Gaussian Process Factor Analysis (GPFA)

GPFA extracts low-d latent trajectories from noisy, high-d time series data. It combines linear dimensionality reduction (factor analysis) with Gaussian-process temporal smoothing in a unified probabilistic framework. GPFA is particularly useful for exploratory analysis of spike trains recorded simultaneously from multiple neurons on individual experimental trials.

Matlab codepack (version 2.03, 119 kB)

References: Yu et al., J Neurophysiol, 2009; Churchland et al., Nat Neurosci, 2010

Distance Covariance Analysis (DCA)

DCA is a linear dimensionality reduction method that can identify linear and nonlinear relationships between multiple data sets. For example, DCA can identify dimensions of population activity in different brain areas that are related to one another and to stimulus or behavioral variables.

Matlab code (version 1.0, 30 kB)   Python version coming soon!

Reference: Cowley et al., AISTATS, 2017


DataHigh is a Matlab-based graphical user interface to visualize and interact with high-dimensional neural population activity. DataHigh has built-in tools to perform dimensionality reduction on raw spike trains, and includes a suite of visualization tools tailored for neural data analysis.

DataHigh webpage

Reference: Cowley et al., JNE, 2013