Synaptic compensation on Hopfield network: implications for memory rehabilitation

被引：3
|
作者：
Menezes, R. A. [1 ]
Monteiro, L. H. A. [1 ,2 ]
机构：
[1] Univ Presbiteriana Mackenzie, Escola Engn, BR-01302907 Sao Paulo, Brazil
[2] Univ Sao Paulo, Escola Politecn, Dept Engn Telecomunicacoes & Controle, BR-05508900 Sao Paulo, Brazil
来源：
NEURAL COMPUTING & APPLICATIONS | 2011年 / 20卷 / 05期
关键词：
Alzheimer's disease; Hopfield neural network; Memory rehabilitation; Synaptic compensation; NEURAL NETWORKS; PLASTICITY; RECOGNITION; IMPAIRMENT; SYSTEMS; NEURONS;
D O I：
10.1007/s00521-010-0480-7
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The discrete-time neural network proposed by Hopfield can be used for storing and recognizing binary patterns. Here, we investigate how the performance of this network on pattern recognition task is altered when neurons are removed and the weights of the synapses corresponding to these deleted neurons are divided among the remaining synapses. Five distinct ways of distributing such weights are evaluated. We speculate how this numerical work about synaptic compensation may help to guide experimental studies on memory rehabilitation interventions.
引用
收藏
页码：753 / 757
页数：5
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