Identifying Interpretable Latent Factors with Sparse Component Analysis
Andrew J. Zimnik, K. Cora Ames, Xinyue An, Laura N. Driscoll, Antonio H. Lara, Abigail A. Russo, Vladislav Susoy, John Patrick Cunningham, Liam Paninski, Mark M. Churchland, Joshua I. Glaser
This paper introduces SCA, an unsupervised method for identifying interpretable latent factors in neural data, and shows its effectiveness across different datasets.
bioRxiv · Feb 5, 2024 · PreprintNeuroscience