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Poster E in Poster Session E: Thursday, August 6, 10:30 am – 12:15 pm, Kimmel Center, Shorin & Rosenthal Rooms

Data symmetries can confound representational similarity analyses

Farhad Pashakhanloo¹, Jacob A Zavatone-Veth¹; ¹Harvard University

Presenter: Farhad Pashakhanloo

What can representational similarity matrices (RSMs) tell us about a neural code? As the popularity of these summary statistics grows, so too does the need for a complete characterization of their properties. Here, we show that symmetries in network inputs can confound RSM-based analyses. Stimulus symmetries render many representations functionally equivalent, but these different configurations can lead to different RSMs. These different RSMs reflect qualitatively different representational geometries. We demonstrate this phenomenon in a toy setup as well as networks trained to encode image data, where the symmetry is latent. Our results illustrate the challenges inherent in comparing non-linear neural codes, when functionally-equivalent representations are not related by a simple rotation.

Topic Area: Methods, Tools, Theory & Neural Coding