2010年
Bayesian estimation of phase response curves.
Neural Networks
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- 巻
- 23
- 号
- 6
- 開始ページ
- 752
- 終了ページ
- 763
- 記述言語
- 英語
- 掲載種別
- 研究論文(学術雑誌)
- DOI
- 10.1016/j.neunet.2010.04.002
- 出版者・発行元
- PERGAMON-ELSEVIER SCIENCE LTD
Phase response curve (PRC) of an oscillatory neuron describes the response of the neuron to external perturbation. The PRC is useful to predict synchronized dynamics of neurons; hence, its measurement from experimental data attracts increasing interest in neural science. This paper introduces a Bayesian method for estimating PRCs from data, which allows for the correlation of errors in explanatory and response variables of the PRC. The method is implemented with a replica exchange Monte Carlo technique; this avoids local minima and enables efficient calculation of posterior averages. A test with artificial data generated by the noisy Morris-Lecar equation shows that the proposed method outperforms conventional regression that ignores errors in the explanatory variable. Experimental data from the pyramidal cells in the rat motor cortex is also analyzed with the method; a case is found where the result with the proposed method is considerably different from that obtained by conventional regression.
- リンク情報
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- DOI
- https://doi.org/10.1016/j.neunet.2010.04.002
- DBLP
- https://dblp.uni-trier.de/rec/journals/nn/NakaeITFA10
- J-GLOBAL
- https://jglobal.jst.go.jp/detail?JGLOBAL_ID=201002208383733122
- PubMed
- https://www.ncbi.nlm.nih.gov/pubmed/20466516
- Web of Science
- https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000280024900011&DestApp=WOS_CPL
- URL
- https://www.wikidata.org/entity/Q43064314
- URL
- https://dblp.uni-trier.de/db/journals/nn/nn23.html#NakaeITFA10
- Scopus
- https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=77953808536&origin=inward
- Scopus Citedby
- https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=77953808536&origin=inward
- ID情報
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- DOI : 10.1016/j.neunet.2010.04.002
- ISSN : 0893-6080
- DBLP ID : journals/nn/NakaeITFA10
- J-Global ID : 201002208383733122
- PubMed ID : 20466516
- SCOPUS ID : 77953808536
- Web of Science ID : WOS:000280024900011