Lab Meeting 3/25/2013: Demixing PCA (dPCA)

If you had lots of spike trains over 4 seconds for 800 neurons, 6 stimulus conditions, and 2 behavioral choices, how would you visualize your data? Unsupervised dimensionality reduction techniques, such as principal component analysis (PCA) finds orthonormal basis vectors that captures the most variance of the data, but the results are not necessarily interpretable. What one wants is to say is something like:

“Along this direction, the population dynamics seems to encode stimulus, and along this other orthogonal dimension, neurons are modulated by the motor behavior…”

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