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"]]

[["dc.bibliographiccitation.firstpage","e1009028"],["dc.bibliographiccitation.issue","6"],["dc.bibliographiccitation.journal","PLoS Computational Biology"],["dc.bibliographiccitation.volume","17"],["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","2021-08-12T07:45:37Z"],["dc.date.available","2021-08-12T07:45:37Z"],["dc.date.issued","2021"],["dc.description.abstract","Divisive normalization (DN) is a prominent computational building block in the brain that has been proposed as a canonical cortical operation. Numerous experimental studies have verified its importance for capturing nonlinear neural response properties to simple, artificial stimuli, and computational studies suggest that DN is also an important component for processing natural stimuli. However, we lack quantitative models of DN that are directly informed by measurements of spiking responses in the brain and applicable to arbitrary stimuli. Here, we propose a DN model that is applicable to arbitrary input images. We test its ability to predict how neurons in macaque primary visual cortex (V1) respond to natural images, with a focus on nonlinear response properties within the classical receptive field. Our model consists of one layer of subunits followed by learned orientation-specific DN. It outperforms linear-nonlinear and wavelet-based feature representations and makes a significant step towards the performance of state-of-the-art convolutional neural network (CNN) models. Unlike deep CNNs, our compact DN model offers a direct interpretation of the nature of normalization. By inspecting the learned normalization pool of our model, we gained insights into a long-standing question about the tuning properties of DN that update the current textbook description: we found that within the receptive field oriented features were normalized preferentially by features with similar orientation rather than non-specifically as currently assumed."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2021"],["dc.identifier.doi","10.1371/journal.pcbi.1009028"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/88509"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-448"],["dc.relation.eissn","1553-7358"],["dc.relation.orgunit","Institut für Informatik"],["dc.rights","CC BY 4.0"],["dc.title","Learning divisive normalization in primary visual cortex"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]