vLGP -- variational Latent Gaussian Process

We propose a practical and efficient inference method, called the variational latent Gaussian process (vLGP), that recovers low-dimensional latent dynamics from high-dimensional time series. Our method performs dimensionality reduction on a single trial basis and allows decomposition of neural signals into a small number of smooth temporal signals and their relative contribution to the population signal. By inferring latent neural trajectories on each trial, they provide a flexible framework for studying the internal neural processes that are not time-locked. Higher-order processes such as decision-making, attention, and memory recall are well suited for latent trajectory analysis due to their intrinsic low-dimensionality of computation.

The vLGP combines a generative model with a history-dependent point process observation together with a smoothness prior on the latent trajectories, and improves upon earlier methods for recovering latent trajectories, which assume either observation models inappropriate for point processes or linear dynamics. In the real electrophysiological recordings from the primary visual cortex, we find that vLGP achieves substantially higher performance than previous methods for predicting omitted spike trains, as well as capturing both the toroidal topology of visual stimuli space, and the noise-correlation. These results show that vLGP is a robust method with a potential to reveal hidden neural dynamics from large-scale neural recordings.

Other relevant information
arXiv preprint: https://arxiv.org/abs/1604.03053
video: https://youtu.be/CrY5AfNH1ik