Can I read The Ripple Pond: Enabling Spiking Networks to See on EtoBox?

The Ripple Pond: Enabling Spiking Networks to See by Afshar, Saeed; Cohen, Gregory; Wang, Runchun; van Schaik, Andre; Tapson, Jonathan; Lehmann, Torsten; Hamilton, Tara Julia is a scholarly article available to read on EtoBox.

What is The Ripple Pond: Enabling Spiking Networks to See about?

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.

Author

Afshar, Saeed; Cohen, Gregory; Wang, Runchun; van Schaik, Andre; Tapson, Jonathan; Lehmann, Torsten; Hamilton, Tara Julia

Published

2013

Language

EN