The Ripple Pond: Enabling Spiking Networks to See - Saeed eAfshar; Saeed eAfshar; Greg Kevin Cohen; Runchun Mark Wang; André evan Schaik; Jonathan eTapson; Torsten eLehmann; Tara Julia Hamilton; Tara Julia Hamilton (Frontiers in Neuroscience, 2013)
We present the biologically inspired Ripple Pond Network (RPN), a simply connected spiking neural network which performs a transformation converting two dimensional images to one dimensional temporal patterns suitable for recognition by temporal coding learning and memory networks. The RPN has been developed as a hardware solution linking previously implemented neuromorphic vision and memory structures such as frameless vision sensors and neuromorphic temporal coding spiking neural networks. Working together such systems are potentially 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.