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

@article{Sornborger2016AMC, 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}, year={2016}, pages={1056-1060} }

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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