The ripple pond : enabling spiking networks to see

Saeed Afshar, Gregory K. Cohen, Runchun M. Wang, Andre van Schaik, Jonathan Tapson, Torsten Lehmann, Tara J. Hamilton

    Research output: Contribution to journalArticlepeer-review

    8 Citations (Scopus)

    Abstract

    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.
    Original languageEnglish
    Number of pages12
    JournalFrontiers in Neuroscience
    Volume7
    Issue number212
    DOIs
    Publication statusPublished - 2013

    Open Access - Access Right Statement

    Copyright © 2013 Afshar, Cohen, Wang, Van Schaik, Tapson, Lehmann and Hamilton. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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