Ecker, Alexander S.

[["dc.contributor.author","Burg, Max F."],["dc.contributor.author","Cadena, Santiago A."],["dc.contributor.author","Denfield, George H."],["dc.contributor.author","Walker, Edgar Y."],["dc.contributor.author","Tolias, Andreas S."],["dc.contributor.author","Bethge, Matthias"],["dc.contributor.author","Ecker, Alexander S."],["dc.date.accessioned","2020-03-18T13:47:52Z"],["dc.date.available","2020-03-18T13:47:52Z"],["dc.date.issued","2020"],["dc.description.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."],["dc.identifier.doi","10.1101/767285"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/63360"],["dc.language.iso","en"],["dc.title","Learning Divisive Normalization in Primary Visual Cortex"],["dc.type","preprint"],["dc.type.internalPublication","no"],["dspace.entity.type","Publication"]]