Title: Merging insights from artificial and biological neural networks for neuromorphic edge intelligence Abstract: The development of efficient bio-inspired algorithms and hardware is currently missing a clear framework. Should we start from the brain computational primitives and figure out how to apply them to real-world problems (bottom-up approach), or should we build on working AI solutions and fine-tune them to increase their biological plausibility (top-down approach)? We will see why biological plausibility and hardware efficiency are often two sides of the same coin, and how neuroscience- and AI-driven insights can cross-feed each other toward neuromorphic edge intelligence. Bio: Charlotte Frenkel is an Assistant Professor at Delft University of Technology, The Netherlands. She received her Ph.D. from Université catholique de Louvain in 2020 and was a post-doctoral researcher at the Institute of Neuroinformatics, UZH, and ETH Zürich, Switzerland. Her research aims at bridging the bottom-up (bio-inspired) and top-down (engineering-driven) design approaches toward neuromorphic intelligence, with a focus on digital neuromorphic processor design, embedded machine learning, and brain-inspired on-device learning. Dr. Frenkel received a best paper award at the IEEE International Symposium on Circuits and Systems (ISCAS) 2020 conference, and her Ph.D. thesis was awarded the FNRS / Nokia Bell Scientific Award 2021 and the FNRS / IBM Innovation Award 2021. In 2023, she was awarded prestigious AiNed Fellowship and Veni grants from the Dutch Research Council (NWO). She served as a program co-chair of the NICE conference and of the tinyML Research Symposium, as a TPC member of IEEE ESSERC, and as an associate editor for the IEEE Transactions on Biomedical Circuits and Systems."