ID | 39804 |
Title Transcription | BP ニューラル ネットワーク オ モチイタ サーカディアン リズムゲン ノ システム ドウテイ
|
Title Alternative | BP neural netrworks approach for identifying biological rhythm source in circadian data fluctuations
|
Author |
Nagashino, Hirofumi
Department of Radiologic Science and Engineering, School of Health Sciences, The University of Tokushima
Tokushima University Educator and Researcher Directory
KAKEN Search Researchers
Kinouchi, Yohsuke
Department of Electrical and Electronic Engineering, Faculty of Engineering, The University of Tokushima
Tokushima University Educator and Researcher Directory
KAKEN Search Researchers
Akutagawa, Masatake
Department of Radiologic Science and Engineering, School of Health Sciences, The University of Tokushima
Tokushima University Educator and Researcher Directory
KAKEN Search Researchers
Cisse, Youssouf
Department of Electrical and Electronic Engineering, Faculty of Engineering, The University of Tokushima|Department of Medicine, Faculty of Medicine, Laval University
|
Keywords | circadian rhythms
sleep-wake rhythm
system identification
neural network
moving average process
|
Content Type |
Journal Article
|
Description | Almost all land animals coordinate their behavior with circadian rhythms, matching their functions to the daily cycles of lightness and darkness that result from the rotation of the earth corresponding to 24 hours. Through external stimuli, such as dairy life activities or other sources from our environment may influence the internal rhythmicity of sleep and waking properties. However, the rhythms are regulated to keep their activity constant by homeostasis while fluctuating by incessant influences of external forces. A modeling study has been developed to identify homeostatic dynamics properties underlying a circadian rhythm activity of sleep and wake data measured from normal subjects, using an MA (Moving Average) model associated with backpropagation (BP) algorithm. As a result, we found out that the neural network can capture the regularity and irregularity components included in the data. The order of MA neural network model depends on subject’s behavior. The last two data are usually dominant in the case without strong external forces. The adaptive changes of the dynamics are evaluated by the change of weight vectors, a kind of internal representation of the trained network. The dynamics is kept in a steady state for more than 20 days. Identified properties reflect the subject’s behavior, and hence may be useful for medical diagnoses of disorders related to circadian rhythms.
|
Journal Title |
四国医学雑誌
|
ISSN | 00373699
|
NCID | AN00102041
|
Publisher | 徳島医学会
|
Volume | 59
|
Issue | 6
|
Start Page | 304
|
End Page | 314
|
Sort Key | 304
|
Published Date | 2003-12-25
|
Remark | |
EDB ID | |
FullText File | |
language |
jpn
|
departments |
Science and Technology
Medical Sciences
|