ROBUSTNESS AND INFORMATION CAPACITY OF LEARNING RULES FOR NEURAL NETWORK MODELS

被引：0

作者：

SCHNELLE, T

ENGEL, A

机构：

[1] Humboldt University, Department of Physics 04, O1040 Berlin

来源：

PHYSICS LETTERS A | 1991年 / 156卷 / 1-2期

关键词：

D O I：

10.1016/0375-9601(91)90128-U

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

Investigating different learning rules concerning their robustness against dilution and static noise we find a clear complementary relationship between robustness and high storage capacity. For models based on the Hebbian rule it is shown that the information capacity becomes maximal if the synapses are restricted to the values (0, +/- 1 ) only.

引用

页码：69 / 75

页数：7

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