Sparse component analysis: Finding separable computations in neural population activity

For our next lab-internal journal club, I will present a recent *Neuron* paper by Zimnik et al. (2026) on sparse component analysis (SCA), a method that uncovers separable computations within neural population activity. I liked the paper a lot because it is conceptually aligned with a real neuroscientific question: If computations are flexibly composed, what should their latent factors look like?…