Göttingen Graduate Center for Neurosciences, Biophysics, and Molecular Biosciences

Prof. Dr. Christian Tetzlaff


  • 2009 Diplom in Physics, Georg August University, Göttingen
  • 2010-2013 Max Planck Institute for Dynamics and Self-Organization / University of Göttingen / GGNB program Physics of Biological and Complex Systems
  • 2013 Dissertation in Physics (Dr.rer.nat.), Georg August University, Göttingen
  • 2015 - 2021 Bernstein Fellow (independent research position) at the Max Planck Institute for Dynamics and Self-Organization, Göttingen and the University of Göttingen
  • 2015 Guest Scientist at the Weizmann Institute, Rehovot, Israel with Prof. Misha Tsodyks
  • 2015 Guest Scientist at the Columbia University, New York, USA
  • 2017 - 2021 Co-coordinator and work package leader of the European Union’s Horizon 2020 Project “Plan4Act”
  • since 2017 Principal investigator of a subproject part of the SFB 1286 “Quantitative Synaptology” in Göttingen funded by the DFG
  • Since 2018 Principal investigator of a project as part of the Intel Neuromorphic Research Community (INRC) using the Intel-chip “Loihi”
  • Since 2020 R&D coordinator and work package leader of the European Union’s Horizon 2020 Project “ADOPD”
  • Since 2020 Principal investigator of the subproject “FIPPA” being part of the European Union’s Flagship Project “Human Brain Project”
  • Since 2021 W2-Professorship at the Institute for Neuro- and Sensory Physiology of the University Medical Center Göttingen
  • Since 2022 Principal investigator of the BMBF-funded Project “KISSKI”




Major Research Interests


    We use computational and mathematical tools to identify and understand the fundamental principles that are governing learning and computation in neuronal systems. In addition, we transfer these principles to advance cutting-edge technologies like robotics or neuromorphic engineering. For this, we integrate knowledge from different spatial scales, from protein interactions in individual synapses to large-scale adaptive neuronal networks, and take advantage of methods from different scientific disciplines such as nonlinear dynamics, numeric, statistical physics, information theory, or machine learning.



Homepage Department/Research Group
https://tetzlab.com/




Selected Recent Publications


  • Bonnin EA, Golmohammadi A, Rehm R, Tetzlaff C, Rizzoli SO (2024). High-resolution analysis of bound Ca2+ in neurons and synapses. Life Science Alliance, 7(1):e202302030.

  • Ricci S, Kappel D, Tetzlaff C, Ielmini D, Covi E (2023). Tunable synaptic working memory with volatile memristive devices. Neuromorphic Computing and Engineering, 3(4):e044004.

  • Becker MFP, Tetzlaff C (2021) The biophysical basis underlying the maintenance of early phase long-term potentiation. PLoS Computational Biology, 17(3):e1008813

  • Luboeinski J, Tetzlaff C (2021) Memory consolidation and improvement by synaptic tagging and capture in recurrent neural networks. Communications Biology, 4(1):1-17

  • Michaelis C, Lehr A, Tetzlaff C (2020) Robust trajectory generation for robotic control on the neuromorphic research chip Loihi. Frontiers in Neurorobotics, 14:97

  • Reshetniak S, Fernández-Busnadiego R, Müller M, Rizzoli SO, Tetzlaff C (2020) Quantitative Synaptic Biology: A Perspective on Techniques, Numbers and Expectations. International Journal of Molecular Sciences, 21(19):7298

  • Herzog Sǂ, Tetzlaff Cǂ, Wörgötter F (2020) Evolving artificial neural networks with feedback. Neural Networks, 123:153-162

  • Nachstedt T, Tetzlaff C (2017) Working memory requires a combination of transient and attractor-dominated dynamics to process unreliably timed inputs. Scientific Reports, 7:2473

  • Tetzlaff C, Dasgupta S, Kulvicius T, Wörgötter F (2015) The use of hebbian cell assemblies for nonlinear computation. Scientific Reports, 5:12866

  • Fauth M, Wörgötter F, Tetzlaff C (2015) Formation and maintenance of robust long-term information storage in the presence of synaptic turnover. PLoS Computational Biology, 11(12):e1004684

  • Fauth M, Wörgötter F, Tetzlaff C (2015) The formation of multi-synaptic connections by the interaction of synaptic and structural plasticity and their functional consequences. PLoS Computational Biology, 11(1):e1004031

  • Tetzlaff C, Kolodziejski C, Timme M, Tsodyks M, Wörgötter F (2013) Synaptic scaling enables dynamically distinct short- and long-term memory formation. PLoS Computational Biology, 9(10), e10003307