Spatial and color hallucinations in a mathematical model of primary visual cortex

Abstract

We study a simplified model of the representation of colors in the primate primary cortical visual area V1. The model is described by an initial value problem related to a Hammerstein equation. The solutions to this problem represent the variation of the activity of populations of neurons in V1 as a function of space and color. The two space variables describe the spatial extent of the cortex while the two color variables describe the hue and the saturation represented at every location in the cortex. We prove the well-posedness of the initial value problem. We focus on its stationary, i.e. independent of time, and periodic in space solutions. We show that the model equation is equivariant with respect to the direct product G of the group of the Euclidean transformations of the planar lattice determined by the spatial periodicity and the group of color transformations, isomorphic to O(2), and study the equivariant bifurcations of its stationary solutions when some parameters in the model vary. Their variations may be caused by the consumption of drugs and the bifurcated solutions may represent visual hallucinations in space and color. Some of the bifurcated solutions can be determined by applying the Equivariant Branching Lemma (EBL) by determining the axial subgroups of G . These define bifurcated solutions which are invariant under the action of the corresponding axial subgroup. We compute analytically these solutions and illustrate them as color images. Using advanced methods of numerical bifurcation analysis we then explore the persistence and stability of these solutions when varying some parameters in the model. We conjecture that we can rely on the EBL to predict the existence of patterns that survive in large parameter domains but not to predict their stability. On our way we discover the existence of spatially localized stable patterns through the phenomenon of "snaking".

Pattern forming mechanisms of color vision

“…Finally, we emphasize that the mechanism we offer departs from previous combinatorial color models that predict hue sensation at the final stage of processing (De Valois & De Valois, 1993;Mehrani et al, 2020), as well as neural field models that conflate cone-and coloropponency in their interpretations (Faugeras, Song, & Veltz, 2022;Smirnova, Chizhkova, & Chizhov, 2015;Song, Faugeras, & Veltz, 2019). The emergent hue tuning curves we have characterized are a network property reflective of the physiological neuronal responses, and should not be confounded with our perception of hue.…”