Demixed principal component analysis of population activity in higher cortical areas reveals independent representation of task parameters

TL;DR

This paper introduces demixed principal component analysis (dPCA), a new method for disentangling complex neural population activity into independent components related to different task parameters, revealing hidden, condition-independent signals.

Contribution

The paper presents dPCA, a novel dimensionality reduction technique that automatically isolates task-relevant features in neural data, improving interpretability of population activity in higher cortical areas.

Findings

dPCA effectively summarizes population responses in a single figure.

It uncovers dynamic tuning to stimuli, decisions, and rewards.

It reveals strong, condition-independent neural components.

Abstract

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.