Figure 15. | Demixed principal component analysis of neural population data

Open accessCopyright infoDownload PDF

Demixed principal component analysis of neural population data

Figure 15.

Affiliation details

Champalimaud Centre for the Unknown, Portugal; École Normale Supérieure, France; Centre for Integrative Neuroscience, University of Tübingen, Germany; Wake Forest University School of Medicine, United States; Cold Spring Harbor Laboratory, United States; Universidad Nacional Autónoma de México, Mexico; El Colegio Nacional, Mexico; Harvard University, United States
Figure 15.
Download figureOpen in new tabFigure 15. Fourier-like artifacts in PCA.

(Left) In this toy example, single neuron responses are generated from the same underlying Gaussian but are randomly shifted in time. (Right) First three PCA components of the population data. While the leading component resembles the true signal, higher order components look like higher Fourier harmonics. They are artifacts of the jitter in time.

DOI: http://dx.doi.org/10.7554/eLife.10989.020