Spike-based learning of transfer functions with the SpiNNaker neuromimetic simulator

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

  • Authors:
  • Sergio Davies
  • Terry Stewart
  • Chris Eliasmith
  • Steve Furber

Abstract

Recent papers have shown the possibility to implement large scale neural network models that perform complex algorithms in a biologically realistic way. However, such models have been simulated on architectures unable to perform realtime simulations. In previous work we presented the possibility to simulate simple models in real-time on the SpiNNaker neu-romimetic architecture. However, such models were 'static': the algorithm performed was defined at design-time. In this paper we present a novel learning rule, that exploits the peculiarities of the SpiNNaker system, enabling models designed with the Neural Engineering Framework (NEF) to learn transfer functions using a supervised framework. We show that the proposed learning rule, belonging to the Prescribed Error Sensitivity (PES) class, is able to learn, effectively, both linear and non-linear functions. © 2013 IEEE.

Bibliographical metadata

Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks|Proc Int Jt Conf Neural Networks
Place of PublicationUSA
PublisherIEEE
ISBN (Print)9781467361293
DOIs
Publication statusPublished - 2013
Event2013 International Joint Conference on Neural Networks, IJCNN 2013 - Dallas, TX
Event duration: 1 Jul 2013 → …

Conference

Conference2013 International Joint Conference on Neural Networks, IJCNN 2013
CityDallas, TX
Period1/07/13 → …