IEICE TRANSACTIONS on Information
BP Neural Networks Approach for Identifying Biological Signal Source in Circadian Data Fluctuations
Summary :
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 results, we found that the neural network can capture the regularity and irregularity components included in the data. The order of MA neural network model depends on subjects behavior, the first two orders are usually dominant in the case of no strong external forces. The adaptive dynamic changes are evaluated by the change of weight vectors, a kind of internal representation of the trained network. The dynamic is kept in a steady state for more than 20 days at most. Identified properties reflect the subject's behavior, and hence may be useful for medical diagnoses of disorders related to circadian rhythms.
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- IEICE TRANSACTIONS on Information Vol.E85-D No.3 pp.568-576
- Publication Date
- 2002/03/01
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- PAPER
- Category
- Biocybernetics, Neurocomputing
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Youssouf CISSE, Yohsuke KINOUCHI, Hirofumi NAGASHINO, Masatake AKUTAGAWA, "BP Neural Networks Approach for Identifying Biological Signal Source in Circadian Data Fluctuations" in IEICE TRANSACTIONS on Information,
vol. E85-D, no. 3, pp. 568-576, March 2002, doi: .
Abstract: 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 results, we found that the neural network can capture the regularity and irregularity components included in the data. The order of MA neural network model depends on subjects behavior, the first two orders are usually dominant in the case of no strong external forces. The adaptive dynamic changes are evaluated by the change of weight vectors, a kind of internal representation of the trained network. The dynamic is kept in a steady state for more than 20 days at most. Identified properties reflect the subject's behavior, and hence may be useful for medical diagnoses of disorders related to circadian rhythms.
URL: https://global.ieice.org/en_transactions/information/10.1587/e85-d_3_568/_p
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@ARTICLE{e85-d_3_568,
author={Youssouf CISSE, Yohsuke KINOUCHI, Hirofumi NAGASHINO, Masatake AKUTAGAWA, },
journal={IEICE TRANSACTIONS on Information},
title={BP Neural Networks Approach for Identifying Biological Signal Source in Circadian Data Fluctuations},
year={2002},
volume={E85-D},
number={3},
pages={568-576},
abstract={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 results, we found that the neural network can capture the regularity and irregularity components included in the data. The order of MA neural network model depends on subjects behavior, the first two orders are usually dominant in the case of no strong external forces. The adaptive dynamic changes are evaluated by the change of weight vectors, a kind of internal representation of the trained network. The dynamic is kept in a steady state for more than 20 days at most. Identified properties reflect the subject's behavior, and hence may be useful for medical diagnoses of disorders related to circadian rhythms.},
keywords={},
doi={},
ISSN={},
month={March},}
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TY - JOUR
TI - BP Neural Networks Approach for Identifying Biological Signal Source in Circadian Data Fluctuations
T2 - IEICE TRANSACTIONS on Information
SP - 568
EP - 576
AU - Youssouf CISSE
AU - Yohsuke KINOUCHI
AU - Hirofumi NAGASHINO
AU - Masatake AKUTAGAWA
PY - 2002
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E85-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 2002
AB - 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 results, we found that the neural network can capture the regularity and irregularity components included in the data. The order of MA neural network model depends on subjects behavior, the first two orders are usually dominant in the case of no strong external forces. The adaptive dynamic changes are evaluated by the change of weight vectors, a kind of internal representation of the trained network. The dynamic is kept in a steady state for more than 20 days at most. Identified properties reflect the subject's behavior, and hence may be useful for medical diagnoses of disorders related to circadian rhythms.
ER -
English
