Adaptive Co-ordinate Transformation Based on a Spike Timing-Dependent Plasticity Learning Paradigm
Adaptive Co-ordinate Transformation Based on a Spike Timing-Dependent Plasticity Learning Paradigm
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DOI：
10.1007/11539087_54
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发表时间：
2005-08
期刊：
影响因子：
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通讯作者：
Qingxiang Wu;T. McGinnity;L. Maguire;A. Belatreche;B. Glackin
中科院分区：
文献类型：
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作者：
Qingxiang Wu;T. McGinnity;L. Maguire;A. Belatreche;B. Glackin
一个尖峰神经网络（SNN）模型训练尖峰定时相关的可塑性（STDP），提出了执行一个2D坐标变换的极坐标表示的手臂位置的笛卡尔表示，以创建一个虚拟的触觉输入的图像映射。触觉输入的位置用于使用STDP来训练SNN，使得在学习之后，SNN可以执行坐标变换以生成具有与视觉图像相同的坐标的触觉输入的表示。这一原理可以应用于人工智能系统中处理生物刺激的复杂坐标变换。
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.