title: The ripple pond: enabling spiking networks to see.
abstract: 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 (TP) 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 utilizing 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 TP and the use of asynchronous frames for information binding.
authors.affiliation: Bioelectronics and Neurosciences, The MARCS Institute, University of Western Sydney Penrith, NSW, Australia ; School of Electrical Engineering and Telecommunications, The University of New South Wales Sydney, NSW, Australia.
authors.forename: Saeed
authors.initials: S
authors.lastname: Afshar
authors.forename: Gregory K
authors.initials: GK
authors.lastname: Cohen
authors.forename: Runchun M
authors.initials: RM
authors.lastname: Wang
authors.forename: André
authors.initials: A
authors.lastname: Van Schaik
authors.forename: Jonathan
authors.initials: J
authors.lastname: Tapson
authors.forename: Torsten
authors.initials: T
authors.lastname: Lehmann
authors.forename: Tara J
authors.initials: TJ
authors.lastname: Hamilton
keywords: image transformation invariance; neuromorphic engineering; object recognition; polychronous network; spiking neural network; view invariance