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

Sparse sequences of neural spikes are posited to underlie aspects of workingmemory, motor production, and learning. Discovering these sequences in anunsupervised manner is a longstanding problem in statistical neuroscience.Promising recent work utilized a convolutive nonnegative matrix factorizationmodel to tackle this challenge. However, this model requires spike times to bediscretized, utilizes a sub-optimal least-squares criterion, and does notprovide uncertainty estimates for model predictions or estimated parameters. Weaddress each of these shortcomings by developing a point process model thatcharacterizes fine-scale sequences at the level of individual spikes andrepresents sequence occurrences as a small number of marked events incontinuous time. This ultra-sparse representation of sequence events opens newpossibilities for spike train modeling. For example, we introduce learnabletime warping parameters to model sequences of varying duration, which have beenexperimentally observed in neural circuits. We demonstrate these advantages onexperimental recordings from songbird higher vocal center and rodenthippocampus.

Point process models for sequence detection in high-dimensional neural spike trains