Adaptive Co-ordinate Transformation Based on a Spike Timing-Dependent Plasticity Learning Paradigm
نویسندگان
چکیده
A spiking neural network (SNN) model trained with spiking-timingdependent-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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