Göttinger Graduiertenzentrum für Neurowissenschaften, Biophysik und Molekulare Biowissenschaften

Priesemann, Viola, Prof. Dr.

Leiterin einer Max-Planck-Forschungsgruppe Theorie neuronaler Systeme


  • Since 2022: Professor (W3), Faculty of Physics, Georg August University of 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


  • 2021: Medaille für naturwissenschaftliche Publizistik der DPG
    https://de.wikipedia.org/wiki/Medaille_f%C3%BCr_naturwissenschaftliche_Publizistik
  • Communitas Award of the Max Planck Society
  • since 2021: Member of the "Junge Akademie"
  • 2020: Offer for a W3 position, faculty of Physics, U Heidelberg (declined)
  • since 2020: Public outreach and political advisor on the COVID-19 pandemic
  • since 2020: Member of the Cluster of Excellence: Multiscale Bioimaging
  • 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”




Major Research Interests

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
Bayesian Inference
Spreading Dynamics
Information Spreading in Social Networks
COVID-19



Homepage Department/Research Group


http://www.viola-priesemann.de/

Link to my google scholar profile:

https://scholar.google.de/citations?user=5oK8Ek4AAAAJ&hl=de&oi=ao


Selected Recent Publications


  • Rowland JM, van der Plas TL, Loidolt M, Lees RM, Keeling J, Dehning J, Akam T, Priesemann V, Packer AM (2023). Propagation of activity through the cortical hierarchy and perception are determined by neural variability. Nature Neuroscience. 26(9):1584-94

  • Contreras S, Dehning J, Loidolt M, Zierenberg J, Spitzner FP, Urrea-Quintero JH, Mohr SB, Wilczek M, Wibral M, Priesemann V (2021): The challenges of containing SARS-CoV-2 via test-trace-and-isolate. Nature Communications 12, 378

  • Mikulasch FA, Rudelt L, Priesemann V (2020). Local dendritic balance enables learning of efficient representations in networks of spiking neurons. arXiv preprint arXiv:2010.12395 - at PNAS

  • Dehning J, Zierenberg, J, Spitzner FP, Wibral M, Neto JP, Wilczek M, Priesemann V (2020), “Inferring change points in the spread of COVID-19 reveals the effectiveness of interventions”, Science.

  • Cramer B, Stöckel D, Kreft M, Wibral M, Schemmel J, Meier K, Priesemann V (2020), “Control of criticality and computation in spiking neuromorphic networks with plasticity”, Nature Communications.

  • Wilting J, Priesemann V (2019), “Between Perfectly Critical and Fully Irregular: A Reverberating Model Captures and Predicts Cortical Spike Propagation”, Cerebral Cortex.

  • Wilting J, Priesemann V (2018), “Inferring collective dynamical states from widely unobserved systems”, Nature Communications.

  • Zierenberg J, Wilting J, Priesemann V (2018), “Homeostatic Plasticity and External Input Shape Neural Network Dynamics”, Physical Review X.

  • Levina A, Priesemann V (2017), “Subsampling scaling”, Nature Communications.

  • Mikulasch FA, Rudelt L, Wibral M, Priesemann V. Where is the error? Hierarchical predictive coding through dendritic error computation. Trends in Neurosciences. 2023 Jan 1;46(1):45-59.

  • Levina A, Priesemann V, Zierenberg J. Tackling the subsampling problem to infer collective properties from limited data. Nature Reviews Physics. 2022 Dec;4(12):770-84.