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

  title={A multitaper, causal decomposition for stochastic, multivariate time series: Application to high-frequency calcium imaging data},
  author={Andrew T. Sornborger and James D. Lauderdale},
  journal={2016 50th Asilomar Conference on Signals, Systems and Computers},
  • A. Sornborger, J. Lauderdale
  • Published 1 November 2016
  • Computer Science, Mathematics
  • 2016 50th Asilomar Conference on Signals, Systems and Computers
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… 

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