Hyperbolic planforms in relation to visual edges and textures perception
Pascal Chossat, Olivier Faugeras

TL;DR
This paper introduces a hyperbolic geometric framework for modeling visual edge and texture perception in the brain, predicting characteristic neural patterns that could be observed via imaging to reveal invariances in neural organization.
Contribution
It extends classical neural models to hyperbolic geometry, predicting hyperbolic planforms in neural activity related to visual perception.
Findings
Prediction of hyperbolic planforms in neural activity patterns
Extension of the ring model to hyperbolic geometry
Theoretical link between neural invariances and hyperbolic symmetry
Abstract
We propose to use bifurcation theory and pattern formation as theoretical probes for various hypotheses about the neural organization of the brain. This allows us to make predictions about the kinds of patterns that should be observed in the activity of real brains through, e.g. optical imaging, and opens the door to the design of experiments to test these hypotheses. We study the specific problem of visual edges and textures perception and suggest that these features may be represented at the population level in the visual cortex as a specific second-order tensor, the structure tensor, perhaps within a hypercolumn. We then extend the classical ring model to this case and show that its natural framework is the non-Euclidean hyperbolic geometry. This brings in the beautiful structure of its group of isometries and certain of its subgroups which have a direct interpretation in terms of…
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