Final Panel Discussion: Empowering First Generation Academics

On March 12, we hosted our final panel discussion on how to increase diversity in Academia. The topic was “First Generation Academics – How the Family Background Influences the Career Paths of Young Female Scientists." Our expert panelists – Zurna Ahmed, Holmer Steinfath, Britta Korkowsky, Charlotte Prauß and Ann-Kristin Kolwes – openly discussed the challenges faced by young researchers from non-academic backgrounds. These challenges include not only financial barriers but also self-doubt, uncertainty in the academic environment, and lack of understanding from their own families. But the panel also talked about seemingly simple yet significant hurdles in academic life, such as: How do I find a supervisor? Can I approach professors directly? How do I become a student assistant?

New Publication in PNAS: Understanding Local Learning in Neural Networks

How do individual elements in a neural network contribute to solving complex tasks? While both biological and artificial neural networks achieve remarkable performance, their local learning dynamics remain poorly understood. A new study by Michael Wibral, Viola Priesemann, and colleagues, published in PNAS, introduces a novel framework to describe local learning goals using principles from information theory. The researchers present infomorphic networks, which define learning objectives at the level of individual neurons through a parametric approach based on Partial Information Decomposition (PID). This allows them to unify different learning rules and tasks—including supervised, unsupervised, and memory learning—within a single theoretical framework. By making local learning dynamics more interpretable, infomorphic networks help bridge the gap between theoretical neuroscience and artificial intelligence.

Link to the paper