Focusing on biophysics and cell physiology I carried out my diploma work as a physics undergraduate student in Bert Sakmann’s lab at the Max Planck Institute for Medical Research in Heidelberg. The idea that cutting-edge imaging technology breeds scientific advancement in neuroscience brought me to Fritjof Helmchen’s group at the Brain Research Institute in Zurich, where I developed new high-speed calcium imaging methods to investigate information processing in large neuronal networks with a temporal precision of a few milliseconds. Recognizing the potential of population calcium imaging to investigate large scale neural coding I then decided to join Mark Schnitzer’s group in Stanford, where I studied learning induced changes of neuronal network codes utilizing cutting-edge miniaturized microscopes that allow imaging in freely-moving and behaving animals.
My long-term vision is to extract fundamental principles of network-learning from real biological networks and then to reverse-engineer their functionality as logical, reproducible algorithms. I am convinced that an analytical understanding of neural network computation and learning would allow us to implement similar neural algorithms in software or directly as electrical circuits. Coming close to mimic human thinking and performance such new technologies would be poised to transform our daily use of intelligent systems.
My pet projects outside the lab include breadboarding with deep neural networks on FPGAs and playing around network learning algorithms in Python.
INI-401, 227-1037-00 Introduction to Neuroinformatics
INI-507, 227-0421-00 Learning in Deep Artificial and Biological Neuronal Networks