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Contributed Talk Session: Wednesday, August 5, 10:15 – 11:15 am, Skirball Theater
Poster F in Poster Session F: Thursday, August 6, 1:45 – 3:30 pm, Kimmel Center, Shorin & Rosenthal Rooms

Neural Footprints: Variance-Based Methods Systematically Miss High-Level Representations

Jon Gauthier¹, Tyler BrookeWilson²; ¹University of California, San Francisco, ²Yale University

Presenter: Jon Gauthier

The brain computes at vastly different levels of representation, encoding both abstract knowledge and fine-grained sensory information about the world. These two ends of the spectrum have distinct statistical properties: while abstract knowledge about the world is compact and low-dimensional, sensory information is highly variable and difficult to compress. This statistical asymmetry creates fundamental issues for several neuroscience analysis techniques, biasing these methods to favor high-dimensional sensory features over low-dimensional abstract representations. In a simulation experiment, we demonstrate that variance-based techniques fail to distinguish between obviously correct and incorrect models of visual perception due to this statistical asymmetry. Reliable neural analysis therefore requires models that are not merely fit to explain variance in neural activations, but that more deeply link representations to the computations they support.

Topic Area: Methods, Tools, Theory & Neural Coding