Storing Sequences in Binary Tournament-Based Neural Networks

被引：18

作者：

Jiang, Xiaoran ^{[1
,2
]}

Gripon, Vincent ^{[1
,2
]}

Berrou, Claude ^{[1
,2
]}

Rabbat, Michael ^{[3
]}

机构：

[1] Telecom Bretagne, Dept Elect, F-29238 Brest, France

[2] Lab Sci & Technol Informat Commun & Knowledge, F-29238 Brest, France

[3] McGill Univ, Dept Elect & Comp Engn, Montreal, PQ H3A 0G4, Canada

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | 2016年 / 27卷 / 05期

基金：

欧洲研究理事会;

关键词：

Associative memory; directed graph; information theory; redundancy; sequential memory; sparse coding; MEMORY SEQUENCES; ANTICIPATION; RECALL; MODEL;

D O I：

10.1109/TNNLS.2015.2431319

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

An extension to a recently introduced architecture of clique-based neural networks is presented. This extension makes it possible to store sequences with high efficiency. To obtain this property, network connections are provided with orientation and with flexible redundancy carried by both spatial and temporal redundancies, a mechanism of anticipation being introduced in the model. In addition to the sequence storage with high efficiency, this new scheme also offers biological plausibility. In order to achieve accurate sequence retrieval, a double-layered structure combining heteroassociation and autoassociation is also proposed.

引用

页码：913 / 925

页数：13

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