English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Neural data analysis has increasingly incorporated causal information tostudy circuit connectivity. Dimensional reduction forms the basis of mostanalyses of large multivariate time series. Here, we present a new,multitaper-based decomposition for stochastic, multivariate time series thatacts on the covariance of the time series at all lags, $C(\tau)$, as opposed tostandard methods that decompose the time series, $\mathbf{X}(t)$, using onlyinformation at zero-lag. In both simulated and neural imaging examples, wedemonstrate that methods that neglect the full causal structure may bediscarding important dynamical information in a time series.

A Multitaper, Causal Decomposition for Stochastic, Multivariate Time Series: Application to High-Frequency Calcium Imaging Data