Demixed principal component analysis of neural population data
- Champalimaud Centre for the Unknown, Portugal
- Wake Forest University School of Medicine, United States
- Cold Spring Harbor Laboratory, United States
- Universidad Nacional Autónoma de México, Mexico
- Harvard University, United States
Abstract
Neurons in higher cortical areas, such as the prefrontal cortex, are often tuned to a variety of sensory and motor variables, and are therefore said to display mixed selectivity. This complexity of single neuron responses can obscure what information these areas represent and how it is represented. Here we demonstrate the advantages of a new dimensionality reduction technique, demixed principal component analysis (dPCA), that decomposes population activity into a few components. In addition to systematically capturing the majority of the variance of the data, dPCA also exposes the dependence of the neural representation on task parameters such as stimuli, decisions, or rewards. To illustrate our method we reanalyze population data from four datasets comprising different species, different cortical areas and different experimental tasks. In each case, dPCA provides a concise way of visualizing the data that summarizes the task-dependent features of the population response in a single figure.
Article and author information
Author details
Reviewing Editor
- Mark CW van Rossum, University of Edinburgh, United Kingdom
Version history
- Received: August 19, 2015
- Accepted: April 7, 2016
- Accepted Manuscript published: April 12, 2016 (version 1)
- Version of Record published: May 31, 2016 (version 2)
Copyright
© 2016, Kobak et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Demixed principal component analysis of neural population data
eLife 5:e10989.
https://doi.org/10.7554/eLife.10989
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