Adaptive co-ordinate transformation based on a spike timing-dependent plasticity learning paradigm

Authors:
QingXiang Wu;T. M. McGinnity;L. P Maguire;A. Belatreche;B. Glackin
Affiliations:
School of Computing and Intelligent Systems, University of Ulster, Derry, N.Ireland, UK;School of Computing and Intelligent Systems, University of Ulster, Derry, N.Ireland, UK;School of Computing and Intelligent Systems, University of Ulster, Derry, N.Ireland, UK;School of Computing and Intelligent Systems, University of Ulster, Derry, N.Ireland, UK;School of Computing and Intelligent Systems, University of Ulster, Derry, N.Ireland, UK
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Year:
2005

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Abstract

A spiking neural network (SNN) model trained with spiking-timing-dependent-plasticity (STDP) is proposed to perform a 2D co-ordinate transformation of the polar representation of an arm position to a Cartesian representation in order to create a virtual image map of a haptic input. The position of the haptic input is used to train the SNN using STDP such that after learning the SNN can perform the co-ordinate transformation to generate a representation of the haptic input with the same co-ordinates as a visual image. This principle can be applied to complex co-ordinate transformations in artificial intelligent systems to process biological stimuli.