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Plos Computational Biology|November 11, 2024
Teaching deep networks to see shape: Lessons from a simplified visual worldChristian Jarvers, Heiko NeumannPlos One|August 5, 2011
Combining feature selection and integration--a neural model for MT motion selectivityCornelia Beck, Heiko NeumannNeural Networks : the Official Journal of the International Neural Network Society|August 4, 2004
A simple cell model with dominating opponent inhibition for robust image processingThorsten Hansen, Heiko NeumannNeural Computation|April 9, 2004
Neural mechanisms for the robust representation of junctionsThorsten Hansen, Heiko NeumannNeural Networks : the Official Journal of the International Neural Network Society|November 26, 2009
A neural model of the temporal dynamics of figure-ground segregation in motion perceptionFlorian Raudies, Heiko NeumannIEEE Transactions on Pattern Analysis and Machine Intelligence|December 16, 2006
A fast biologically inspired algorithm for recurrent motion estimationPierre Bayerl, Heiko NeumannPlos One|June 16, 2009
Extraction of surface-related features in a recurrent model of V1-V2 interactionsUlrich Weidenbacher, Heiko NeumannBio Systems|February 7, 2007
A neural model of feature attention in motion perceptionPierre Bayerl, Heiko NeumannPlos One|September 21, 2016
Biologically Inspired Model for Inference of 3D Shape from TextureOlman Gomez, Heiko NeumannSpatial Vision|April 29, 2005
Neural mechanisms of human texture processing: texture boundary detection and visual searchAxel Thielscher, Heiko NeumannPageof 10