Poisson Sparse Component Analysis: A method to parse single-trial neural population activity into factors reflecting distinct neural processes

A central aim in systems neuroscience is discovering the distinct neural processes instantiated within the spiking activity of neural populations. Doing so within single trials or unconstrained behaviors presents dual challenges: an analysis method must disentangle the distinct signals mixed within individual neurons, while simultaneously denoising that activity from the stochastic spiking within large populations. To tackle these challenges, we present Poisson Sparse Component Analysis (pSCA), an unsupervised approach for discovering latent factors reflecting distinct neural processes in spiking datasets. pSCA seeks latents that occur sparsely in time and reside in orthogonal subspaces, enabling the decomposition of population activity into signals reflective of distinct neural processes without prior knowledge of these processes’ identity or timing. Through a convolutional autoencoder architecture and masked training procedure, pSCA recovers denoised latent factors that align with identifiable task events and behavioral variables in multiple datasets across mouse and monkey cortex. Without supervision, pSCA finds a decomposition of spiking activity in which simpler components combine to produce more complex behaviors, exposing compositional structures alternative approaches do not yield.