Sparse component analysis: A method that uncovers separable computations within neural population activity

In many neural populations, the computationally relevant signals are posited to be a set of "latent factors"-signals shared across many individual neurons. A given brain area may perform many computations, each associated with distinct factors, which can together compose an overall action. The methods for uncovering such structure typically require supervision, which can limit the discovery of novel aspects of activity. Here, we introduce sparse component analysis (SCA), an unsupervised approach. SCA facilitated surprisingly clear parcellations of neural activity across a range of behaviors, when seeking both linear and nonlinear embeddings. We applied SCA to motor cortex activity from reaching and cycling monkeys, single-trial imaging data from C. elegans, and activity from a multitask artificial network. SCA revealed both simple and unexpected instances where the overall population response was built compositionally from sets of factors with distinct computational roles.