Differences in non-linearities determine retinal cell types

Trapani, Francesco; Spampinato, Giulia Lia Beatrice; Yger, Pierre; Marre, Olivier

doi:10.1101/2022.05.26.493557

Cited by 3 publications

(3 citation statements)

References 24 publications

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“…7d). Overall, we were able to record responses from all known RGC types, yet the sampling fractions for most types differ between the datasets, which can be explained by the recording bias inherent in MEA (88) (Fig. 7f).…”

Section: Resultsmentioning

confidence: 99%

Nitric oxide modulates contrast suppression in a subset of mouse retinal ganglion cells

Gonschorek,

Goldin,

Oesterle

et al. 2023

Preprint

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Neuromodulators have major influences on the regulation of neural circuit activity across the nervous system. Nitric oxide (NO) is a prominent neuromodulator in many circuits, and has been extensively studied in the retina across species. NO has been associated with the regulation of light adaptation, gain control, and gap junction coupling, but its effect on the retinal out-put, specifically on the different types of retinal ganglion cells (RGCs), is still poorly understood. Here, we used two-photon Ca2+imaging to record RGC responses to visual stimuli to investigate the neuromodulatory effects of NO on the cell type-level in theex vivomouse retina. We found that about one third of the RGC types displayed highly reproducible and cell type-specific response changes during the course of an experiment, even in the absence of NO. Accounting for these adaptational changes allowed us to isolate NO effects on RGC responses. This revealed that NO affected only a few RGC types, which typically became more active due to a reduction of their response suppression. Notably, NO had no discernible effect on their spatial receptive field size and surround strength. Together, our data suggest that NO specifically modulates suppression of the temporal response in a distinct group of contrast-suppressed RGC types. Finally, our study demonstrates the need for recording paradigms that takes adaptational, non-drug-related response changes into account when analysing potentially subtle pharmacological effects.

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Section: Resultsmentioning

confidence: 99%

Nitric oxide modulates contrast suppression in a subset of mouse retinal ganglion cells

Gonschorek,

Goldin,

Oesterle

et al. 2023

Preprint

Self Cite

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show abstract

“…Standard methods for classifying retinal cells types functionally are based on the response to simple stimuli (full field luminance amplitude or frequency variations, moving bars). However, the extension of our methods using latent GPs could classify the cells using more relevant stimuli (natural images), with the addition of providing a model for each cell [30].…”

Section: Discussionmentioning

confidence: 99%

“…Future work could consider a range of different questions. For example, we could choose stimuli such that we can best ascertain which groups of neurons in the retina belong to the same cell-type [29, 30]. Alternatively, in higher level visual areas we could select stimuli so as to best test the effects of top-down processes such as visual attention and expectations.…”

Section: Discussionmentioning

confidence: 99%

Scalable gaussian process inference of neural responses to natural images

Goldin

Virgili

Chalk

2023

Preprint

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Predicting 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.

show abstract

Scalable Gaussian process inference of neural responses to natural images

Goldin

Virgili

Chalk

2023

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

Predicting 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. On the one hand, deep neural networks perform very well on certain datasets but can fail when data are limited. On the other hand, Gaussian processes (GPs) perform well on limited data but are 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 model neural responses to high-dimensional stimuli. With this addition, GPs largely outperform a deep neural network trained to predict retinal responses to natural images, with the largest differences observed when both models are trained on a small dataset. Further, since they allow us to quantify the uncertainty in their predictions, GPs are well suited to closed-loop experiments, where stimuli are chosen actively so as to collect “informative” neural data. We show how GPs can be used to actively select which stimuli to present, so as to i) efficiently learn a model of retinal responses to natural images, using few data, and ii) rapidly distinguish between competing models (e.g., a linear vs. a nonlinear model). In the future, our approach could be applied to other sensory areas, beyond the retina.

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Differences in non-linearities determine retinal cell types

Cited by 3 publications

References 24 publications

Nitric oxide modulates contrast suppression in a subset of mouse retinal ganglion cells

Nitric oxide modulates contrast suppression in a subset of mouse retinal ganglion cells

Scalable gaussian process inference of neural responses to natural images

Scalable Gaussian process inference of neural responses to natural images

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