Learning Divisive Normalization in Primary Visual Cortex

Abstract

Deep convolutional neural networks (CNNs) have emerged as the state of the art for predicting neural activity in visual cortex. While such models outperform classical linear-nonlinear and wavelet-based representations, we currently do not know what computations they approximate. Here, we tested divisive normalization (DN) for its ability to predict spiking responses to natural images. We developed a model that learns the pool of normalizing neurons and the magnitude of their contribution end-to-end from data. In macaque primary visual cortex (V1), we found that our interpretable model outperformed linear-nonlinear and wavelet-based feature representations and almost closed the gap to high-performing black-box models. Surprisingly, within the classical receptive field, oriented features were normalized preferentially by features with similar orientations rather than non-specifically as currently assumed. Our work provides a new, quantitatively interpretable and high-performing model of V1 applicable to arbitrary images, refining our view on gain control within the classical receptive field.