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 encoding studies explore the relationships between measurements ofneural activity and measurements of a behavior that is viewed as a response tothat activity. The coupling between neural and behavioral measurements istypically imperfect and difficult to measure.To enhance our ability tounderstand neural encoding relationships, we propose that a behavioralmeasurement may be decomposable as a sum of two latent components, such thatthe direct neural influence and prediction is primarily localized to thecomponent which encodes temporal dependence. For this purpose, we propose touse a non-separable Kronecker sum covariance model to characterize thebehavioral data as the sum of terms with exclusively trial-wise, andexclusively temporal dependencies. We then utilize a corrected form of Lassoregression in combination with the nodewise regression approach for estimatingthe conditional independence relationships between and among variables for eachcomponent of the behavioral data, where normality is necessarily assumed. Weprovide the rate of convergence for estimating the precision matricesassociated with the temporal as well as spatial components in the Kronecker summodel. We illustrate our methods and theory using simulated data, and data froma neural encoding study of hawkmoth flight; we demonstrate that the neuralencoding signal for hawkmoth wing strokes is primarily localized to a latentcomponent with temporal dependence, which is partially obscured by a secondcomponent with trial-wise dependencies.

Non-separable covariance models for spatio-temporal data, with applications to neural encoding analysis