Biology subjects

Buffet, T.

Publications and source records attributed to Buffet, T..

4 recordsLinked to original sources

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↗

bioRxiv2025

The Rod Bipolar Cell Pathway Contributes To Surround Responses In OFFRetinal Ganglion Cells

Sensory neurons can be influenced by stimuli beyond their receptive field center, yet the mechanisms underlying this surround modulation remain poorly understood. In the retina, many OFF ganglion cells exhibit responses to ON stimulation outside their receptive field center. However, disentangling the pathways and cell types contributing to these responses has been challenging with traditional experimental approaches. Here, we combined optogenetics, two-photon holographic stimulation, and multi-electrode array recordings to identify the intermediate retinal cell types involved in this circuit. We found that the pathway formed by rod bipolar cells and AII amacrine cells--one of the primary relays of rod-driven signals under low-light conditions--plays a key role in mediating this surround modulation. Specifically, crossover inhibition exploits the same amacrine cells responsible for surround suppression to disinhibit distant ganglion cells. This suggests that the retina repurposes existing circuits for surround modulation, optimizing resources through multifunctional inhibitory pathways.

neuroscience↗

bioRxiv2024

Strong, but not weak, noise correlations are beneficial for population coding

Neural correlations play a critical role in sensory information coding. They are of two kinds: signal correlations, when neurons have overlapping sensitivities, and noise correlations from network effects and shared noise. It is commonly thought that stimulus and noise correlations should have opposite signs to improve coding. However, experiments from early sensory systems and cortex typically show the opposite effect, with many pairs of neurons showing both types of correlations to be positive and large. Here, we develop a theory of information coding by correlated neurons which resolves this paradox. We show that noise correlations are always beneficial if they are strong enough. Extensive tests on retinal recordings under different visual stimuli confirm our predictions. Finally, using neuronal recordings and modeling, we show that for high dimensional stimuli noise correlation benefits the encoding of fine-grained details of visual stimuli, at the expense of large-scale features, which are already well encoded.

neuroscience↗

bioRxiv2023

Temporal pattern recognition in retinal ganglion cells is mediated by dynamical inhibitory synapses

A fundamental task for the brain is to generate predictions of future sensory inputs, and signal errors in these predictions. Many neurons have been shown to signal omitted stimuli during periodic stimulation, even in the retina. However, the mechanisms of this error signaling are unclear. Here we show that depressing inhibitory synapses enable the retina to signal an omitted stimulus in a flash sequence. While ganglion cells, the retinal output, responded to an omitted flash with a constant latency over many frequencies of the flash sequence, we found that this was not the case once inhibition was blocked. We built a simple circuit model and showed that depressing inhibitory synapses were a necessary component to reproduce our experimental findings. We also generated new predictions with this model, that we confirmed experimentally. Depressing inhibitory synapses could thus be a key component to generate the predictive responses observed in many brain areas.

neuroscience↗