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

doi:10.1016/j.neuron.2026.05.022

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

2026-07-02

0

OA

AI

DOI：10.1016/j.neuron.2026.05.022

Original

Original request for help

Save

Abstract

En 中文

• Sparse component analysis (SCA) is an unsupervised dimensionality reduction approach • SCA parcellates activity into latent factors corresponding to separate computations • SCA uncovers compositional reuse of latent factors across datasets

Keywords:

dimensionality reduction

population activity

latent factors

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

Abstract

Journal

Organization