Göttinger Graduiertenzentrum für Neurowissenschaften, Biophysik und Molekulare Biowissenschaften

Fauth, Michael, Dr.*

Sub-Group Leader, Bernstein Center for Computational Neuroscience; Third Physics Institute


  • 2010/2011 1st State Exam for Teachers (Gymnasium): Mathematics, Physics & Astronomy, TU Dresden
  • 2012-2016 Member of GGNB Program PTCN, University of Göttingen
  • 2016 Dissertation (Dr.rer.nat.) in Physics with Prof. Florentin Wörgötter, Göttingen
  • 2017-2018 DFG Reasearch Stipend for Postdoc with Prof. Mark van Rossum, Edinburgh University
  • since 2017 Co-Supervisor of a subproject part of the SFB 1286 “Quantitative Synaptology” in Göttingen funded by the DFG
  • since 2021 Principal investigator of a subproject in the second funding period of SFB 1286



Major Research Interests

As biological entities, the neurons and synapses that determine information processing and storage in our brains exhibit two properties: On the one hand, their characteristics fluctuate over time and are unstable and noisy. This can be observed on multiple spatial and temporal scales: size of synapses undergoes large spontaneous fluctuations within minutes, synaptic proteins are exchanged within hours, and a substantial fraction of synapses is removed and replaced by new connections on a daily basis. On the other hand, these fluctuations entail that the dynamics of these entities is heterogeneous – that is different for each neuron and synapse. For example, firing rate adaptation follows different time courses in every neuron and plasticity processes exhibit different dynamics even for synapses at the same neuron.
In my research, I address the consequences of these properties, asking (a) how the brain implements stable function such as information processing and storage based on an unstable, heterogeneous substrate and (b) whether this instabilities and heterogeneities are functionally useful. The answers to these questions, I investigate mathematical models of the dynamics of synapses and neurons at different levels of detail ranging from the molecular dynamics and function of the individual synapse , over small circuits, up to network models with multiple brain areas. Besides the simulations, model dynamics are analyzed using methods from information theory, nonlinear dynamical system theory, machine learning, cellular automata, stochastic process theory and statistics. Moreover, to also highlight the functional impact of the discovered phenomena, I strive to link them behavioral predictions or demonstrate their applicability in technical systems.




Homepage Department/Research Group

in preparation

Selected Recent Publications


  • Miner D., Wörgötter F., Tetzlaff C., Fauth M. (2021). Self-organized structuring of recurrent neuronal networks for reliable information transmission. Biology, 10,577.
  • Bonilla-Quintana M., Wörgötter F., D’Este E., Tetzlaff C., Fauth M. (2021). Reproducing asymmetrical spine shape fluctuations in a model of actin dynamics predicts self-organized criticality. Scientific Reports, 11, 4012.
  • Bonilla-Quintana M., Wörgötter F., Tetzlaff C., Fauth M. (2020). Modeling the Shape of Synaptic Spines by Their Actin Dynamics. Front. Synaptic Neurosci., 12:9. doi: 10.3389/fnsyn.2020.00009.
  • Fauth M., van Rossum M.C. (2019). Self-organized reactivation maintains and reinforces memories despite synaptic turnover. eLife, 2019;8:e43717.
  • Fauth M.and Tetzlaff C. (2016). Opposing effects of neuronal activity on structural plasticity. Frontiers in Neuroanatomy, 10:75.
  • Fauth M., Wörgötter F., and 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., and 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.