Characterizing Retinal Ganglion Cell Responses to Electrical Stimulation Using Generalized Linear Models

Sekhar, Sudarshan; Ramesh, Poornima; Bassetto, Giacomo; Zrenner, Eberhart; Macke, Jakob H.; Rathbun, Daniel L.

doi:10.3389/fnins.2020.00378

Cited by 7 publications

(10 citation statements)

References 34 publications

(46 reference statements)

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“…The kernel obtained in (30) is the response of the RG cell to a Dirac pulse corresponding, in experiments, to a brief light (or dark) full-field flash. It can also obtained from a white noise stimulus, corresponding, in experiments, to the so-called Spike Triggered Average (STA) [38][39][40].…”

Section: Discussionmentioning

confidence: 99%

“…The structure of the retina and its behaviour are thus well studied on the experimental side. There are comparatively fewer modelling studies although important work has been done on retinal coding [20][21][22][23][24], biophysically detailed models [25][26][27] and generalized linear models applied to retinal coding [28][29][30]. Several powerful software has been designed to model the retina at different scales such as COREM [31], Convis [32], Isetbio https://github.com/isetbio/isetbio/wiki (accessed on 22 November 2021).…”

Section: Introductionmentioning

confidence: 99%

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Retinal Processing: Insights from Mathematical Modelling

Cessac

2022

J. Imaging

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The retina is the entrance of the visual system. Although based on common biophysical principles, the dynamics of retinal neurons are quite different from their cortical counterparts, raising interesting problems for modellers. In this paper, I address some mathematically stated questions in this spirit, discussing, in particular: (1) How could lateral amacrine cell connectivity shape the spatio-temporal spike response of retinal ganglion cells? (2) How could spatio-temporal stimuli correlations and retinal network dynamics shape the spike train correlations at the output of the retina? These questions are addressed, first, introducing a mathematically tractable model of the layered retina, integrating amacrine cells’ lateral connectivity and piecewise linear rectification, allowing for computing the retinal ganglion cells receptive field together with the voltage and spike correlations of retinal ganglion cells resulting from the amacrine cells networks. Then, I review some recent results showing how the concept of spatio-temporal Gibbs distributions and linear response theory can be used to characterize the collective spike response to a spatio-temporal stimulus of a set of retinal ganglion cells, coupled via effective interactions corresponding to the amacrine cells network. On these bases, I briefly discuss several potential consequences of these results at the cortical level.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Retinal Processing: Insights from Mathematical Modelling

Cessac

2022

J. Imaging

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“…The fact that predictive retinal models perform worse for RCS under electrical stimulation than for LE retina under visual stimulation is not surprising and can be explained by several factors, including the increased spontaneous firing rate in the degenerate retina [ 25 , 26 ], likely due to higher uptake of retinoic acid [ 27 ]. There is evidence that even in healthy mice retinas, intrinsic variability of the RGCs response to network-mediated electrical activation is higher than with natural visual stimulation [ 28 ]. Replicability and reliability of the network-mediated stimulation also decreases with retinal rewiring during degeneration [ 29 ].…”

Section: Discussionmentioning

confidence: 99%

Decoding network-mediated retinal response to electrical stimulation: implications for fidelity of prosthetic vision

Shmakov

Palanker

2020

J. Neural Eng.

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Objective. Patients with the photovoltaic subretinal implant PRIMA demonstrated letter acuity by ~0.1 logMAR worse than the sampling limit for 100μm pixels (1.3 logMAR) and performed slower than healthy subjects, which exceeded the sampling limit at equivalently pixelated images by ~0.2 logMAR. To explore the underlying differences between the natural and prosthetic vision, we compare the fidelity of the retinal response to visual and subretinal electrical stimulation through single-cell modeling and ensemble decoding.Approach. Responses of the retinal ganglion cells (RGC) to optical or electrical (1mm diameter arrays, 75μm pixels) white noise stimulation in healthy and degenerate rat retinas were recorded via MEA. Each RGC was fit with linearnon-linear (LN) and convolutional neural network (CNN) models. To characterize RGC noise level, we compared statistics of the spike-triggered average (STA) in RGCs responding to electrical or visual stimulation of healthy and degenerate retinas. At the population level, we constructed a linear decoder to determine the certainty with which the ensemble of RGCs can support the N-way discrimination tasks.Main results. Although LN and CNN models can match the natural visual responses pretty well (correlation ~0.6), they fit significantly worse to spike timings elicited by electrical stimulation of the healthy retina (correlation ~0.15). In the degenerate retina, response to electrical stimulation is equally bad. The signal-to-noise ratio of electrical STAs in degenerate retinas matched that of the natural responses when 78±6.5% of the spikes were replaced with random timing. However, the noise in RGC responses contributed minimally to errors in the ensemble decoding. The determining factor in accuracy of decoding was the number of responding cells. To compensate for fewer responding cells under electrical stimulation than in natural vision, larger number of presentations of the same stimulus are required to deliver sufficient information for image decoding.Significance. Slower than natural pattern identification by patients with the PRIMA implant may be explained by the lower number of electrically activated cells than in natural vision, which is compensated by a larger number of the stimulus presentations.

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“…The voltage of RG cells is sent through a non linearity (pink curve in the black circle) so as to produce spike trains conveyed to the LGN. physically detailed models [26,27,28], Generalized Linear Models applied to retinal coding [29,30,31]. Several powerful software has been designed to model the retina at different scales such as COREM [32], Convis [33], Isetbio https: //github.com/isetbio/isetbio/wiki.…”

Section: Introductionmentioning

confidence: 99%

Retinal processing: insights from mathematical modelling

Cessac

2022

Preprint

View full text Add to dashboard Cite

The retina is the entrance of the visual system. Although based on common biophysical principles the dynamics of retinal neurons is quite different from their cortical counterparts, raising interesting problems for modellers. In this paper I address some mathematically stated questions in this spirit, discussing, in particular: (1) How could lateral amacrine cell connectivity shape the spatio-temporal spike response of retinal ganglion cells ? (2) How could spatio-temporal stimuli correlations and retinal network dynamics shape the spike train correlations at the output of the retina ? These questions are addressed, first, introducing a mathematically tractable model of the layered retina, integrating amacrine cells lateral connectivity and piecewise linear rectification, allowing to compute the retinal ganglion cells receptive field together with the voltage and spike correlations of retinal ganglion cells resulting from the amacrine cells networks. Then, I review some recent results showing how the concept of spatio-temporal Gibbs distributions and linear response theory can be used to characterize the collective spike response to a spatio-temporal stimulus of a set of retinal ganglion cells, coupled via effective interactions corresponding to the amacrine cells network. On these bases, I briefly discuss several potential consequences of these results at the cortical level.

show abstract

Characterizing Retinal Ganglion Cell Responses to Electrical Stimulation Using Generalized Linear Models

Cited by 7 publications

References 34 publications

Retinal Processing: Insights from Mathematical Modelling

Retinal Processing: Insights from Mathematical Modelling

Decoding network-mediated retinal response to electrical stimulation: implications for fidelity of prosthetic vision

Retinal processing: insights from mathematical modelling

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