scholarly journals Modeling a population of retinal ganglion cells with restricted Boltzmann machines

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Riccardo Volpi ◽  
Matteo Zanotto ◽  
Alessandro Maccione ◽  
Stefano Di Marco ◽  
Luca Berdondini ◽  
...  
Keyword(s):  
Retinal Ganglion Cells ◽  
Latent Variables ◽  
Latent Variable ◽  
Ganglion Cells ◽  
Visual Stimuli ◽  
Retinal Ganglion ◽  
Electrode Arrays ◽  
Restricted Boltzmann Machines ◽  
Boltzmann Machines ◽  
Spiking Activity

Abstract The retina is a complex circuit of the central nervous system whose aim is to encode visual stimuli prior the higher order processing performed in the visual cortex. Due to the importance of its role, modeling the retina to advance in interpreting its spiking activity output is a well studied problem. In particular, it has been shown that latent variable models can be used to model the joint distribution of Retinal Ganglion Cells (RGCs). In this work, we validate the applicability of Restricted Boltzmann Machines to model the spiking activity responses of a large a population of RGCs recorded with high-resolution electrode arrays. In particular, we show that latent variables can encode modes in the RGC activity distribution that are closely related to the visual stimuli. In contrast to previous work, we further validate our findings by comparing results associated with recordings from retinas under normal and altered encoding conditions obtained by pharmacological manipulation. In these conditions, we observe that the model reflects well-known physiological behaviors of the retina. Finally, we show that we can also discover temporal patterns, associated with distinct dynamics of the stimuli.

scholarly journals Voltage-gated calcium channel subunit α2δ-3 shapes light responses of mouse retinal ganglion cells mainly in low and moderate light levels

2020 ◽  
Author(s):  
Hartwig Seitter ◽  
Vithiyanjali Sothilingam ◽  
Boris Benkner ◽  
Marina Garcia Garrido ◽  
Alexandra Kling ◽  
...  
Keyword(s):  
Calcium Channels ◽  
Retinal Ganglion Cells ◽  
Ganglion Cells ◽  
Horizontal Cell ◽  
Gaussian White Noise ◽  
Retinal Ganglion ◽  
Visual Responses ◽  
Electrode Arrays ◽  
Voltage Gated ◽  
Voltage Gated Calcium Channels

AbstractLittle is known about the function of the auxiliary α2δ subunits of voltage-gated calcium channels in the retina. We investigated the role of α2δ-3 (Cacna2d3) using a mouse in which α2δ-3 was knocked out by LacZ insertion. Behavior experiments indicated a normal optokinetic reflex in α2δ-3 knockout animals. Strong expression of α2δ-3 could be localized to horizontal cells using the LacZ-reporter, but horizontal cell mosaic and currents carried by horizontal cell voltage-gated calcium channels were unchanged by the α2δ-3 knockout. In vivo electroretinography revealed unaffected photoreceptor activity and signal transmission to depolarizing bipolar cells. We recorded visual responses of retinal ganglion cells with multi-electrode arrays in scotopic to photopic luminance levels and found subtle changes in α2δ-3 knockout retinas. Spontaneous activity in OFF ganglion cells was elevated in all luminance levels. Differential response strength to high- and low-contrast Gaussian white noise was compressed in ON ganglion cells during mesopic ambient luminance and in OFF ganglion cells during scotopic and mesopic ambient luminances. In a subset of ON ganglion cells, we found a sharp increase in baseline spiking after the presentation of drifting gratings in scotopic luminance. This increase happened after gratings of different spatial properties in knockout compared to wild type retinas. In a subset of ON ganglion cells of the α2δ-3 knockout, we found altered delays in rebound-like spiking to full-field contrast steps in scotopic luminance. In conclusion, α2δ-3 seems to participate in shaping visual responses mostly within brightness regimes when rods or both rods and cones are active.

scholarly journals Genetically Identified Suppressed-by-Contrast Retinal Ganglion Cells Reliably Signal Self-Generated Visual Stimuli

