David Kappel is an assistant professor at the Center for Cognitive Interaction Technology (CITEC) Bielefeld University, where he leads the group of Sustainable Machine Learning since 2024. Previously, he was a postdoctoral researcher at the Ruhr-Universität Bochum, the TU Dresden and the University of Göttingen. David Kappel received his Ph.D. in computer science from Graz University of Technology in 2018. His research interests focus on efficient algorithms and models for synaptic plasticity, neural dynamics, Bayesian inference and hierarchical learning models.

Title: What we can learn from the hierarchical organization of brain networks for efficient AI

Abstract: Brain networks exhibit a prominent hierarchical organization at all levels, from anatomical organization to activity patterns. In contrast, artificial deep neural networks are organized sequentially using the end-to-end error backpropagation algorithm for training. This algorithm propagates information forward and backward through the entire network, updating its parameters in tiny steps. While this approach scales well to large networks, it treats them as monolithic “black boxes,” requiring substantial resources. In this talk, I will revisit results on the hierarchical organization of biological neural networks. Based on these results, I will identify principled mechanisms for the next generation of learning algorithms that efficiently use available resources while maintaining robust performance on challenging AI tasks.