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
  • 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

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

Homepage Department/Research Group

Link to my google scholar profile:

Selected Recent Publications

  • 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.