Topic: Gaussian Process Factor Analysis

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

Calcium imaging (CI) has emerged as a prominent method for recording neural population activity. Therefore, CI recordings are prime candidates for the application of latent variable models designed to identify latent neural states and dynamics. However, in CI experiments spiking events are measured only indirectly via calcium ions that rapidly enter a cell during action potentials and then slowly seep back out. To analyze CI data, per-neuron fluorescence time-traces are typically either de-convolved to approximate spiking events or analyzed directly. The former approach, although enabling many

Identifying how brain regions work together to process inputs and generate behavior is a central goal in systems neuroscience. Recently, there has been a surge of interest in characterizing communication between brain regions and shared variability in multi-region neural recordings. In this work, we use a multi-region latent variable model (LVM) to identify computations shared-across and independent-to specific brain regions in spiking neural population data from two different animals. Here, we use a multi-region latent variable model (LVM) to identify computations shared-across and independen