Adaptive Restructuring of Radial Basis Functions Using Integrate-and-Fire Neurons

被引：0
|
作者：
Marvel, Jeremy A. [1 ]
机构：
[1] NIST, Gaithersburg, MD 20899 USA
来源：
2014 13TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA) | 2014年
关键词：
radial basis functions; neural networks; feed-forward networks; machine learning; SYNAPTIC PLASTICITY;
D O I：
10.1109/ICMLA.2014.35
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper proposes a neurobiology-based extension of integrate-and-fire models of Radial Basis Function Neural Networks (RBFNN) that adapts to novel stimuli by means of dynamic restructuring of the network's structural parameters. The new architecture automatically balances synapses modulation, re-centers hidden Radial Basis Functions (RBFs), and stochastically shifts parameter-space decision planes to maintain homeostasis. Example results are provided throughout the paper to illustrate the effects of changes to the RBFNN model.
引用
收藏
页码：189 / 194
页数：6
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