  In this paper we consider graph algorithms and graphical analysis as a new
application for neuromorphic computing platforms. We demonstrate how the
nonlinear dynamics of spiking neurons can be used to implement low-level graph
operations. Our results are hardware agnostic, and we present multiple versions
of routines that can utilize static synapses or require synapse plasticity.
