Increasing neural network robustness improves match to macaque V1 eigenspectrum, spatial frequency preference and predictivity.

ABSTRACT: Task-optimized convolutional neural networks (CNNs) show striking similarities to the ventral visual stream. However, human-imperceptible image perturbations can cause a CNN to make incorrect predictions. Here we provide insight into this brittleness by investigating the representations of models that are either robust or not robust to image perturbations. Theory suggests that the robustness of a system to these perturbations could be related to the power law exponent of the eigenspectrum of its set of neural responses, where power law exponents closer to and larger than one would indicate a system that is less susceptible to input perturbations. We show that neural responses in mouse and macaque primary visual cortex (V1) obey the predictions of this theory, where their eigenspectra have

SUBMITTER: Kong NCL 

PROVIDER: S-EPMC8775238 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature