Learning what we know and knowing what we learn: Gaussian process priors for neural data analysis

Guillaume Hennequin, Kris Jensen - University of Cambridge Colab notebooks: Introduction to FA and GPFA as probabilistic generative models: https://colab.research.google.com/dri... Fitting an example data set from a primate reaching task with GPFA: https://colab.research.google.com/dri... Additional papers and resources Rasmussen & Williams (2006) http://www.gaussianprocess.org/gpml/ The standard textbook for Gaussian processes. David Duvenaud’s kernel cookbook https://www.cs.toronto.edu/~duvenaud/... An overview of different covariance functions commonly used for Gaussian processes. Rutten et al. (2020) https://proceedings.neurips.cc/paper/... Primary reference for Gaussian process factor analysis with dynamical structure (GPFADS) Jensen & Kao et al. (2021) https://www.biorxiv.org/content/10.11... Primary reference for Bayesian GPFA Jensen et al. (2020) https://proceedings.neurips.cc/paper/... Extension of Gaussian process latent variable models to non-Euclidean manifolds Nieh et al. (2021) https://www.nature.com/articles/s4158... Demonstration that hippocampus encodes additional latent structure as well as position in an evidence accumulation task