Speaker
Description
Perceptual sensitivity in the visual domain pertains to the ability to detect informative features in visual scenes. As per efficient coding framework, this is achieved by becoming tuned to the statistical regularities of its environment. These regularities can be described as local correlations in light distribution, known as multipoint correlations. Previous studies characterized natural scene statistics using synthetic textures that allows control over different multipoint correlation, called Maximum Entropy Textures (MET). Human observers display sensitivity ranking that closely matches the variability of these statistics in natural scenes, thus consistent with the efficient coding. Similar ranking signatures have subsequently been reported in rodents and chicks. Despite this evidence, the underlying neural computations remain poorly understood.
To address this, we constructed a null model of early visual processing to determine how much of the observed phenomenon can be explained before invoking specialized higher-order mechanisms. We model simple-cells with Gabor functions, complex-cells with energy models, using these tractable computations as a benchmark. Responses to the same MET stimuli are analysed with a linear decoder to quantify the information available. We compare simple cells, complex cells, and mixed populations to evaluate their respective contributions.
Preliminary results indicate that populations of V1-like populations already contain substantial information about higher-order image statistics and largely reproduce qualitative trends reported experimentally. Secondly, matching receptive field properties reported in rodents further pointed towards the necessary parameters. While 1-point and 2-point correlations are readily decoded, sensitivity to higher-order statistics depends strongly on receptive field sizes, population size, and the number of training instances. These findings establish a computational baseline for investigating where and how sensitivity to higher-order image statistics emerges in the visual hierarchy and provide a modular platform for other studies involving V1.