Selectivity to oriented patterns of different precisions

Abstract

The selectivity of the visual system to oriented patterns is very well documented in a wide range of species, especially in mammals. In particular, neurons of the primary visual cortex are anatomically grouped by their preference to a given oriented visual stimulus. Interactions between such groups of neurons have been successfully modeled using recurrently-connected network of spiking neurons, so called "ring models". Nonetheless, this selectivity is most often studied with crystal-like patterns such as gratings. Here, we studied the ability of human observers to discriminate texture-like patterns for which we could quantitatively tune the precision of their oriented content and we propose a generic model to explain such results. The first contribution shows that the discrimination threshold as a function of the precision did not vary smoothly as would be expected, but more in a binary, "all or none" fashion. Our second contribution is to propose a novel model of orientation selectivity that is based on deep-learning techniques, which performance we evaluated in the same task. This model has human-like performance in term of accuracy and exhibits qualitatively similar psychophysical curves. One hypothesis that such a structure allows for the system to be robust to noise in its visual inputs.

Hugo Ladret

Phd in Computational Neuroscience

My PhD subject focused on the role of precision in natural and artificial neural networks.

Laurent U Perrinet

Researcher in Computational Neuroscience

My research interests include Machine Learning and computational neuroscience applied to Vision.