Kobak, D., Brendel, W., Constantinidis, C., Feierstein, C. E., Kepecs, A., Mainen, Z. F., Romo, R., Qi, X. L., Uchida, N., Machens, C. K. (April 2016) Demixed principal component analysis of neural population data. Elife, 5. ISSN 2050-084X (Electronic)2050-084X (Linking)
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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.
| Item Type: | Paper |
|---|---|
| Subjects: | organs, tissues, organelles, cell types and functions > cell types and functions > cell types > neurons organs, tissues, organelles, cell types and functions > cell types and functions > cell types > neurons organs, tissues, organelles, cell types and functions > cell types and functions > cell types > neurons organs, tissues, organelles, cell types and functions > tissues types and functions > prefrontal cortex |
| CSHL Authors: | |
| Communities: | CSHL labs > Kepecs lab |
| Depositing User: | Matt Covey |
| Date: | 12 April 2016 |
| Date Deposited: | 26 Apr 2016 16:19 |
| Last Modified: | 03 Nov 2017 18:44 |
| PMCID: | PMC4887222 |
| Related URLs: | |
| URI: | https://repository.cshl.edu/id/eprint/32621 |
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