Efficient processing of sensory data requires adapting the neuronal encoding strategy to the statistics of natural stimuli. Previously, Multiple studies showed that local multipoint correlation patterns that are most variable in natural images are also the most perceptually salient for human observers, in a way that is compatible with the efficient coding principle. Understanding the neuronal mechanisms underlying such adaptation to image statistics will require performing invasive experiments that are impossible in humans. Therefore, it is important to understand whether a similar phenomenon can be detected in animal species that allow for powerful experimental manipulations, such as rodents. To understand if rats and humans are similarly adapted to process the multipoint correlations found in natural images, we selected four image statistics (from 1- to 4-point correlations) and trained four groups of rats to discriminate between white noise patterns and binary textures containing variable intensity levels of one of such statistics. An ideal observer model of the task explained well the behavioral performance of individual rats and allowed us to infer their sensitivity to the statistics they were tested on. Results confirmed our hypothesis as we found that rat sensitivity matches human sensitivity and the variability of the statistics in natural images. Building on the results of the first project, we decided to investigate the neuronal substrate behind the processing of multipoint correlations in rat visual cortex. We were interested in testing in which area of the rat equivalent of the ventral stream these statistics are encoded and discriminated from noise, and what differences may emerge between texture representations in primary visual cortex (V1) and the highest level area of the hierarchy (area LL). To answer these questions, we performed extracellular recordings on anaesthetized rats in V1 and LL. Animals were presented textures containing the same statistics already investigated in the behavioral study with varying levels of intensity. Current results don’t show any clear tuning in neither area for higher-order correlations (3- and 4-point ones), but reveal some key differences between the two areas in terms of their ability to support discrimination of textures defined by 1- and 2-point correlations – with LL displaying a higher degree of invariance (i.e., tolerance to variations across texture exemplars), especially in the case of 2-point correlations.

A study of rat perceptual sensitivity to multipoint correlations and their representation in the visual cortex / Caramellino, Riccardo. - (2023 Jan 23).

A study of rat perceptual sensitivity to multipoint correlations and their representation in the visual cortex

Caramellino, Riccardo
2023-01-23

Abstract

Efficient processing of sensory data requires adapting the neuronal encoding strategy to the statistics of natural stimuli. Previously, Multiple studies showed that local multipoint correlation patterns that are most variable in natural images are also the most perceptually salient for human observers, in a way that is compatible with the efficient coding principle. Understanding the neuronal mechanisms underlying such adaptation to image statistics will require performing invasive experiments that are impossible in humans. Therefore, it is important to understand whether a similar phenomenon can be detected in animal species that allow for powerful experimental manipulations, such as rodents. To understand if rats and humans are similarly adapted to process the multipoint correlations found in natural images, we selected four image statistics (from 1- to 4-point correlations) and trained four groups of rats to discriminate between white noise patterns and binary textures containing variable intensity levels of one of such statistics. An ideal observer model of the task explained well the behavioral performance of individual rats and allowed us to infer their sensitivity to the statistics they were tested on. Results confirmed our hypothesis as we found that rat sensitivity matches human sensitivity and the variability of the statistics in natural images. Building on the results of the first project, we decided to investigate the neuronal substrate behind the processing of multipoint correlations in rat visual cortex. We were interested in testing in which area of the rat equivalent of the ventral stream these statistics are encoded and discriminated from noise, and what differences may emerge between texture representations in primary visual cortex (V1) and the highest level area of the hierarchy (area LL). To answer these questions, we performed extracellular recordings on anaesthetized rats in V1 and LL. Animals were presented textures containing the same statistics already investigated in the behavioral study with varying levels of intensity. Current results don’t show any clear tuning in neither area for higher-order correlations (3- and 4-point ones), but reveal some key differences between the two areas in terms of their ability to support discrimination of textures defined by 1- and 2-point correlations – with LL displaying a higher degree of invariance (i.e., tolerance to variations across texture exemplars), especially in the case of 2-point correlations.
Zoccolan, Davide Franco
Caramellino, Riccardo
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11767/130850
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