Demixed principal component analysis of neural population data

• DOI: 10.7554/elife.10989
• Publication Date: 2016-04-12T11:31:15Z
• Authors: Dmitry Kobak; Wieland Brendel; Christos Constant...; Claudia E Feierstein; Adam Kepecs; Zachary F Mainen; Xue-Lian Qi; Ranulfo Romo; Naoshige Uchida; Christian K Machens
• Keywords: Representation

Neurons in higher cortical areas, such as the prefrontal cortex, are often tuned to a variety of sensory and motor variables, therefore said display mixed selectivity. This complexity single neuron responses can obscure what information these areas represent how it is represented. Here we demonstrate advantages new dimensionality reduction technique, demixed principal component analysis (dPCA), that decomposes population activity into few components. In addition systematically capturing majority variance data, dPCA also exposes dependence neural representation on task parameters stimuli, decisions, or rewards. To illustrate our method reanalyze data from four datasets comprising different species, experimental tasks. each case, provides concise way visualizing summarizes task-dependent features response figure.