  Despite being originally inspired by the central nervous system, artificial
neural networks have diverged from their biological archetypes as they have
been remodeled to fit particular tasks. In this paper, we review several
possibilites to reverse map these architectures to biologically more realistic
spiking networks with the aim of emulating them on fast, low-power neuromorphic
hardware. Since many of these devices employ analog components, which cannot be
perfectly controlled, finding ways to compensate for the resulting effects
represents a key challenge. Here, we discuss three different strategies to
address this problem: the addition of auxiliary network components for
stabilizing activity, the utilization of inherently robust architectures and a
training method for hardware-emulated networks that functions without perfect
knowledge of the system's dynamics and parameters. For all three scenarios, we
corroborate our theoretical considerations with experimental results on
accelerated analog neuromorphic platforms.
