Sprecher
Beschreibung
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.