Stimulus symmetries can confound representational similarity analyses
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 more 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, ranging from disentangled to maximally-mixed codes. We show that stochastic gradient descent or energetic regularization can generate sparse, drifting codes, leading in turn to driftin...
| Authors | Farhad Pashakhanloo, Jacob A. Zavatone-Veth |
|---|---|
| Primary category | q-bio.NC |
| Published | 20 May 2026 |
| Last updated at source | 2 October 2026 |
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