https://hal.inria.fr/hal-02383643Chossat, PascalPascalChossatCOMUE UCA - COMUE Université Côte d'Azur (2015-2019)MATHNEURO - Mathématiques pour les Neurosciences - CRISAM - Inria Sophia Antipolis - Méditerranée - Inria - Institut National de Recherche en Informatique et en AutomatiqueCNRS - Centre National de la Recherche ScientifiqueThe hyperbolic model for edge and texture detection in the primary visual cortexHAL CCSD2020Primary visual cortexneural field equationstexture perceptionstructure tensorpattern formationhyperbolic geometry[MATH.MATH-DS] Mathematics [math]/Dynamical Systems [math.DS][SCCO.NEUR] Cognitive science/NeuroscienceChossat, Pascal2019-11-27 21:18:322023-03-15 08:58:092019-11-28 09:31:40enJournal articleshttps://hal.inria.fr/hal-02383643/document10.1186/s13408-020-0079-yapplication/pdf1The modeling of neural fields in the visual cortex involves geometrical structures which describe in mathematical formalism the functional architecture of this cortical area. The case of contour detection and orientation tuning has been extensively studied and has become a paradigm for the mathematical analysis of image processing by the brain. Ten years ago an attempt was made to extend these models by replacing orientation (an angle) with a second-order tensor built from the gradient of the image intensity, and it was named the structure tensor. This assumption does not follow from biological observations (experimental evidence is still lacking) but from the idea that the effectiveness of texture processing with the structure tensor in computer vision may well be exploited by the brain itself. The drawback is that in this case the geometry is not Euclidean but hyperbolic instead, which complicates the analysis substantially. The purpose of this review is to present the methodology that was developed in a series of papers to investigate this quite unusual problem, specifically from the point of view of tuning and pattern formation. These methods, which rely on bifurcation theory with symmetry in the hyperbolic context, might be of interest for the modeling of other features such as color vision or other brain functions.