  Neuromorphic Computing is a nascent research field in which models and
devices are designed to process information by emulating biological neural
systems. Thanks to their superior energy efficiency, analog neuromorphic
systems are highly promising for embedded, wearable, and implantable systems.
However, optimizing neural networks deployed on these systems is challenging.
One main challenge is the so-called timescale mismatch: Dynamics of analog
circuits tend to be too fast to process real-time sensory inputs. In this
thesis, we propose a few working solutions to slow down dynamics of on-chip
spiking neural networks. We empirically show that, by harnessing slow dynamics,
spiking neural networks on analog neuromorphic systems can gain non-trivial
performance boosts on a battery of real-time signal processing tasks.