2015 ◽  
Vol 35 (30) ◽  
pp. 10815-10820 ◽  
Author(s):  
N.-W. Tien ◽  
J. T. Pearson ◽  
C. R. Heller ◽  
J. Demas ◽  
D. Kerschensteiner
Keyword(s):  
Retinal Ganglion Cells ◽  
Ganglion Cells ◽  
Visual Stimuli ◽  
Retinal Ganglion

scholarly journals Scalable and accurate automated method for neuronal ensemble detection in spiking neural networks

2020 ◽  
Author(s):  
Rubén Herzog ◽  
Arturo Morales ◽  
Soraya Mora ◽  
Joaquin Araya ◽  
María-José Escobar ◽  
...  
Keyword(s):  
Retinal Ganglion Cells ◽  
Ganglion Cells ◽  
Light Stimulus ◽  
Synthetic Data ◽  
Retinal Ganglion ◽  
Electrode Arrays ◽  
Neuronal Ensembles ◽  
Automated Method ◽  
Wide Range ◽  
Ensemble Activity

AbstractWe propose a novel, scalable, and accurate automated method for detecting neuronal ensembles from a population of spiking neurons. Our approach offers a simple yet powerful tool to study ensemble activity. It allows the participation of neurons in different ensembles, has few parameters to tune and is computationally efficient. We used spike trains of retinal ganglion cells obtained from multi-electrode array recordings under a simple ON-OFF light stimulus to test our method. We found a consistent stimuli-evoked ensemble activity intermingled with spontaneously active ensembles and irregular activity. Our results suggest that the early visual system activity is already organized in clearly distinguishable functional ensembles. To validate the performance and generality of our method, we generated synthetic data, where we found that our method accurately detects neuronal ensembles for a wide range of simulation parameters. Additionally, we found that our method outperforms current alternative methodologies. Finally, we provide a Graphic User Interface, which aims to facilitate our method’s use by the scientific community.Author summaryNeuronal ensembles are strongly interconnected groups of neurons that tend to fire together (Hebb 1949). However, even when this concept was proposed more than 70 years ago, only recent advances in multi-electrode arrays and calcium imaging, statistical methods, and computing power have made it possible to record and analyze multiple neurons’ activities spiking simultaneously, providing a unique opportunity to study how groups of neurons form ensembles spontaneously and under different stimuli scenarios. Using our method, we found that retinal ganglion cells show a consistent stimuli-evoked ensemble activity, and, when validated with synthetic data, the method shows good performance by detecting the number of ensembles, the activation times, and the core-cells for a wide range of firing rates and number of ensembles accurately.

scholarly journals Non-parametric physiological classification of retinal ganglion cells in the mouse retina

2018 ◽  
Author(s):  
Jonathan Jouty ◽  
Gerrit Hilgen ◽  
Evelyne Sernagor ◽  
Matthias H. Hennig
Keyword(s):  
Retinal Ganglion Cells ◽  
Visual Information ◽  
Large Scale ◽  
Distance Measure ◽  
Ganglion Cells ◽  
High Density ◽  
Mouse Retina ◽  
Retinal Ganglion ◽  
Electrode Arrays

Retinal ganglion cells, the sole output neurons of the retina, exhibit surprising diversity. A recent study reported over 30 distinct types in the mouse retina, indicating that the processing of visual information is highly parallelised in the brain. The advent of high density multi-electrode arrays now enables recording from many hundreds to thousands of neurons from a single retina. Here we describe a method for the automatic classification of large-scale retinal recordings using a simple stimulus paradigm and a spike train distance measure as a clustering metric. We evaluate our approach using synthetic spike trains, and demonstrate that major known cell types are identified in high-density recording sessions from the mouse retina with around 1000 retinal ganglion cells. A comparison across different retinas reveals substantial variability between preparations, suggesting pooling data across retinas should be approached with caution. As a parameter-free method, our approach is broadly applicable for cellular physiological classification in all sensory modalities.

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