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

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.

• Publication year

  2016

• Venue

  Asilomar Conference on Signals, Systems and Computers

• Publication date

  2016-11-01

• Fields of study

  Biology, Physics, Computer Science, Mathematics, Medicine

• Identifiers
  DOI 10.1109/ACSSC.2016.7869531arXiv 1703.05414PMID 28649174PMCID PMC5479311
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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