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

Many studies of neural activity in behaving animals aim to discoverinterpretable low-dimensional structure in large-scale neural populationrecordings. One approach to this problem is demixed principal componentanalysis (dPCA), a supervised linear dimensionality reduction technique to findcomponents that depend on particular experimental parameters. Here, I introducekernel dPCA (kdPCA) as a nonlinear extension of dPCA by applying kernelleast-squares regression to the demixing problem. I consider simulated examplesof neural populations with low-dimensional activity to compare the componentsrecovered from dPCA and kdPCA. These simulations demonstrate that neurallyrelevant nonlinearities, such as stimulus-dependent gain and rotation,interfere with linear demixing of neural activity into components thatrepresent to individual experimental parameters. However, kdPCA can stillrecover interpretable components in these examples. Finally, I demonstratekdPCA using two examples of neural populations recorded during perceptualdecision-making tasks.

Nonlinear demixed component analysis for neural population data as a low-rank kernel regression problem