Unlocking neural population non-stationarities using hierarchical dynamics modelsUnlocking neural population non-stationarities using hierarchical dynamics modelsPark, Mijung and Bohner, Gergo and Macke, Jakob H.2015
Paper summarynipsreviewsThis paper describes using an additional time scale over trials to model (slow) non-stationarities. It adds to the successful PLDS model, another gain vector matching the latent dimensions that is constant during each trial. Many neuroscientific datasets indeed show such slow drifts, which could very well be captured by such modeling effort.
This paper describes using an additional time scale over trials to model (slow) non-stationarities. It adds to the successful PLDS model, another gain vector matching the latent dimensions that is constant during each trial. Many neuroscientific datasets indeed show such slow drifts, which could very well be captured by such modeling effort.