. Dr.

Max Planck Research Group Leader Neural Systems Theory


  • since 2022: Group Leader, MPI for Dynamics and Self-Organizaiton, and Professor, Department of Physics, Georg-August University Göttingen
  • since 2016: Max Planck Research Group Leader, MPI for Dynamics and Self-Organization, Göttingen, Germany
  • 01/2017 – 03/2017: Guest Researcher, Ernst-Strüngmann-Institute, Frankfurt
  • 2014 – 2016: Bernstein Fellow and Group Leader, Bernstein Center for Computational Neuroscience & MPI for Dynamics and Self-Organization, Göttingen, Germany
  • 2013 – 2014: PostDoc, MPI for Dynamics and Self-Organization, Göttingen, Germany
  • 2013: PhD, Goethe University Frankfurt, Germany
  • 2008 – 2013: Research Projects at the Ecole Normale Superieure (Paris, France), Caltech
  • Pasadena, USA), MPI for Brain Research & FIAS (Frankfurt, Germany)


Honours, Grants & Service to the Community

  • since 2023: member of the Göttingen Academy of Science and Humanities
  • 2022: Lise Meitner Lecturer, German Physical Society (DPG) and Austrian Physical Society (ÖPG)
  • 2022: Arthur Burkhardt Award
  • 2021: Dannie Heineman Award of the Göttingen Academy of Sciences and Humanities
  • 2021: Lower Saxony Science Prize
  • 2021:Communitas Prize, Max Planck Society
  • since 2021: member of Junge Akademie
  • since 2020: Associated to the Max Planck - U of Toronto Centre of Neurophysics
  • since 2020: Lead-PI in a project of the SPP 2205 “Evolutionary Optimisation of Neuronal Processing”
  • since 2019: Scientific Member of the Max Planck Society
  • since 2019: Initiation of the “Göttingen Neural Networking” Days
  • 2016 – 2017: German-Israel Foundation (GIF) Young Investigator Grant
  • since 2015: Fellow of the Schiemann Kolleg of the Max Planck Society
  • 2014 – 2016: Bernstein Fellow
  • 2010: Thomas B. Grave and Elizabeth F. Grave Scholarship



Major Research Interests

  • Neural Networks
  • Neural Networks
  • Information Processing
  • Statistical Physics
  • Nonlinear Dynamics
  • Collective Phenomena
  • Living Computation
  • Self-Organization of Computation
  • Neural Plasticity & Learning
  • Homeostatic Plasticity
  • Design and Optimization of Neural Computation
  • Information Theory


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