BP Neural Networks Approach for Identifying Biological Signal Source in Circadian Data Fluctuations
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- CISSE Youssouf
- the Dept. of Electr. And Electro. Eng., University of Tokushima
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- KINOUCHI Yohsuke
- the Dept. of Electr. And Electro. Eng., University of Tokushima
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- NAGASHINO Hirofumi
- the Dept. of Electr. And Electro. Eng., University of Tokushima
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- AKUTAGAWA Masatake
- School of Medical Sciences, University of Tokushima
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説明
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.
収録刊行物
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- IEICE transactions on information and systems
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IEICE transactions on information and systems 85 (3), 568-576, 2002-03-01
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詳細情報 詳細情報について
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- CRID
- 1571135652465713280
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- NII論文ID
- 110003219943
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- NII書誌ID
- AA10826272
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- ISSN
- 09168532
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- 本文言語コード
- en
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- データソース種別
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- CiNii Articles
