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
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
收藏
页码:459 / 463
页数:5
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