  Artificial neural networks and computational neuroscience models have made
tremendous progress, allowing computers to achieve impressive results in
artificial intelligence (AI) applications, such as image recognition, natural
language processing, or autonomous driving. Despite this remarkable progress,
biological neural systems consume orders of magnitude less energy than today's
artificial neural networks and are much more agile and adaptive. This
efficiency and adaptivity 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, activity of
biological neurons follows continuous-time dynamics in real, physical time,
instead of operating on discrete temporal cycles abstracted away from
real-time. Here, we present neuromorphic processing devices that emulate the
biological style of processing by using parallel instances of mixed-signal
analog/digital circuits that operate in real time. We argue that this approach
brings significant advantages in efficiency of computation. We show examples of
embodied neuromorphic agents that use such devices to interact with the
environment and exhibit autonomous learning.
