Artificial Intelligence (AI) neural networks and machine learning inference accelerators represent a successful technology for solving a wide range of complex tasks. However for many practical purposes that involve fast real-time interactions with the environment these systems still cannot match the performance and efficiency of their biological counterparts. One possible reason lies in the differences between the principles of computation used by nervous systems and those used by conventional time-multiplexed computing systems. In this talk I will present neuromorphic electronic circuits that directly emulate the physics of computation used in animal brains to build neural processing systems which use spike-based representations and brain-inspired adaptation and learning mechanisms. I will show how large-scale multi-core architectures can be built by combining these circuits with asynchronous digital logic ones, and I will present examples of chips that are ideally suited for real-world sensory- processing edge-computing applications.