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Elife. 2016 Apr 12;5. pii: e10989. doi: 10.7554/eLife.10989.

Demixed principal component analysis of neural population data.

Author information

1
Champalimaud Neuroscience Program, Champalimaud Centre for the Unknown, Lisbon, Portugal.
2
École Normale Supérieure, Paris, France.
3
Centre for Integrative Neuroscience, University of Tübingen, Tübingen, Germany.
4
Wake Forest University School of Medicine, Winston-Salem, United States.
5
Cold Spring Harbor Laboratory, Cold Spring Harbor, United States.
6
Instituto de Fisiología Celular-Neurociencias, Universidad Nacional Autónoma de México, Mexico City, Mexico.
7
El Colegio Nacional, Mexico City, Mexico.
8
Harvard University, Cambridge, 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.

KEYWORDS:

dimensionality reduction; neuroscience; population activity; prefrontal cortex; principal component analysis; rat; rhesus macaque

PMID:
27067378
PMCID:
PMC4887222
DOI:
10.7554/eLife.10989
[Indexed for MEDLINE]
Free PMC Article

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