Abstract. 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. 1

The human brain can perform a range of complicated computations and logical reasoning using neural networks with a huge number of neurons. Since Hodgkin and Huxley proposed a set of equations to describe the electrophysiological properties of spiking neurons, various network structures of neurons have been developed through neuroscience research that can now be simulated by electronic circuits or computer programs. In this paper, an adaptive learning mechanism is simulated based on the biological property related to the spike time dependent plasticity of synapses. A demonstration shows that such spiking neurons are able to develop their specific receptive field for recognition of patterns. This mechanism can be used to explain some adaptive behaviours in biological systems. It is can also be applied to artificial intelligent systems. Keywords: Spiking neural network; computational model; adaptive learning; spiking time dependent plasticity. 1.

In estimating properties of the world, we often use multiple sources of information. For example, in estimating the 3-d layout of a scene, there are many sources of information or “cues ” available for the estimation of depth and shape (Kaufman, 1974). These include binocular cues (disparity, vergence), motion