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

The Ripple Pond: Enabling Spiking Networks to See