title: A mechanistic cortical microcircuit of attention for amplification, normalization and suppression.
abstract: Computational models of visual attention have replicated a large number of data from visual attention experiments. However, typically each computational model has been shown to account for only a few data sets. We developed a novel model of attention, particularly focused on explaining single cell recordings in multiple brain areas, to better understand the underlying computational circuits of attention involved in spatial- and feature-based biased competition, modulation of the contrast response function, modulation of the neuronal tuning curve, and modulation of surround suppression. In contrast to previous models, we use a two layer structure inspired by the layered cortical architecture which implements amplification, divisive normalization and suppression as well as spatial pooling.
authors.affiliation: Chemnitz University of Technology, Artificial Intelligence, Strasse der Nationen 62, D-09111 Chemnitz, Germany. Electronic address: beuth@cs.tu-chemnitz.de.
authors.forename: Frederik
authors.initials: F
authors.lastname: Beuth
authors.affiliation: Chemnitz University of Technology, Artificial Intelligence, Strasse der Nationen 62, D-09111 Chemnitz, Germany. Electronic address: fred.hamker@informatik.tu-chemnitz.de.
authors.forename: Fred H
authors.initials: FH
authors.lastname: Hamker
mesh_terms: D001288:Attention / Q000502:physiology*; D003198:Computer Simulation; D006801:Humans; D008433:Mathematics; D008959:Models, Neurological; D009434:Neural Pathways / Q000502:physiology*; D014793:Visual Cortex / Q000502:physiology*; D014796:Visual Perception / Q000502:physiology*
keywords: Attention models; Biased competition; Contrast vs. response gain; Scaling vs. sharpening of tuning curves; Surround suppression