  Neuromorphic engineers aim to develop event-based spiking neural networks
(SNNs) in hardware. These SNNs closer resemble dynamics of biological neurons
than todays' artificial neural networks and achieve higher efficiency thanks to
the event-based, asynchronous nature of processing. Learning in SNNs is more
challenging, however. Since conventional supervised learning methods cannot be
ported on SNNs due to the non-differentiable event-based nature of their
activation, learning in SNNs is currently an active research topic.
Reinforcement learning (RL) is particularly promising method for neuromorphic
implementation, especially in the field of autonomous agents' control, and is
in focus of this work. In particular, in this paper we propose a new digital
multiplier-less hardware implementation of an SNN. We show how this network can
learn stimulus-response associations in a context-dependent task through a RL
mechanism. The task is inspired by biological experiments used to study RL in
animals. The architecture is described using the standard digital design flow
and uses power- and space-efficient cores. We implement the behavioral
experiments using a robot, to show that learning in hardware also works in a
closed sensorimotor loop.
