Sparse Component Analysis: An interpretable dimensionality reduction tool that identifies building blocks of neural computation
Joshua Glaser, Andrew Zimnik, Vladislav Susoy, Liam Paninski, John Cunningham, Mark Churchland · COSYNE 2023
All behaviors, even simple actions, result from the interplay of multiple neural computations, and understanding how a given behavior is generated requires delineating these computations from one another. Traditionally, identifying these critical ‘computational building blocks’ requires a researcher to impose structure on the data: specific task epochs are analyzed separately or specific population-level structure is sought using supervised dimensionality reduction methods. While these approaches can be successful, they have obvious drawbacks – many behaviors cannot be easily parsed into disti
Sat, Mar 11, 2023