Demixed principal component analysis of population activity in higher cortical areas reveals independent representation of task parameters
This provides a method for neuroscientists to better interpret complex neural data in higher cortical areas, though it is incremental as an extension of existing dimensionality reduction techniques.
The authors tackled the problem of obscured information in neural population activity due to diverse tuning by introducing demixed principal component analysis (dPCA), a dimensionality reduction technique that automatically discovers essential features, and applied it to prefrontal cortex data from rats and monkeys, revealing demixed components that capture most variance and highlight dynamic tuning to task parameters.
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.