  Reconfigurable intelligent surfaces (RISs) operate similarly to
electromagnetic (EM) mirrors and remarkably go beyond Snell law to generate an
applicable EM environment allowing for flexible adaptation and fostering
sustainability in terms of economic deployment and energy efficiency. However,
the conventional RIS is controlled through high-latency field programmable gate
array or micro-controller circuits usually implementing artificial neural
networks (ANNs) for tuning the RIS phase array that have also very high energy
requirements. Most importantly, conventional RIS are unable to function under
realistic scenarios i.e, high-mobility/low-end user equipment (UE). In this
paper, we benefit from the advanced computing power of neuromorphic processors
and design a new type of RIS named \emph{NeuroRIS}, to supporting high mobility
UEs through real time adaptation to the ever-changing wireless channel
conditions. To this end, the neuromorphic processing unit tunes all the RIS
meta-elements in the orders of $\rm{ns}$ for particular switching circuits
e.g., varactors while exhibiting significantly low energy requirements since it
is based on event-driven processing through spiking neural networks for
accurate and efficient phase-shift vector design. Numerical results show that
the NeuroRIS achieves very close rate performance to a conventional RIS-based
on ANNs, while requiring significantly reduced energy consumption with the
latter.
