Unlocking neural population non-stationarities using hierarchical dynamics models Unlocking neural population non-stationarities using hierarchical dynamics models
Paper summary 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.
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Unlocking neural population non-stationarities using hierarchical dynamics models
Park, Mijung and Bohner, Gergo and Macke, Jakob H.
Neural Information Processing Systems Conference - 2015 via Bibsonomy
Keywords: dblp


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