Mouse visual cortex as a limited resource system that self-learns an ecologically-general representation
Fig 4
Neural predictivity vs. model depth.
A model’s median neural predictivity across all units from each visual area is plotted against its depth (number of linear-nonlinear layers; in log-scale). Models with fewer linear-nonlinear layers can achieve neural predictivity performances that outperform or are on par with those of models with many more linear-nonlinear layers. The “Primate Model Baseline” denotes a supervised VGG16 trained on 224 px inputs, used in prior work [14, 15, 36].