Pan-retinal characterisation of Light Responses from Ganglion Cells in the Developing Mouse Retina

Abstract

We have investigated the ontogeny of light-driven responses in mouse retinal ganglion cells (RGCs). Using a large-scale, high-density multielectrode array, we recorded from hundreds to thousands of RGCs simultaneously at pan-retinal level, including dorsal and ventral locations. Responses to different contrasts not only revealed a complex developmental profile for ON, OFF and ON-OFF responses, but also unveiled differences between dorsal and ventral RGC responses. At eye-opening, dorsal RGCs of all types were more responsive to light, perhaps indicating an environmental priority to nest viewing for pre-weaning pups. The developmental profile of ON and OFF responses exhibited antagonistic behaviour, with the strongest ON responses shortly after eye-opening, followed by an increase in the strength of OFF responses later on. Further, we found that with maturation receptive field (RF) center sizes decrease, spike-triggered averaged responses to white noise become stronger, and centers become more circular while maintaining differences between RGC types. We conclude that the maturation of retinal functionality is not spatially homogeneous, likely reflecting ecological requirements that favour earlier maturation of the dorsal retina.

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

“…A very reliable criterion is neural morphology, in particular dendritic stratification in the inner plexiform layer. While this is impossible to obtain for all neurons recorded with large scale MEAs, the registration of sparsely labelled neurons with spike sorted data is feasible (20), a method that could be used to augment the clustering obtained with our method. A further criterion to assess the validity of clustered types is regular spatial tiling of each type (35,37), although there are exceptions to this rule (38).…”

“…Spike sorting Spikes were detected and sorted using the algorithms described in (20,29), using the HS2 software (https://github.com/mhhennig/HS2). Briefly, spikes were first detected as threshold crossings individually on each channel, and then merged into unique events based on spatial and temporal proximity.…”

“…The linear receptive field was computed as the spike triggered average (STA) stimulus, obtained during white noise stimulation. To increase the spatial resolution, the stimulus squares were shifted randomly with each presentation, as described by Hilgen et al (20). A bivariate Gaussian was fitted to each STA at the time of the first peak, and its average width taken as the receptive field size.…”

“…A main caveat in previous studies was that RGC recordings from multiple preparations were pooled together to obtain a data set of sufficient size. Large scale, high density microelectrode arrays (MEAs) now make it possible to record large populations comprising thousands of cells from a single retina (18)(19)(20)(21). This reduces contaminating effects of variability between animals and experimental conditions, and allows precise control of stimulation for all recorded neurons.…”

Reconstruction of natural visual scenes from neural spikes with deep neural networks

“…In particular, in the retina, a population of spike trains from hundreds of retinal ganglion cells can be obtained with well-controlled visual scenes, such as images and movies [17,34]. New techniques may record several thousands of neurons simultaneously [49,50,51]. Together with other recent studies [17,25], our results in this work show that a better decoding performance can be archived with neural spikes, which is beyond the scope of fMRI signal [19,20,41].…”