Artificial neural networks (ANNs) and computational neuroscience models have made tremendous progress, enabling us to achieve impressive results in artificial intelligence applications, such as image recognition, natural language processing, and autonomous driving. Despite this, biological neural systems consume orders of magnitude less energy than today's ANNs and are much more flexible and robust. This adaptivity and efficiency gap is partially explained by the computing substrate of biological neural processing systems that is fundamentally different from the way today?s computers are built. Biological systems use in-memory computing elements operating in a massively parallel way rather than time-multiplexed computing units that are reused in a sequential fashion. Moreover, the activity of biological neurons follows continuous-time dynamics in real, physical time instead of operating on discrete temporal cycles abstracted away from real time.