Evolving spiking neural networks for audiovisual information processing

被引：79

作者：

Wysoski, Simei Gomes ^{[1
]}

Benuskova, Lubica ^{[1
,2
]}

Kasabov, Nikola ^{[1
]}

机构：

[1] Auckland Univ Technol, Knowledge Engn & Discovery Res Inst, Auckland 1051, New Zealand

[2] Univ Otago, Dept Comp Sci, Dunedin, New Zealand

来源：

NEURAL NETWORKS | 2010年 / 23卷 / 07期

关键词：

Spiking neural network; Audio and visual pattern recognition; Face recognition; Speaker authentication; Online classification; PATTERN-RECOGNITION; OBJECT RECOGNITION; NEURONS; FACE; COMPUTATION; INTEGRATION; MODEL;

D O I：

10.1016/j.neunet.2010.04.009

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents a new modular and integrative sensory information system inspired by the way the brain performs information processing, in particular, pattern recognition. Spiking neural networks are used to model human-like visual and auditory pathways. This bimodal system is trained to perform the specific task of person authentication. The two unimodal systems are individually tuned and trained to recognize faces and speech signals from spoken utterances, respectively. New learning procedures are designed to operate in an online evolvable and adaptive way. Several ways of modelling sensory integration using spiking neural network architectures are suggested and evaluated in computer experiments. (C) 2010 Elsevier Ltd. All rights reserved.

引用

页码：819 / 835

页数：17

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