Influence of retinal ganglion cell morphology on neuronal response properties - a simulation study

Bai, Shuxiong; Guo, Tianruo; Tsai, David; Morley, John W.; Suaning, Gregg J.; Lovell, Nigel H.; Dokos, Socrates

doi:10.1109/ner.2015.7146714

Cited by 3 publications

(4 citation statements)

References 11 publications

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“…To extend these points of stratification into an extensive population of heterogenous dendritic fields, we utilized the modified minimum spanning tree algorithm of Bai et al (2015) with ten carrier points, a 0.5 balance factor, and a maximum segment length of 1.5 µm. It was empirically determined that these metrics produced small dendritic clusters with a single primary arbor, analogous to observations of mRGCs in the central retina (Kolb and Dekorver 1991, Dacey and Petersen 1992, Dacey 1993, Masri et al 2019).…”

Section: Synthesizing a Mosaic Of Dendritic Fieldsmentioning

confidence: 99%

Improving the spatial resolution of artificial vision using midget retinal ganglion cell populations modeled at the human fovea

Italiano¹,

Guo²,

Lovell³

et al. 2022

J. Neural Eng.

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Objective. Retinal prostheses seek to create artificial vision by stimulating surviving retinal neurons of patients with profound vision impairment. Notwithstanding tremendous research efforts, the performance of all implants tested to date has remained rudimentary, incapable of overcoming the threshold for legal blindness. To maximize the perceptual efficacy of retinal prostheses, a device must be capable of controlling retinal neurons with greater spatiotemporal precision. Most studies of retinal stimulation were derived from either non-primate species or the peripheral primate retina. We investigated if artificial stimulation could leverage the high spatial resolution afforded by the neural substrates at the primate fovea and surrounding regions to achieve improved percept qualities. Approach. We began by developing a new computational model capable of generating anatomically accurate retinal ganglion cell (RGC) populations within the human central retina. Next, multiple RGC populations across the central retina were stimulated in-silico to compare clinical and recently proposed neurostimulation configurations based on their ability to improve perceptual efficacy and reduce activation thresholds. Main results. Our model uniquely upholds eccentricity-dependent characteristics such as RGC density and dendritic field diameter, whilst incorporating anatomically accurate features such as axon projection and three-dimensional (3D) RGC layering, features often forgone in favor of reduced computational complexity. Following epiretinal stimulation, the RGCs in our model produced response patterns in shapes akin to the complex and non-trivial percepts reported in clinical trials. Our results also demonstrated that even within the neuron-dense central retina, epiretinal stimulation using a multi-return hexapolar electrode arrangement could reliably achieve spatially focused RGC activation and could achieve single-cell excitation in 56% of all tested locations. Significance. This study establishes an anatomically accurate 3D model of RGC populations within the human central retina and demonstrates the potential for an epiretinal hexapolar configuration to achieve consistent, spatially confined retinal responses, even within the unique and neuron-dense foveal region. Our results and model promote the prospect and optimization of higher spatial resolution in future epiretinal implants.

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Section: Synthesizing a Mosaic Of Dendritic Fieldsmentioning

confidence: 99%

Improving the spatial resolution of artificial vision using midget retinal ganglion cell populations modeled at the human fovea

Italiano¹,

Guo²,

Lovell³

et al. 2022

J. Neural Eng.

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“…ON and OFF RGC clusters were implemented using the computational software NEURON [11]. Techniques used to model individual RGCs have been described in detail previously [12][13][14].…”

Section: Computational Model Of On and Off Rgc Clustersmentioning

confidence: 99%

“…Firstly, the morphological structures of different RGCs were simulated by a customized neural morphology generator [13,15] (see Fig. 1A).…”

Section: Computational Model Of On and Off Rgc Clustersmentioning

confidence: 99%

Insights from Computational Modelling: Selective Stimulation of Retinal Ganglion Cells

Guo

Tsai

Bai

et al. 2020

Brain and Human Body Modeling 2020

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Improvements to the efficacy of retinal neuroprostheses can be achieved by developing more sophisticated neural stimulation strategies to enable selective or differential activation of specific retinal ganglion cells (RGCs). Recent retinal studies have demonstrated the ability to differentially recruit ON and OFF RGCs – the two major information pathways of the retina – using high-frequency electrical stimulation (HFS). However, there remain many unknowns, since this is a relatively unexplored field. For example, can we achieve ON/OFF selectivity over a wide range of stimulus frequencies and amplitudes? Furthermore, existing demonstrations of HFS efficacy in retinal prostheses have been based on epiretinal placement of electrodes. Other clinically popular techniques include subretinal or suprachoroidal placement, where electrodes are located at the photoreceptor layer or in the suprachoroidal space, respectively, and these locations are quite distant from the RGC layer. Would HFS-based differential activation work from these locations? In this chapter, we conducted in silico investigations to explore the generalizability of HFS to differentially active ON and OFF RGCs. Computational models are particularly well suited for these investigations. The electric field can be accurately described by mathematical formulations, and simulated neurons can be “probed” at resolutions well beyond those achievable by today’s state-of-the-art experimental techniques.

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“…ON and OFF RGC clusters were implemented using the NEURON computational software [10]. The techniques we used for modelling individual RGCs have been described in detail previously [11,12]. We employed a custom neural morphology generator, adapted from an existing neuronal morphology generation approach [12,13], to simulate the different dendritic morphological structures of ON and OFF RGCs (see Fig.…”

Section: Computational Model Of On and Off Rgc Clusters Amentioning

confidence: 99%

Computational Models And Tools For Developing Sophisticated Stimulation Strategies For Retinal Neuroprostheses

Guo

Tsai

Muralidharan

et al. 2018

2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Improvements to the efficacy of retinal neuroprostheses can be achieved by developing more sophisticated neural stimulation strategies to enable selective or preferential activation of specific retinal ganglion cells (RGCs). Computational models are particularly well suited for these investigations. The electric field can be accurately described by mathematical formalisms, and the population-based neural responses to the electrical stimulation can be investigated at resolutions well beyond those achievable by current state-of-the-art biological techniques.In this study, we used a biophysically-and morphologically-detailed RGC model to explore the ability of high frequency electrical stimulation (HFS) to preferentially activate ON and OFF RGC subtypes, the two major information pathways of the retina. The performance of a wide range of electrical stimulation amplitudes (0 -100 µA) and frequencies (1 -10 kHz) on functionally-distinct RGC responses were evaluated. We found that ON RGCs could be preferentially activated at relatively higher stimulation amplitudes (> 50 µA) and frequencies (> 2 kHz) while OFF RGCs were activated by lower stimulation amplitudes (10 to 50 µA) across all tested frequencies. These stimuli also show great promise in eliciting RGC responses that parallel RGC encoding: one RGC type exhibited an increase in spiking activity during electrical stimulation whilst another exhibited decreased spiking activity, given the same stimulation parameters.

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Influence of retinal ganglion cell morphology on neuronal response properties - a simulation study

Cited by 3 publications

References 11 publications

Improving the spatial resolution of artificial vision using midget retinal ganglion cell populations modeled at the human fovea

Improving the spatial resolution of artificial vision using midget retinal ganglion cell populations modeled at the human fovea

Insights from Computational Modelling: Selective Stimulation of Retinal Ganglion Cells

Computational Models And Tools For Developing Sophisticated Stimulation Strategies For Retinal Neuroprostheses

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