Friedemann Zenke studied physics at the University of Bonn. He also studied at the Australian National University in Canberra. He pursued his Ph.D. in computational neuroscience with Wulfram Gerstner at the École polytechnique fédérale de Lausanne (EPFL). After his Ph.D., Friedemann joined Surya Ganguli’s group at Stanford as a postdoctoral researcher. He then moved to the University of Oxford as a Sir Henry Wellcome fellow with Tim Vogels. Friedemann is now a senior research group leader at the Friedrich Miescher Institute for Biomedical Research (FMI) and an assistant professor at the University of Basel, Switzerland. His group addresses fundamental theoretical questions about biological neural networks and learning algorithms.

Abstract: Learning algorithms for spiking and physical neural networks

Biological intelligence is remarkably data- and energy-efficient. We strive to understand and replicate this in artificial intelligence. A key step toward this goal is to develop robust, scalable learning algorithms for physical and biologically inspired neural networks.Achieving this requires rethinking fundamental principles of representation learning and credit assignment. In this talk, I will present recent work from our group on learning algorithms suited for recurrent, noisy, and resource-constrained substrates. These include, but are not limited to, spiking and neuromorphic systems. I will show how local, biologically plausible learning rules can emerge from self-supervised objectives. I will also discuss applied work on training spiking networks for ultra-low-power brain-machine interfaces.