Augmented Gaussian process variational autoencoders for multi-modal experimental data
Rabia Gondur, Evan Schaffer, Mikio Aoi, Stephen Keeley · COSYNE 2023
Characterizing the relationship between neural population activity and behavioral data is a central goal of neuroscience. While latent variable models (LVMs) are successful in describing high dimensional data, they are typically only designed for a single type of data, making it difficult to identify structure shared across different experimental data modalities. Here, we address this shortcoming by proposing an LVM which extracts shared and independent latents for distinct, simultaneously recorded experimental modalities. We do this by combining Gaussian Process Factor Analysis (GPFA), an int
Sun, Mar 12, 2023