Biology subjects

Virgili, S.

Publications and source records attributed to Virgili, S..

6 recordsLinked to original sources

bioRxiv2026

Retinal adaptive mechanisms confer selectivity to homogeneous objects in natural scenes

Adaptive mechanisms in sensory neurons are crucial to transmit information in different contexts. In the retina, it is assumed that their role is to normalize neuronal responses to input statistics like mean and variance. However, this role has mostly been characterized with simple, artificial stimuli, and remains unclear for natural stimuli. Here we show that during their response to natural scenes, adaptive mechanisms reshape the feature selectivity of ganglion cells, the retinal output. We recorded retinal ganglion cell responses to rapid sequences of natural images in mice. Including a bio-inspired adaptive mechanism in an artificial neural network model was necessary to predict cell responses to new sequences of natural images. This adaptive mechanism tuned specific cell types to selectively respond to homogeneous regions situated within cluttered visual surrounds, a feature suited for detecting threats. Adaptive mechanisms do not merely normalize responses but actively enable new feature selectivity in the early visual system.

neuroscience↗

bioRxiv2026

VIP+ amacrine cells synchronize neural activity in the retina

In early sensory circuits, inhibitory interneurons are best known for mediating lateral inhibition, gain control, and feature selectivity, but their roles at the population level are less understood. Using two-photon digital holography, we found that in the retina, vasoactive intestinal peptide-expressing (VIP+) amacrine cells not only inhibit some retinal ganglion cell types via GABAergic synapses but also excite specific other types through gap junctions. Selective knockout of these junctions abolished synchronized firing among these ganglion cells, showing that VIP+ amacrine cells coordinate their activity. Thus, VIP+ interneurons have a dual, cell-type-specific role: synchronizing some ganglion cells electrically while inhibiting others chemically, thereby differentially shaping retinal output.

neuroscience↗

bioRxiv2026

Primitives for motion segmentation in the retina

The center surround structure of ganglion cells receptive fields is suited for edge detection, a first step towards image segmentation. However, in a dynamical visual scene with moving, textured objects, it is less clear how the retina represents the local boundaries of these objects. Here, we show that the spatial selectivity of ganglion cells changes during their responses to a moving object. Specific cell types only respond to contrast changes near the edges of the moving object, while being insensitive to changes in other parts of their receptive field. Using a non-linear model to reproduce this result, we could isolate the mechanism responsible for this selective representation. These types of ganglion cells represent selectively textures only when they are near moving edges, and may thus provide useful primitives for motion segmentation.

neuroscience↗

bioRxiv2024

Nonlinear spatial integration allows the retina to detect the sign of defocus in natural scenes

Eye growth is regulated by the visual input. Many studies suggest that the retina can detect if a visual image is focused in front or behind the back of the eye, and modulate eye growth to bring it back to focus. How can the retina distinguish between these two types of defocus? Here we simulated how eye optics transform natural images and recorded how the isolated retina responds to different types of simulated defocus. We found that some ganglion cell types could distinguish between an image focussed in front or behind the retina, by estimating spatial contrast. Aberrations in the eye optics made spatial contrast, but not luminance, a reliable cue to distinguish these two types of defocus. Our results suggest a mechanism for how the retina can estimate the sign of defocus and provide an explanation for several results aiming at mitigating strong myopia by slowing down eye growth.

neuroscience↗

bioRxiv2023

Scalable gaussian process inference of neural responses to natural images

1Predicting the responses of sensory neurons is a long-standing neuroscience goal. However, while there has been much progress in modeling neural responses to simple and/or artificial stimuli, predicting responses to natural stimuli remains an ongoing challenge. One the one hand, deep neural networks perform very well on certain data-sets, but can fail when data is limited. On the other hand, gaussian processes (GPs) perform well on limited data, but are generally poor at predicting responses to high-dimensional stimuli, such as natural images. Here we show how structured priors, e.g. for local and smooth receptive fields, can be used to scale up GPs to high-dimensional stimuli. We show that when we do this, a GP model largely outperforms a deep neural network trained to predict retinal responses to natural images, with largest differences observed when both models are trained on a very small data-set. Further, since GPs compute the uncertainty in their predictions, they are well-suited to closed-loop experiments, where stimuli are chosen actively so as to collect informative neural data. We show how this can be done in practice on our retinal data-set, so as to: (i) efficiently learn a model of retinal responses to natural images, using little data, and (ii) rapidly distinguish between competing models (e.g. a linear vs a non-linear model). In the future, our approach could be applied to other low-level sensory areas, beyond the retina.

neuroscience↗

bioRxiv2021

Context-dependent selectivity to natural scenes in the retina

Retina ganglion cells extract specific features from natural scenes and send this information to the brain. In particular, they respond to local light increase (ON responses), and/or decrease (OFF). However, it is unclear if this ON-OFF selectivity, characterized with synthetic stimuli, is maintained when they are stimulated with natural scenes. Here we recorded the responses of ganglion cells of mice and axolotls to stimuli composed of natural images slightly perturbed by patterns of random noise to determine their selectivity during natural stimulation. The ON-OFF selectivity strongly depended on the natural image. A single ganglion cell can signal luminance increase for one natural image, and luminance decrease for another. Modeling and experiments showed that this was due to the non-linear combination of different pathways of the retinal circuit. Despite the versatility of the ON-OFF selectivity, a systematic analysis demonstrated that contrast was reliably encoded in these responses. Our perturbative approach thus uncovers the selectivity of retinal ganglion cells to more complex features than initially thought during natural scene stimulation.

neuroscience↗