Oscillatory model of attention-guided object selection and novelty detection

被引：41

作者：

Borisyuk, RA

Kazanovich, YB

机构：

[1] Univ Plymouth, Ctr Theoret & Computat Neurosci, Plymouth PL4 8AA, Devon, England

[2] Russian Acad Sci, Inst Math Problems Biol, Pushchino 142290, Moscow Region, Russia

来源：

NEURAL NETWORKS | 2004年 / 17卷 / 07期

基金：

英国生物技术与生命科学研究理事会;

关键词：

oscillatory neural networks; synchronization; resonance; selective attention; novelty detection;

D O I：

10.1016/j.neunet.2004.03.005

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We develop a new oscillatory model that combines consecutive selection of objects and discrimination between new and familiar objects. The model works with visual information and fulfils the following operations: (1) separation of different objects according to their spatial connectivity: (2) consecutive selection of objects located in the visual field into the attention focus; (3) extraction of features; (4) representation of objects in working memory; (5) novelty detection of objects. The functioning of the model is based on two main principles: the synchronization of oscillators through phase-locking and resonant increase of the amplitudes of oscillators if they work in-phase with other oscillators. The results of computer simulation of the model are described for visual stimuli representing printed words. (C) 2004 Elsevier Ltd. All rights reserved.

引用

页码：899 / 915

页数：17

共 95 条

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