Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.04.23.057620v1?rss=1 Authors: Lerer, A., Keil, M. S., Super, H. Abstract: T he visual system is highly sensitive to spatial context for encod ing luminance patterns. Context sensitivity inspired the proposal of many neural mechanisms for explaining t he perception of luminance (brightness) . Here we propose a novel computational model for e stimating the brightness of many visual illusions. W e hypothesize that many aspects of brightness can be explained by a predictive coding mechanism which reduces the redundan cy in edge representations on the one hand, while non-redundant activity is enhanced on the other (response equalization). Response equalization is implemented with a dynamic filtering process , which (dynamically) adapts to each input image. Dynamic filtering is applied to the responses of complex cells in order to build a gain control map. The gain control map then acts on simple cell responses before they are used to create a brightness map via activity propagation. Our approach is s uccessful in predicting many challenging visual illusions, including contrast effect s , assimilation, and reverse contrast. Copy rights belong to original authors. Visit the link for more info