THE DYNAMICS OF HEBBIAN SYNAPSES CAN BE STABILIZED BY A NONLINEAR DECAY TERM

被引：4

作者：

RIEDEL, H ^{[1
]}

SCHILD, D ^{[1
]}

机构：

[1] UNIV GOTTINGEN,INST PHYSIOL,HUMBOLDTALLEE 23,W-3400 GOTTINGEN,GERMANY

来源：

NEURAL NETWORKS | 1992年 / 5卷 / 03期

关键词：

HEBBIAN LEARNING RULES; NORMALIZATION; STABILIZATION;

D O I：

10.1016/0893-6080(92)90007-6

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In the field of neural networks, the dynamics of the connections (synapses) between neurones are often assumed to follow Hebb's rule. The corresponding differential equations are, however, unstable; stability is usually achieved by introducing a subsequent step of normalization or saturation. Here we propose a nonlinear decay term in the equation describing the dynamics of synaptical strengths. With this term the dynamics of synapses becomes intrinsically stable and subsequent normalization is not needed.

引用

页码：459 / 463

页数：5

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