Competition between feed-forward and lateral information processing in layered neural networks

被引：0
|
作者：
Coolen, ACC
Viana, L
机构：
[1] Kings Coll London, Dept Math, London WC2R 2LS, England
[2] Univ Nacl Autonoma Mexico, Lab Ensenada, Inst Fis, Ensenada, Baja California, Mexico
来源：
BIOLOGICAL AND ARTIFICIAL COMPUTATION: FROM NEUROSCIENCE TO TECHNOLOGY | 1997年 / 1240卷
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：
We use replica theory to analyse layered networks of binary neurons, with lateral Hebbian-type synapses within individual layers, in combination with strictly feed-forward Hebbian-type synapses between successive layers. The competition between two qualitatively different modes of operation, feed-forward versus lateral, induces interesting transitions, and allows for the identification of an optimal value for the balance between the two synapse types.
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页码：257 / 264
页数：8
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