A multitaper, causal decomposition for stochastic, multivariate time series: Application to high-frequency calcium imaging data

Andrew T. Sornborger; James D. Lauderdale

doi:10.1109/ACSSC.2016.7869531

A multitaper, causal decomposition for stochastic, multivariate time series: Application to high-frequency calcium imaging data

Andrew T. Sornborger, James D. Lauderdale

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Neural data analysis has increasingly incorporated causal information to study circuit connectivity. Dimensional reduction forms the basis of most analyses of large multivariate time series. Here, we present a new, multitaper-based decomposition for stochastic, multivariate time series that acts on the covariance of the time series at all lags, C (τ), as opposed to standard methods that decompose the time series, X(t), using only information at zero-lag. In both simulated and neural imaging examples, we demonstrate that methods that neglect the full causal structure may be discarding important dynamical information in a time series.

Original language	English
Title of host publication	Conference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016
Editors	Michael B. Matthews
Publisher	IEEE Computer Society
Pages	1056-1060
Number of pages	5
ISBN (Electronic)	9781538639542
DOIs	https://doi.org/10.1109/ACSSC.2016.7869531
State	Published - 1 Mar 2017
Externally published	Yes
Event	50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016 - Pacific Grove, United States Duration: 6 Nov 2016 → 9 Nov 2016

Publication series

Name	Conference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)	1058-6393

Conference

Conference	50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016
Country/Territory	United States
City	Pacific Grove
Period	6/11/16 → 9/11/16

Keywords

Causality
Dimension Reduction
Matrix Decomposition
Multitaper Methods
Multivariate Time Series
Neural Imaging
Spectral Analysis

Access to Document

10.1109/ACSSC.2016.7869531

Cite this

Sornborger, A. T., & Lauderdale, J. D. (2017). A multitaper, causal decomposition for stochastic, multivariate time series: Application to high-frequency calcium imaging data. In M. B. Matthews (Ed.), Conference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016 (pp. 1056-1060). Article 7869531 (Conference Record - Asilomar Conference on Signals, Systems and Computers). IEEE Computer Society. https://doi.org/10.1109/ACSSC.2016.7869531

Sornborger, Andrew T. ; Lauderdale, James D. / A multitaper, causal decomposition for stochastic, multivariate time series : Application to high-frequency calcium imaging data. Conference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016. editor / Michael B. Matthews. IEEE Computer Society, 2017. pp. 1056-1060 (Conference Record - Asilomar Conference on Signals, Systems and Computers).

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abstract = "Neural data analysis has increasingly incorporated causal information to study circuit connectivity. Dimensional reduction forms the basis of most analyses of large multivariate time series. Here, we present a new, multitaper-based decomposition for stochastic, multivariate time series that acts on the covariance of the time series at all lags, C (τ), as opposed to standard methods that decompose the time series, X(t), using only information at zero-lag. In both simulated and neural imaging examples, we demonstrate that methods that neglect the full causal structure may be discarding important dynamical information in a time series.",

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Sornborger, AT & Lauderdale, JD 2017, A multitaper, causal decomposition for stochastic, multivariate time series: Application to high-frequency calcium imaging data. in MB Matthews (ed.), Conference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016., 7869531, Conference Record - Asilomar Conference on Signals, Systems and Computers, IEEE Computer Society, pp. 1056-1060, 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016, Pacific Grove, United States, 6/11/16. https://doi.org/10.1109/ACSSC.2016.7869531

A multitaper, causal decomposition for stochastic, multivariate time series: Application to high-frequency calcium imaging data. / Sornborger, Andrew T.; Lauderdale, James D.
Conference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016. ed. / Michael B. Matthews. IEEE Computer Society, 2017. p. 1056-1060 7869531 (Conference Record - Asilomar Conference on Signals, Systems and Computers).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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KW - Dimension Reduction

KW - Matrix Decomposition

KW - Multitaper Methods

KW - Multivariate Time Series

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Sornborger AT, Lauderdale JD. A multitaper, causal decomposition for stochastic, multivariate time series: Application to high-frequency calcium imaging data. In Matthews MB, editor, Conference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016. IEEE Computer Society. 2017. p. 1056-1060. 7869531. (Conference Record - Asilomar Conference on Signals, Systems and Computers). doi: 10.1109/ACSSC.2016.7869531

A multitaper, causal decomposition for stochastic, multivariate time series: Application to high-frequency calcium imaging data

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Keywords

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