Identifying Interpretable Latent Factors with Sparse Component Analysis.

ABSTRACT: In many neural populations, the computationally relevant signals are posited to be a set of 'latent factors' - signals shared across many individual neurons. Understanding the relationship between neural activity and behavior requires the identification of factors that reflect distinct computational roles. Methods for identifying such factors typically require supervision, which can be suboptimal if one is unsure how (or whether) factors can be grouped into distinct, meaningful sets. Here, we introduce Sparse Component Analysis (SCA), an unsupervised method that identifies interpretable latent factors. SCA seeks factors that are sparse in time and occupy orthogonal dimensions. With these simple constraints, SCA facilitates surprisingly clear parcellations of neural activity across a range

SUBMITTER: Zimnik AJ 

PROVIDER: S-EPMC10871230 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature