The central goal of my research is to understand the synaptic and neuronal dynamics originating from the interactions of diverse adaptive processes on different time scales and the resulting emergence of complex, cognitive behaviors.

In more detail, based on experimental data, my group analyses with mathematical tools from various scientific fields (e.g., nonlinear dynamics, graph theory, machine learning) the interactions of different, experimentally well-known plasticity mechanisms depending on environmental stimuli and their relations to cognitive processes such as learning, computation, and memory formation, which serve as the basis of complex behaviors.

Thereby, one important part of my research is to link the mathematical models to experimental findings by, on the one hand, analyzing experimental data to derive the theoretical foundations of the models and, on the other hand, by deducing experimentally verifiable predictions from the mathematical models to test the overall hypotheses.

In addition, the identified theoretical principles are transferred to technological applications such as robotic platforms or neuromorphic chips to verify derived hypotheses and to advance neuro-inspired technologies.

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