Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity
We consider the problem of extracting smooth, low-dimensional neural trajecto- ries that summarize the activity recorded simultaneously from tens to hundreds of neurons on individual experimental trials. Current methods for extracting neural trajectories involve a two-stage process: the data are first “denoised” by smooth- ing over time, then a static dimensionality reduction technique is applied. We first describe extensions of the two-stage methods that allow the degree of smoothing to be chosen in a principled way, and account for spiking variability that may vary both across neurons and across time. We then present a novel method for extract- ing neural trajectories, Gaussian-process factor analysis (GPFA), which unifies the smoothing and dimensionality reduction operations in a common probabilis- tic framework. We applied these methods to the activity of 61 neurons recorded simultaneously in macaque premotor and motor cortices during reach planning and execution. By adopting a goodness-of-fit metric that measures how well the activity of each neuron can be predicted by all other recorded neurons, we found that GPFA provided a better characterization of the population activity than the two-stage methods.