Parametric neural control differentiates top neural network models of primate visual cortex
This study introduces axis-aligned feature accentuation as a causal method to reveal that, despite similar accuracy in predicting natural image responses, leading deep neural network models diverge significantly in their ability to control primate visual cortex firing, with performance better predicted by input gradient spatial frequency structure than by adversarial robustness.