AI RESEARCH
Learning Coupled Subspaces for Multi-Condition Spike Data
arXiv CS.LG
•
ArXi:2410.19153v2 Announce Type: replace In neuroscience, numerous studies conduct sensory or behavioral experiments under multiple conditions to acquire neural responses in the form of high-dimensional spike train datasets. Analyzing high-dimensional spike data is a challenging statistical problem. To this end, Gaussian process factor analysis (GPFA), a popular class of latent variable models, has been proposed for data collected under a single experimental condition. GPFA extracts smooth, low-dimensional latent trajectories that summarize highdimensional spike datasets.