  In this paper we present the biologically inspired Ripple Pond Network (RPN),
a simply connected spiking neural network that, operating together with
recently proposed PolyChronous Networks (PCN), enables rapid, unsupervised,
scale and rotation invariant object recognition using efficient spatio-temporal
spike coding. The RPN has been developed as a hardware solution linking
previously implemented neuromorphic vision and memory structures capable of
delivering end-to-end high-speed, low-power and low-resolution recognition for
mobile and autonomous applications where slow, highly sophisticated and power
hungry signal processing solutions are ineffective. Key aspects in the proposed
approach include utilising the spatial properties of physically embedded neural
networks and propagating waves of activity therein for information processing,
using dimensional collapse of imagery information into amenable temporal
patterns and the use of asynchronous frames for information binding.
