Current artificial intelligence approaches do not scale well; they constantly require more energy and data. Thus, researchers are taking inspiration from the brain to develop a new generation of efficient and bio-inspired artificial neural networks to mimic the effectiveness and efficiency of biological neural systems. This involves studying the computational properties of neural systems and designing specialized "neuromorphic" hardware architectures that support bio-inspired neural networks. The Neuro Computing course aims to teach students about the fundamental characteristics of neural circuits and their computational properties. Additionally, it will teach students how to design spiking neural networks using analog "neuromorphic" electronic circuits, which can express complex non-linear dynamics and are endowed with adaptation and online learning mechanisms. Content: basic neuroscience knowledge, mixed-signal neuromorphic circuits, systems, and architectures.