Learning divisive normalization in primary visual cortex
Fig 7
Size-tuning in silico experiments and spatially extended DN control models.
A. inset: Prediction of the best DN model (chosen by validation set accuracy) for all neurons to gratings of increasing size. The gratings’ properties were determined from the units’ optimally stimulating Gabor pattern. As grating diameter increased, only very few neurons showed mostly weak suppression. Predictions normalized to maximum response per neuron. Suppression index measures asymptotic suppression relative to the maximum prediction A. main panel: Across all neurons and the ten best DN models (chosen by validation set accuracy), almost no neurons show significant surround suppression. B. Test set performance of the ten best performing DN models. The model’s performance rapidly decreases for spatially increasing normalization pool size (in units of visual angle in degrees). The best model on the validation set is indicated by a blue dot. C. & D. Weights of the spatial normalization pool for the best performing model with pool size of (C.) 1.06° of visual field (5 px × 5 px) and (D.) 1.34° of visual field (7 px × 7 px; all evaluated in terms of the validation set accuracy). For each feature (columns), the two components (rows) of the in total 32 spatial normalization pools are shown. Darker color corresponds to higher weights. Both components are similar. B. insets: Average across features and normalization pool components. The model learned normalization from the receptive field center (on average).