Real-time event-driven spiking neural network object recognition on the SpiNNaker platform

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

  • External authors:
  • Garrick Orchard
  • Xavier Lagorce
  • Christoph Posch
  • Ryad Benosman
  • Francesco Galluppi


This paper presents a real-time spiking neural network adaptation of the HMAX object recognition model on an event-driven platform. Visual input is provided by a spiking silicon retina, while the SpiNNaker system is used as a computational hardware platform for implementation. We show the implementation of a simple Leaky Integrate-and-Fire (LIF) neuron model on SpiNNaker to create an event driven network, where a neuron only updates when it receives an interrupt indicating that a new input spike has been received. The model output consists of view tuned neurons which respond selectively to a particular view of an object. The network can be used to discriminate between objects, or between the same object at different views. On a 26 class character recognition task, the correct class is always assigned the highest probability (69.42% on average).

Bibliographical metadata

Original languageEnglish
Title of host publicationProceedings - IEEE International Symposium on Circuits and Systems
Number of pages4
ISBN (Print)9781479983919
Publication statusPublished - 27 Jul 2015
EventIEEE International Symposium on Circuits and Systems, ISCAS 2015 - Lisbon, Portugal
Event duration: 24 May 201527 May 2015


ConferenceIEEE International Symposium on Circuits and Systems, ISCAS 2015
Internet address