  Neurons in higher cortical areas, such as the prefrontal cortex, are known to
be tuned to a variety of sensory and motor variables. The resulting diversity
of neural tuning often obscures the represented information. Here we introduce
a novel dimensionality reduction technique, demixed principal component
analysis (dPCA), which automatically discovers and highlights the essential
features in complex population activities. We reanalyze population data from
the prefrontal areas of rats and monkeys performing a variety of working memory
and decision-making tasks. In each case, dPCA summarizes the relevant features
of the population response in a single figure. The population activity is
decomposed into a few demixed components that capture most of the variance in
the data and that highlight dynamic tuning of the population to various task
parameters, such as stimuli, decisions, rewards, etc. Moreover, dPCA reveals
strong, condition-independent components of the population activity that remain
unnoticed with conventional approaches.
