My long-term vision is to extract fundamental principles of network-learning from real biological networks and then to reverse-engineer their functionality as logical, reproducible algorithms. I am convinced that an analytical understanding of neural network computation and learning would allow us to implement similar neural algorithms in software or directly as electrical circuits. Coming close to mimic human thinking and performance such new technologies would be poised to transform our daily use of intelligent systems. My pet projects outside the lab include breadboarding with deep neural networks on FPGAs and playing around network learning algorithms in Python.