Characterization of Visual Object Representations in Rat Primary Visual Cortex

Abstract

For most animal species, quick and reliable identification of visual objects is critical for survival. This applies also to rodents, which, in recent years, have become increasingly popular models of visual functions. For this reason in this work we analyzed how various properties of visual objects are represented in rat primary visual cortex (V1). The analysis has been carried out through supervised (classification) and unsupervised (clustering) learning methods. We assessed quantitatively the discrimination capabilities of V1 neurons by demonstrating how photometric properties (luminosity and object position in the scene) can be derived directly from the neuronal responses.

Intrinsic dynamics enhance temporal stability of stimulus representation along rodent visual cortical hierarchies

“…These frameworks suggest that deeper areas, carrying untangled representations of objects, should support better discrimination by a linear classifier, as demonstrated in several monkey studies (Hong et al 2016;Hung et al 2005;Li et al 2009;Rust and Dicarlo 2010). However, in rats, lower-order areas along the hierarchy have been shown to encode prominent low-level features (such as luminance and contrast) (Tafazoli et al 2017;Vascon et al 2018). This leads to higher absolute discriminability for scenes where, as in our stimuli, such features are not matched (Tafazoli et al 2017).…”

Rat sensitivity to multipoint statistics is predicted by efficient coding of natural scenes

“…For 1-point correlations we chose positive intensity values because they yield patterns that are brighter than white noise. Since previous work from our group has shown that rat V1 neurons are very sensitive to increases of luminance (Tafazoli et al 2017;Vascon et al 2019), our choice ensured that 1-point textures were highly distinguishable from white noise (as indeed observed in our data; see Figure 2B-C), which was the key requirement for our benchmark statistic. This enabled us to guard against issues in our task design: if the animals had failed to discriminate 1-point textures, this would have suggested an overall inadequacy of the behavioral task rather than a lack of perceptual sensitivity to luminance changes.…”

“…As for the 2-point statistic, we selected one of the two gliders (the horizontal one) that yielded the largest sensitivity in humans, so as to include in our stimulus set at least an instance of both the most discriminable (2-point -) and least discriminable (3-point θ ¬ ) textures. In addition, we also tested the 1-point statistic because, given the well-established sensitivity of the rat visual system to luminance changes (Minini and Jeffery 2006;Tafazoli et al 2017;Vascon et al 2019; Vermaercke and Op de Beeck 2012), performance with this statistic served as a useful benchmark against which to compare rat discrimination of the other, more complex textures. Finally, while in Hermundstad et al 2014, both positive and negative values of the statistics were probed against white noise, here we tested only one side of the texture intensity axis (either positive, for 1-, 2-and 4-point configurations, or negative, for 3-point ones) -again, with the goal of limiting the number of rats used in the experiment (see Methods for more details on the rationale behind the choice of statistics and their polarity, and see Discussion for an assessment of the possible impact of these choices on our conclusions).…”

“…We note also that we decided to test the γ statistic, even though this was omitted by Hermundstad et al 2014 (as explained in that paper, the method used to assess the variability of all other multipoint correlation patterns in natural images can't be applied to γ by construction, because the binarization threshold used for images is such that γ = 0 for all images in the dataset). The reason for including γ was that it provided a useful control on the effectiveness of our experimental design, as (unlike for the other visual patterns) we expected rats to be able to easily discriminate stimuli differing by average luminosity (Minini and Jeffery 2006;Tafazoli et al 2017;Vascon et al 2019;Vermaercke and Op de Beeck 2012). As mentioned in the Discussion, failure of the rats to discriminate 1-point textures would have indicated a likely issue in the design of the task.…”

Rat sensitivity to multipoint statistics is predicted by efficient coding of natural scenes

“…As for the two-point statistic, we selected one of the two gliders (the horizontal one) that yielded the largest sensitivity in humans, so as to include in our stimulus set at least an instance of both the most discriminable (two-point -) and least discriminable (three-point ) textures. In addition, we also tested the one-point statistic because, given the well-established sensitivity of the rat visual system to luminance changes ( Minini and Jeffery, 2006 ; Tafazoli et al, 2017 ; Vascon et al, 2019 ; Vermaercke and Op de Beeck, 2012 ), performance with this statistic served as a useful benchmark against which to compare rat discrimination of the other, more complex textures. Finally, while in Hermundstad et al, 2014 , both positive and negative values of the statistics were probed against white noise, here we tested only one side of the texture intensity axis (either positive, for one-, two-, and four-point configurations, or negative, for three-point ones) — again, with the goal of limiting the number of rats used in the experiment (see Materials and methods for more details on the rationale behind the choice of statistics and their polarity, and see Discussion for an assessment of the possible impact of these choices on our conclusions).…”