  Efficient and robust control using spiking neural networks (SNNs) is still an
open problem. Whilst behaviour of biological agents is produced through sparse
and irregular spiking patterns, which provide both robust and efficient
control, the activity patterns in most artificial spiking neural networks used
for control are dense and regular -- resulting in potentially less efficient
codes. Additionally, for most existing control solutions network training or
optimization is necessary, even for fully identified systems, complicating
their implementation in on-chip low-power solutions. The neuroscience theory of
Spike Coding Networks (SCNs) offers a fully analytical solution for
implementing dynamical systems in recurrent spiking neural networks -- while
maintaining irregular, sparse, and robust spiking activity -- but it's not
clear how to directly apply it to control problems. Here, we extend SCN theory
by incorporating closed-form optimal estimation and control. The resulting
networks work as a spiking equivalent of a linear-quadratic-Gaussian
controller. We demonstrate robust spiking control of simulated
spring-mass-damper and cart-pole systems, in the face of several perturbations,
including input- and system-noise, system disturbances, and neural silencing.
As our approach does not need learning or optimization, it offers opportunities
for deploying fast and efficient task-specific on-chip spiking controllers with
biologically realistic activity.
