Visual Universitätsmedizin Mainz

Research group: „Theory of neuronal dynamics“ (Prof. Tchumatchenko)

The long-term goal of our work is to find out how the neural code works and what computational strategies neurons have at their disposal. We are a computational team that works closely with experimental groups. This collaboration is essential for our projects and several team members belong to both groups.


We believe that mathematical models and quantitative methods drive the progress

The development of new models and analysis techniques that draw on mathematical, physical and computer science knowledge is driving our understanding of neural networks. We believe that a good model or analysis is derived from first principles and has a range of validity within which it makes and interprets accurate predictions. A biological model can operate at different abstract levels, from the micro-level of protein interaction to the macro-level of network dynamics. We focus on the network level and build models that address how neurons encode incoming information and how they organise their global network dynamics to accomplish this task. Although we specialise in neural signal processing, we aim to apply our models and methods to other disciplines. We hope to impact applications such as machine learning and brain-computer interfaces.

The role of computer models

The study of the brain has historically been a discipline dominated by biology, with experiments taking centre stage.  Although we are theorists at heart, we place great emphasis on verifying our models with experimental data. We work with excellent groups who carry out these experiments and make the feedback loop between theory and experiment enjoyable and fun.


Modelling the dynamics of AMPA receptors in neuronal dendrites

Synapses are considered the fundamental unit for information transmission and brain function. Synapses have the extraordinary ability to change their transmission strength via synaptic plasticity mechanisms. Long-term plasticity (LTP) (Bliss and Lømo, 1973) is a well-studied form of synaptic plasticity, and an ionotropic glutamate receptor known as the α-amino-3-hydroxy-5-methyl-4-isoxazolepropionate AMPA receptor (AMPAR). It is one of the main players in the modulation of LTP. In LTP, repeated synaptic stimulation leads to increased AMPAR numbers at the postsynaptic density (PSD) and consequently strengthens the synapse. Similarly, long-term depression (LTD), which leads to the weakening of a synapse, is achieved by reducing the number of AMPARs anchored at the PSD (Park, 2018). LTP and LTD have synaptic correlates with many brain functions such as memory and learning (Malenka, 2003).

The anchoring of AMPARs in the membrane at the PSD involves specific interplay between the receptor and several scaffold molecules (see Fig. 1). Studies have shown that AMPAR can traffic between intracellular and extrasynaptic membrane compartments via endosomal recycling and surface diffusion (Newpher and Ehlers, 2008). This bidirectional movement of AMPARs modulates their levels in the PSD and influences LTP and LTD. To understand synaptic plasticity, it is therefore essential to study AMPAR transport.

Schematic representation of the different mechanisms involved in AMPAR trafficking.


  1. Bliss, T.V.P., Lømo, T., 1973. Long-lasting potentiation of synaptic transmission in the   dentate area of the anaesthetized rabbit following stimulation of the perforant path. J. Physiol. 232, 331–356.
  2. Fonkeu, Y., N. Kraynyukova, A.S. Hafner, L. Kochen, F. Sartori, E.M. Schuman, and T. Tchumatchenko. 2019. How mRNA Localization and Protein Synthesis Sites Influence Dendritic Protein Distribution and Dynamics. Neuron. 103:1109-1122.e7. doi:10.1016/j.neuron.2019.06.022.
  3. Malenka, R.C., 2003. The long-term potential of LTP. Nat. Rev. Neurosci. 4, 923–926.
  4. Newpher, T.M., Ehlers, M.D., 2008. Glutamate Receptor Dynamics in Dendritic Microdomains. Neuron 58, 472–497.
  5. Park, M., 2018. AMPA Receptor Trafficking for Postsynaptic Potentiation. Front. Cell. Neurosci. 12.
  6. Sartori, F., A.S. Hafner, A. Karimi, A. Nold, Y. Fonkeu, E.M. Schuman, and T. Tchumatchenko. 2020. Statistical Laws of Protein Motion in Neuronal Dendritic Trees. Cell Rep. 33:108391. doi:10.1016/j.celrep.2020.108391.
Further information about the Research Group