PATTERN-SPECIFIC NEURAL-NETWORK DESIGN

被引：1

作者：

ANDERLE, M ^{[1
]}

SCHWENG, H ^{[1
]}

KURTEN, KE ^{[1
]}

KRATKY, KW ^{[1
]}

机构：

[1] JOHANNES KEPLER UNIV,INST THEORET PHYS,A-4040 LINZ,AUSTRIA

来源：

JOURNAL OF STATISTICAL PHYSICS | 1995年 / 81卷 / 3-4期

关键词：

INFORMATION STORAGE; HOPFIELD MODEL; SPECIFIC CONNECTIVITY;

D O I：

10.1007/BF02179261

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

We present evidence that the performance of the traditional fully connected Hopfield model can be dramatically improved by carefully selecting an information-specific connectivity structure, while the synaptic weights of the selected connections are the same as in the Hopfield model. Starting from a completely disconnected network we let ''genuine'' Hebbian synaptic connections grow, one by one, until a desired degree of stability is achieved. Neural pathways are thus fixed not before, but during the learning phase.

引用

页码：843 / 849

页数：7

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