Supervised learning with spiking neural networks

被引：0
|
作者：
Xin, JG [1 ]
Embrechts, MJ [1 ]
机构：
[1] Rensselaer Polytech Inst, Dept Mech Engn Aeronaut Engn & Mech, Troy, NY 12180 USA
来源：
IJCNN'01: INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-4, PROCEEDINGS | 2001年
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper we derive a supervised learning algorithm for a spiking neural network which encodes information in the timing of spike trains. This algorithm is similar to the classical error back propagation algorithm for sigmoidal neural network but the learning parameter is adaptively changed. The algorithm is applied to the complex nonlinear classification problem and the results show that the spiking neural network is capable of performing nonlinearly separable classification tasks. Several issues concerning the spiking neural network are discussed.
引用
收藏
页码：1772 / 1777
页数：6
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