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.
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Wu, Q., McGinnity, T.M., Maguire, L.P., Belatreche, A., Glackin, B. (2005). Adaptive Co-ordinate Transformation Based on a Spike Timing-Dependent Plasticity Learning Paradigm. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3610. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539087_54
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DOI: https://doi.org/10.1007/11539087_54
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28323-2
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