Scaling Properties of Dimensionality Reduction for Neural Populations and Network Models
Fig 8
Modes of shared variability for spiking network models.
(A) Left: Modes of clustered network. Each column of the heatmap is an eigenvector of the shared covariance matrix computed from a set of 500 neurons and 10,000 trials. Columns are ordered with modes explaining the most shared variance on the left. Neurons (rows) are ordered by cluster (black lines indicate cluster boundaries), sorted with the highest mean firing rate clusters at the top. Note that due to random sampling there are an unequal number of neurons in each cluster. (B) Modes of non-clustered network. Same conventions as in (A), except rows are ordered by firing rate of individual neurons, with the highest mean firing rate at the top. The number of dimensions that maximized the cross-validated data likelihood was 100 in (A) and 110 in (B). (C) Percent of shared variance explained by each mode in (A). (D) Percent of shared variance explained by each mode in (B).