Research interests

I study the computational principles of information processing and learning in biological and artificial brains by combining methods from cognitive science, neuroscience, machine learning and electrical engineering. I employ tractable behavioral experiments to reveal how humans and animals learn to cope with uncertain and ambiguous sensory evidence in their environment, and I explore in computational models how the required mathematical operations could be implemented in neuronal networks. My work further spawns engineering applications by developing operating principles for novel, brain-inspired computers.

Short bio

After my physics studies at Heidelberg University, Germany, I joined the Institute for Theoretical Computer Science in Graz, Austria, for my doctoral studies with Robert Legenstein. After my PhD, Karlheinz Meier gave me the opportunity to give a course on Brain-inspired Computing for master students in Heidelberg. In 2017, I joined the groups of Jan Drugowitsch (Neurobiology) and Sam Gershman (Psychology) at Harvard University.

Publications

List of publications: Google scholar
PhD thesis: Probabilistic Models of Computation and Self-organization in Recurrent Networks of Spiking Neurons (pdf)
Diploma thesis: Self-Stabilizing Network Architectures on a Neuromorphic Hardware System (pdf)