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

We propose a general statistical framework for clustering multiple timeseries that exhibit nonlinear dynamics into an a-priori-unknown number ofsub-groups. Our motivation comes from neuroscience, where an important problemis to identify, within a large assembly of neurons, subsets that respondsimilarly to a stimulus or contingency. Upon modeling the multiple time seriesas the output of a Dirichlet process mixture of nonlinear state-space models,we derive a Metropolis-within-Gibbs algorithm for full Bayesian inference thatalternates between sampling cluster assignments and sampling parameter valuesthat form the basis of the clustering. The Metropolis step employs recentinnovations in particle-based methods. We apply the framework to clusteringtime series acquired from the prefrontal cortex of mice in an experimentdesigned to characterize the neural underpinnings of fear.

Clustering Time Series with Nonlinear Dynamics: A Bayesian Non-Parametric and Particle-Based Approach