  Future developments in artificial intelligence will profit from the existence
of novel, non-traditional substrates for brain-inspired computing. Neuromorphic
computers aim to provide such a substrate that reproduces the brain's
capabilities in terms of adaptive, low-power information processing. We present
results from a prototype chip of the BrainScaleS-2 mixed-signal neuromorphic
system that adopts a physical-model approach with a 1000-fold acceleration of
spiking neural network dynamics relative to biological real time. Using the
embedded plasticity processor, we both simulate the Pong arcade video game and
implement a local plasticity rule that enables reinforcement learning, allowing
the on-chip neural network to learn to play the game. The experiment
demonstrates key aspects of the employed approach, such as accelerated and
flexible learning, high energy efficiency and resilience to noise.
